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SYNCHRONICITY U R B A N A DA P TAT I O N

OF

E MERGENT T ECHNOLOGIES AND D ESIGN A RCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE

I N D I G E N O U S S PAT I A L A T T R I B U T E S

1

Y UNFU Y I . A BHINAV C HAMPANERI


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SYNCHRONICITY URBAN ADAPTATION

OF INDIGENOUS

SPATIAL ATTRIBUTES

MASTER OF ARCHITECTURE, EMERGENT TECHNOLOGIES AND DESIGN ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE

Course Director : Michael Weinstock Course Director : George Jeronimidis

MArch Candidates: Yunfu Yi Abhinav Champaneri

Studio Master : Evan Greenberg Studio Tutor :

Mehran Gharleghi

Studio Tutor :

Wolf Mangelsdorf 3


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Architectural Association School of Architecture Graduate School Programme

Programme:

Emergent Technologies and Design

Term:

04 (2012 -2014)

Student Name:

Yunfu Yi, Abhinav Champaneri

Submission Title:

SynchroniCity

Course Tutors:

Michael Weinstock, George Jeronimidis

Course Title:

Emergent Technologies and Design, Master of Architecture

Submission Date:

14.02.2014

Declaration:

‘‘ We certify that this piece of work is entirely our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged. ’’

Signature:

Yunfu Yi

Abhinav Champaneri

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ACKNOWLEDGEMENT This thesis would have not been possible without sincere guidance and support from several individuals who contributed their expertise and experience to the project. First and foremost, we would like to express our utmost gratitude to our course directors; Michael Weinstock and George Jeronimidis, their commitment and support enabled us to constantly explore new dimensions in design and allowed us to take maximum advantage of the course. Their experience and assistance greatly helped us further our knowledge, skill and understanding in the ďŹ eld of architecture. We take the pleasure to thank our Studio Masters, Evan Greenberg and our studio tutors, Mehran Gharleghi for their unfailing and consistent support, encouragement, advice and teaching throughout our Emtech course. We express our thanks to Wolf Mangelsdorf for sharing valuable insights to our study that enhanced our thesis. We also appreciate and thank our teammates Tejas Sidnal and Yuchen Wang for their contributions during the MSc. phase of the project which formed the groundwork for our current work. We are also grateful to the jury whose valuable comments helped us to evaluate our project critically. Finally, we would like to thank our families for their constant support and encouragement as well as our EmTech classmates for their cheerful company, memorable experiences and moral support during this phase. 7 5


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ABSTRACT Synchronicity addresses the phenomenon of decline in spatial

open space distribution are referenced from the local settings to

attributes in developing countries due to rapid urbanisation. The

be embedded into the new urban morphology. The system would

ambition is to establish a design system that reinterprets spatial

operate in two levels; in the first stage optimised geometries would be

logics and spatial identity associated with socio-cultural life into

generated and catalogued. The catalogue would constitute geometries

quantifiable parameters, and incorporates them with high density

differentiated in terms of spatial and density attributes to suit different

urban development. This would not only add social relevance but also

urban requirements. These catalogue geometries would be applied in

create location specific architectural identity.

actual urban scenarios with site constraints of boundary conditions, density and network patterns and programmatic requirements. The

The process undertakes analysis of selected indigenous settings for

ability of these geometries to mitigate and modify to suit differentiated

their spatial attributes. The open spaces and built morphologies in

site conditions would be tested in this stage.

these settings are studied as they are the embodiment of the local socio-cultural and environmental aspects. Innovative sampling

The system elaborately intertwines bottom–up and top-down

methods are adopted to extract and convert spatial and organisational

approaches for application within urban scenario. This takes into

aspects into numeric parameters and geometric logics, which would

consideration the emergent aspects occurring in the bottom-up

be used in the computational design process.

approach and the top-down imposed architectural objectives for the site. The intelligence of the system would be based on taking into

The attempt focuses on the development of an evolutionary urban

consideration the local settings, the urban demographic demands, the

design model, which accommodates demographic pressures

capacity/limitations of the catalogue geometries and the architectural

of density while retaining its spatial identity based on former

ambitions. The attempt would be to generate, with the same system,

settlements. Organizational aspects like spatial hierarchy and

differentiated result for varying urban scenarios. 9 7


CONTENTS

1 2 3

Acknowledgement

5

Abstract

7

Introduction

10-17

Domain

18-35

2.1 Spontaneous Adaptation of Indigenous Settlements

20

2.2 Precedents- Designed Indigenous Projects in Urbanized Context

22

2.3 Precedents- High-rise Residential Typology

28

2.4 Precedents- Urban Growth Patterns in Beijing and Mumbai

30

2.5 Precedents- Evolutionary Urban Morphologies

32

2.6 Conclusion

34

2.7 Architectural Ambition

35

Methods

36-49

3.1 Proposed Design Methodology

38

3.2 Associative Techniques

40

Design Aspects

4 5 6 10

Parameters 3.3 Generative Techniques

Analysis Of Existing Vernacular Settlements

41 43 46

50-65

4.1 Indigenous Settlements : Overview and Design Logic

54

4.2 Indigenous Settlements : Parametric Study

67

4.3 Conclusion

81

Block Level Catalogue Generation

66-81

5.1 Design Inputs

84

5.2 Experiments

86

5.3 Block Generation

89

5.4 Block Level Catalogue

96

5.5 Conclusion

99

Cluster Level Catalogue Generation

82-101

6.1 Aggregation

104

6.2 Design Inputs

110

6.3 Generation Process

114

6.4 Cluster Level Catalogue

119

6.5 Conclusion

121


7

Neighbourhood Level Design Development

124-211

7.1 Initial Experiments 7.1.1 Experiment I - Density Variation and Emergent Spaces

138

7.1.2 Experiment II - Cluster Adjacencies in Aggregation

142

7.1.3 Experiment III - Cluster Modification for Boundary Adaptation

144

7.2 Site Research 7.2.1 Beijing - Site Overview and Analysis

150

7.2.2 Mumbai - Site Overview and Analysis

154

7.2.3 Conclusions and Site Specific Ambitions

158

7.3 Aggregation 7.3.1 Experiment IV : Cluster Organisation

162

7.3.2 Mumbai Aggregation

164

7.3.3 Beijing Aggregation

170

7.4 Network Generation 7.4.1 Network Generation Principal Criteria

178

7.4.2 Network Generation : Mumbai

180

7.4.3 Network Generation : Beijing

184

7.5 Block Differentiation 7.5.1 Block Differentiation Criteria

190

7.5.2 Block Differentiation : Mumbai

192

7.5.3 Block Differentiation : Beijing

196

7.6 Programmatic Variation

8

7.6.1 Local Level Retail Units

202

7.6.2 Corporate Large Scale Establishments

208

Critical & Comparative Analysis

212-237

8.1 Conclusions

213

8.2 System Evaluation and Future Prospects

233

Appendix

238-271

Bibliography

272-275

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1

INTRODUCTION Indigenous Settlements

14

Urban Sprawl Phenomenon

15

Urbanisation and Population Growth

16

Thesis Overview

17

Introduction

11


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Fig 1.1 : The Besieged Settlements Around the City

The Complexity Cities happen to be problems in organised complexity. They present situations in which several dozen quantities are all varying simultaneously and subtly interconnected ways. The variables are many but they are not helter skelter; they are “interrelated into an [1] organic whole.” -Jane Jacobs, the Death and Life of Great American Cities

Fig 1.2 Image showing the vernacular settlement

threatened by the urban infrastructure

Cities are complex systems which according to Herbert A. Simon (The Architecture of Complexity, 1962) is made up of large number [2]

of parts that interact in a non- simple, non-linear manner . Cities exist in layers of information which constitute the dense system of infrastructural networks, movement patterns, environmental responses, socio-cultural aspects, human behaviour and many more. The interrelations between these are expressed by the built and open spaces that form the architecture of the place. The success of architecture therefore depends on the right balance and integration that these elements are able to establish within themselves. The current trend of urbanisation puts a lot of pressure on the balance of these systems. To accommodate the growing density certain elements within the city system get undermined or sacrificed. In most cases the neglected elements are those that have the least economic impact. Open spaces in this regard suffer the most in terms of both quality and quantity, as the short term economic gain to encroach these spaces is the highest. However, the socio-economic and socio-cultural impact in the long run is severe where the liveability of the city goes down drastically. This trend is very prominent in the developing countries that face rapid urbanisation. A new urban design model of development is required which better Fig 1.3: Traditional Shikumen Buildings Besieged by Urban Architectures in Shanghai

prioritises the delicate balance between the various elements and

[1]. Jacobs, J. (1961). The Death and Life of Great American Cities. In Batty, M. Introduction p.01, Cities and Complexities-Understanding Cities with Cellular Automata, Agent Based Models and Fractals, The MIT Press, Cambridge, Massachusetts [2]. Simon, H.A. (1962). The Architecture of Complexity, In Batty, M. Cities and Complexities-Understanding Cities with Cellular Automata, Agent Based Models and Fractals, The MIT Press, Cambridge, Massachusetts

Introduction

13


Indigenous Settlements

Mumbai, India Fig 1.4

Beijing, China

layers of a city. A significant aspect is therefore integrating quality

environmental responses. In most cases they are also low energy

of space with the ever increasing demographic demands. Apart

responses which are based on situational availability of material

from environmental factors, socio-cultural relevance should also

and existing geography. All these aspects give the setting a distinct

be included in defining quality of space.

architectural identity, missing from most modern day development.

The thesis approaches the subject keeping this as the primary

It is on this identity that the three essential aspects are based: the

focus. In this regard spatial attributes of indigenous settings are

adaptation and evolution of built form to efficiently perform in the

studied where socio- cultural aspects have architectural adaptation

existing environmental conditions, the accommodation of socio -

or vice- versa. These would serve as references for developing an

cultural aspects with the built form and lastly the integration and

urban fabric with a new logic.

relation of built and open spaces in terms of organisation within

Indigenous Settlements

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SynchroniCity

the fabric. Successful examples based on these aspects would be studied and analysed parametrically to derive ideal built–open

Indigenous settlements of a place hold unique features and

affiliations and compared with contemporary models emerging

characteristics (Fig 1.4). They are unique because they are

in the urban scenarios in major cities of developing countries like

the embodiment of the local socio-cultural aspects and local

India and China.


Urban Sprawl Phenomenon

Mumbai, India Fig 1.5

Beijing, China

Urban Sprawls Urban sprawls of today hold a completely contrary situation (Fig 1.5).

creating spaces that are more compact and efficient in terms of

The pressure to achieve higher densities results in better and more

economic gains but not in terms of quality. The spaces that suffer

efficient infrastructure. However, its architectural identity does

most are the informal open spaces that would potentially represent

not present attributes that correspond to its location. It is because

the socio-cultural aspect and improve the liveability of the place.

of this that the urban sprawl takes a homogeneous architectural

The lack of these spaces creates an abrupt transition from public

character, where the built configuration in any new development

to private and leaves no scope for informal semi-public spaces. To

looks standardised and similar. The urban agglomeration boasts of

understand this trend we look at sites where indigenous character

economic prowess and technological advancement but is also the

with rich spatial attributes is under pressure of getting converted

centre of high energy consumption, pollution and traffic problems.

into homogeneous urban sprawl. A number of architects address this problem at an architectural level as well as at an urban level

Even so, urbanisation is an inevitable phenomenon because of the

which we discuss in Chapter 2: Domain. However, a comprehensive

opportunities that it provides in terms of occupation and lifestyle.

approach that takes into consideration not only the quantity of

Because of this, cities would always be under demographic pressure

built and un-built spaces but also their interrelation, socio-cultural

to create more usable space. The demand for this space results in

connotation and quality aspects are still missing. Introduction

15


Urbanisation and Population Growth

1960

1980

2011

2025

Fig 1.6 Percentage urban / urban agglomerations by size class [4]

Percentage Urbanized 0-25% 25-50% 50-75% 75-100% City Population 1-5 million 5-10 million 10 million or more

Global Aspects According to World Health Organisation-

a significant shift in this matter and in most cases it is the rural

“For the first time ever, the majority of the world’s population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40% of the global population lived in a city, but as of 2010, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. Currently, around half of all urban dwellers live in cities with between 100 000 - 500 000 people, and fewer than 10% of urban dwellers live in megacities.’’ [3]

villages that are getting converted into urban cores. This shift is evident from the statistics shown in Fig 1.3.1 where there is a drastic decline in the rural areas and a proportional increase in urban settlements. Urbanisation is closely linked to modernisation, industrialisation, and the sociological process of rationalisation. This rationalisation results in replacement of indigenous socio-cultural aspects or values which are motivators for behaviour in society with calculated ones. This is most commonly seen in the modern day

[3] World Health Organisation, Urban Population Growth (2009) Available from: [http://www.who. int/gho/urban_health/situation_trends/urban_ population_growth_text/en/] [Accessed : 10 June 2013] [4] United Nations, Department of Economic and Social Affairs, World Urbanisation Prospects (2011) Available from: [http://esa.un.org/unup/Maps/maps_ urban_2011.htm] [Accessed : 10 June 2013]

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SynchroniCity

The global urban phenomenon shows emergence of significant

conversion of indigenous settings into urban systems where the

number of new cities with population greater than 1 million. The

rate of this shift into urbanisation brings into demand a lot more

statistics show that a large part of this new urbanisation would be

usable floor space. This pressure of development results in creating

concentrated in developing countries like China and India where

urban cores that are economically beneficial, but most lack the

the economic growth has ushered a wave of urban expansion. It is

architectural cohesion with the local urban fabric. Most of the new

predicted that a total of 81 new cities would emerge by 2025 in China

urban developments are created with the singular inclination to

and India, apart from this 289 cities in China and India would hold

create higher usable floor space and to create a global identity, with

a population of more than 1 million. As a result both the countries

rare consideration to space quality. Even if this is a consideration,

would be investing significant resources into city building. In

they do not hold any aspects of local / indigenous settlement into

these countries up until 1980’s less than 25% of the population was

context, the socio-cultural reference for architecture goes down

urbanised and by 2025 the urbanised population would reach 75%.

and the economical factors take over. Therefore the development

Both the countries had an agrarian economy where most of the

lacks local identity and is devoid of architectural aspects that can

population resided in the rural regions. Urbanisation has driven

make it unique from other developments around the world.


Thesis Overview The transition of a settlement from rural to urban

The attempt would be to convert these socio-cultural

logic uses the extracted parameters to define spaces in

in most cases is drastic and the new development is

aspects of morphology into parametric data to inform

high density scenarios.

alienated from all aspects of the former settlement. The

the design level.

development would relate more to the local settings if

The derived architectural models will be tested and

certain rich aspects of indigenous settlement can be

The exploration is divided into three parts: The first

compared with contemporary models existing around

applied in the new urban context, to add a socio-cultural

part of the investigation shall be based around studying

the world. The third part would look at the design

reference which in most new developments is missing.

indigenous settings in China and India with open spaces

at an urban level where the architectural shall be

The investigation therefore is into an alternative urban

as the focus. The criteria to select these settings would

integrated into the urban fabric keeping both the socio-

design model that looks to generate a high density

be based on sites that possess spatial configurations that

cultural aspects of space and the urban demands in

setting which derives its spatial attributes keeping the

are unique to the indigenous setting, have the ability to

consideration. The parameters and organisation of open

local and indigenous settlements in context. The overall

encourage social interactions and are important for their

spaces in indigenous settings would be used to inform

ambition would be to test and analyse indigenous spatial

cultural settings. The spaces would be studied in terms

space generation in the urban context. The design at

logics and define methods to successfully integrate

of both experiential and environmental aspects that

this stage will be further informed by other aspects

these with the modern day urban fabric which is under

contribute to their successful working. The second part

that augment urban systems in terms of connectivity,

constant demographic pressure. The emphasis of the

deals with using these spatial logics in form of extracted

density management, programmatic distribution and

intervention would be the socio-cultural aspects that

parameters. System logic for design would be developed

environmental compatibility to produce morphologies

convert into architectural morphology or vice versa.

with the help of experiments and exercises. This system

that have high spatial quality and improve liveability. Introduction

17


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2

DOMAIN This chapter focusses on examples that hold semblance to the chosen subject; it is based on different cases which depict the current trend of development in the urban scenario: (a) the phenomena of spontaneous alterations to vernacular settlements during population densification and the subsequent change in architectural character because of this. (b) the current efforts in design practice to reproduce certain notable aspects of vernacular or indigenous settings in an urban scenario and (c) ‘state of the art’ examples where buildings are designed to accommodate green spaces within high rise buildings to function as social spaces. The chapter also analyses the precedents in evolutionary computational processes used for generating urban morphologies. The aim is to critically evaluate the pros/ cons of all the practices stated above and to propose an approach that synthesizes their advantages in both environmental and socio-cultural aspects. 2.1 Spontaneous Adaptation of Indigenous Settlements

20

2.2 Precedents- Designed Indigenous Projects in Urbanized Context

22

2.3 Precedents- High-rise Residential Typology

28

2.4 Precedents- Urban Growth Patterns in Beijing and Mumbai

30

2.5 Precedents- Evolutionary Urban Morphologies

32

2.6 Conclusion

34

2.7 Architectural Ambition

35 Domain

19


2.1 Spontaneous Adaptation of Indigenous Settlements

16.

Private Open Space 31% Fig 2.1.1 Private Open Space in 1948 (Ranwar Village, Mumbai)

Private Open Space 15% Fig 2.1.2 Private Open Space in 2010 (Ranwar Village, Mumbai)

In the urbanisation process, there is always a massive migration of

courtyard house located in Dashilar Area of Beijing where the semi

rural population into cities. The migration causes infrastructural

public space is reduced from 38% to 14% (Fig 2.1.5). This has been

and demographic pressure on the city. This trend can be observed

a noticeable trend in most of the indigenous settings as these

in Mumbai since 1950s and in Beijing since mid 1970s. The

places originally had high percentage of open space dedicated to

population tripled in several decades and up to now these two

the household.

urban agglomerations accommodate over 20 million dwellers

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SynchroniCity

each. The increasing demand on land resulted in autonomous

As a consequence, the horizontal infill development mode

densification; the fenced plot in Mumbai and courtyard buildings

succeeded in responding to the demand of density at the cost of

in Beijing are no longer owned by single household, but instead

deteriorating environmental as well as socio-cultural qualities of

turned into multi-family compound. Extensions are built in the

the spaces as these autonomous adaptations were often reported to

open spaces in order to maximize floor area. This trend is indicated

have poor solar admittance or over-proximity between houses. This

in Ranwar Village Mumbai where the semi-public has reduced

not only affects the spatial attributes of the place but also liveability

from 31% (Fig 2.1.1) of plot area to 15% (Fig 2.1.2) and also in a typical

of the city as a whole.


Fig 2.1.3 Courtyard for semi public activities

Fig 2.1.4 Encroached courtyard to accommodate dwellings

1975

1980

1990

21

19.

Private Open Space 38%

Private Open Space 29%

Private Open Space 14%

Fig 2.1.5 Typical chronological modiďŹ cation to single storey large courtyard unit in Beijing Domain

21


2.2 Precedents- Designed Indigenous Projects In Urbanized Context Belapur Housing Project

Residential Unit

Private

Covered Semi-public Area

Open Semi Public Area

Shared Courtyard Neighbourhood level Open space Playground level Open space

Public Fig 2.2.1 View of typical courtyard in a cluster

Housing Project New Mumbai, India, 1983-1986

The Belapur Housing Project is derived from the indigenous

around a larger public space. Spatial qualities such as shading

dwelling configuration (Fig 2.2.1) with one overriding principle that

property were taken into elaborate consideration during the design

each unit has its own individual site to allow for expansion. The

process.

design was made by Architect Charles Correa to embody the basic attributes of a typical traditional Indian dwelling. The scheme

The organisation of clusters forms well-bedded spatial hierarchy

caters to a wide range of income groups from the lowest up to the

(Fig 2.2.4) with remarkable fractal patterns (Fig 2.2.5). Although

affluent.

the project embodies value in reinterpreting vernacular spatial configuration and some of the spatial qualities, the adaptation into

22

SynchroniCity

The main concept was based on the abstract idea of hierarchical

high density context seems not to be the aim of the project. The

organisation of clusters and the open spaces: 6-8 fenced residential

inherited low density model is not be able to accommodate the

units aggregate around an open space and form a cluster with

increasing demographic pressure. Therefore the project is more of

central courtyard (Fig 2.2.3). These clusters are further aggregated

a replication of the existing rural typology


6x 8x Residential Unit

Living Area

Type A

Type B

Type C

Type D

Toilet

Fig 2.2.2

21. View

of typical cluster with a courtyard Fig 2.2.3

Hierarchy of open spaces within the site Fig 2.2.4

22.

Hierarchy of open spaces within the site Fig 2.2.5 Domain

23


2.2 Precedents- Designed Indigenous Projects In Urbanized Context Ju’er Hutong Courtyard Housing Project

Fig 2.2.12 View of typical courtyard in a cluster Beijing, China, 1988

Ju’er Hutong Courtyard Housing Project Ju’er Hutong Courtyard Housing Project (World Habitat Award

single-storey courtyard typology into multi-level means that the

winner 1993) is generally regarded by far the most thorough attempt

introverted courtyard which used to be owned by 4 households

to reinterpret essence of vernacular dwelling via a quadrangle

turned into common-owned space that is shared up to 18 families,

courtyard-like typology (Fig 2.2.12). The scheme attempts to

This results in over-surveillance of the activities in courtyard as 18

accommodate the increasing population density in urbanized

households watch the activities.

Beijing via fixed two and three storey height new courtyard housing. The two and three storey courtyard house is proved to be an optimal low-rise typology that achieves FAR 1.28 which is same as four storey slab (stripe) building or six storey tower (point) building. The construction of 46 new homes also increased the average per capita living space from 5.3 m2 to 12.4 m2. Despite the success in accommodating the extra density, extruding 24

SynchroniCity


Fig 2.2.13 View of typical courtyard in a cluster

Fig 2.2.14 View of typical courtyard in a cluster

Fig 2.2.15 Isometric view showing a typical block typology

Domain

25


2.2 Precedents- Designed Indigenous Projects In Urbanized Context Asian Games Village

Fig 2.2.7 Ariel view of Asian Games Village

Asian Games Village New Delhi, India, 1982

Asian Games Village was built in 1982 to house athletes of the

via low rise residence typology. Despite the success in architecture

games. 500 housing units were designed as group housing on 35

typology scale, the project failed to hold a critical viewpoint in

acres of land. The aim was to create an urban pattern of low rise

neighbourhood scale. The courtyards in each building cluster are

high density, with high level of interconnected shaded passages

directly linked to the motorway, creating typical modern traďŹƒc-

(Fig 2.2.10). Courtyard morphology is formed by mirroring the

oriented street rather than traditional mixed-use streets. While

merging blocks along orthogonal axes, responding to climatic and

the latter is proved to be an indispensable socio-cultural element

social needs. The streets are consciously broken up into visually

that eliminate rapid transit traďŹƒc and encourage social activities.

comprehensible units, often with gateways, bringing about pauses,

The shading analysis (Fig 2.2.11) show that through out the day, the

point of rest and changing vistas.

internal courtyard provides a very good shading from the sun.

The project possesses merits in providing shading performance by abundant concave-convex transformation in shape and by courtyard morphology, and moreover, in creating high FAR value 26

SynchroniCity


Individual Building Fig 2.2.8

Block forming shaded court in the centre

Block arrangement forming various sizes of shaded courts in the centre

9 am

28.

Top view showing streets intersections and internal courts Fig 2.2.9 Semi-public space Private space

12 pm

6 pm Fig 2.2.11 Shading analysis on residential blocks Fig 2.2.10 View of courtyard in residential buildings Domain

27


2.3 Precedents- High-rise Residential Typology

Fig 2.3.1 Design proposal by OMA, Mahanakhon in Thailand Status: Type: Height: Floor area: Est. completion:

Under construction Residential, retail, hotel 314m 150,000 m2 2015

Under - Construction High-rises with Multi Level Open Spaces A number of architectural practises around the world are

like spaces in close proximity to their residence, a luxury not

attempting to integrate green spaces at multiple levels in new high

common in the high density, high rise scenario.

rise buildings. The proposals like Mahanakhon in Thailand by OMA (Fig 2.3.1), The Cloud in Seoul (Fig 2.3.2) and Sky village in

The intent for such development seems justified considering

Rødovre by MVRDV (Fig 2.3.3) are some examples of growing trend

the current trend of development, where very limited amount

arising all over the world where significance of providing semi –

of quality open space is found in cities. However the method of

public open spaces has been recognised.

integrating these spaces is questionable, most of the green spaces are not designed for semi-public activities, they are mere terraces

Fig 2.3.2 Design proposal by OMA, Mahanakhon in Thailand 28

SynchroniCity

The proposals incorporate these spaces at multiple levels as pockets

or balconies converted into sky gardens. The spaces are extroverted

of social spaces. These levels of interactive spaces have disappeared

which lack visual privacy and therefore fail to become part of the

from most modern urban scenarios, realizing the significance of

household. Design of these spaces lack environmental and socio

these spaces has led architects to design such options. These serve

considerations factors that differentiate them from other public

as attractions for the buyers, as such spaces provide green, garden

spaces.


Fig 2.3.3 Design proposal by MVRDV, Cloud in Seoul

Fig 2.3.4 Design proposal by MVRDV, Cloud in Seoul

Fig 2.3.5 Design proposal by MVRDV, Sky Village in Rødovre

Fig 2.3.6 Design proposal by MVRDV, Sky Village in Rødovre

Domain

29


2.4 Precedents- Urban Growth Patterns in Beijing and Mumbai

Fig 2.4.1 Residential Development in Beijing

The exponential urban growth in both the cities has given rise

In terms of urban development there is a similar trend. The city

to large scale residential development to sustain the population

developmental plans are manipulated to appease economic

growth. In a haste to provide dwelling western design models are

aspects. The buildings are built as segregated entities independent

being applied without considering the local settings in terms of

of the impact on surroundings. This has resulted in a sprawl of

cultural aspects and environmental adaptability. As a result high

multi-storey apartments with no form of spatial or environmental

rise typologies dominate the construction scene of both these

considerations. This growing number of multi-storey buildings

locations. These buildings are not based on the local context and

creates inferior and congested urban environments that don’t

can be seen in any other part of the world.

relate to the human scale and thereby reduce liveability of these cities.

Economic beneďŹ ts dominate other incentives and therefore spatial aspects, environmental aspects and socio-cultural aspects remain ignored. Further, there are no researches or studies performed to analyse the impact of these typologies. 30

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Fig 2.4.2 Typical Blocks for Slum Rehabilitation Projects in Mumbai

Fig 2.4.3 Urban Development Patterns in Mumbai

Fig 2.4.4 Construction in Beijing

Fig 2.4.5 Construction in Beijing

Domain

31


2.5 Precedents - Evolutionary Urban Morphologies Comparison on Generation Logic

This chapter analyses various academic research projects that adopt evolutionary design methods to create context based urban systems. The comparative study illustrates the observation and differences between 3 precedents in evolutionary urban morphology. They are:

Associative Design

Generation Strategy

[5]

Metabolism and Culture

Bottom-Up

Bottom-Up/ Top-Down

[6]

Autonomous Infrastructures [7]

Bottom-Up/ Top-Down Fig 2.5.1 Associative Design: Berlage Institute

Aggregation Process

Recursive

Linear

Linear

Plot Footprint Shape

Quadrangles/ Pentagons

Squares

Quadrangles

Geometry Deformation

Distorted

Non-distorted

Distorted

Emergent Attributes

T/X Junction

Public Spaces

X Junction

Geometry based Aggregation Fig 2.5.2 Metabolism and Culture: AA EmTech

The aggregation logics are developed according to the local geometries which are embedded with optimum values of certain parameters. In Associative Design, the courtyard units deformation results in local geometries with quadrangle/ pentagon footprint. And since the inner angles of the polygons vary from each other, the junction where adjacent blocks meet could be T/X shaped with various intersecting angles. Metabolism and Culture, on the other hand, developed a deformation- free aggregation logic that is based on the packing of approximate bounding geometries. This novel strategy guarantees the local parameters from being affected by the geometry distortion. Nevertheless, the influences of neighbour geometry condition was left unaddressed. It is also noticeable that top-down interference was introduced during programmatic distribution. One more example we analysed is Autonomous Infrastructures. It is also featured by an elaborated enshrinement of top-down and bottom-up approaches. The site footprint was sub-divided into small quadrangle patches. And they are categorised according to a square-diamond distortion level in which way the qualities of geometries could be guaranteed by minimising the total distortion level. 32

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Fig 2.5.3 Autonomous Infrastructures: AA EmTech


Process Analysis

Bottom Up

Block Generation

Distortion with 90° Angles

Neighbourhood

Geometry Differentiation

Programmatic Distribution

Re-organisation Bottom Up

Fig 2.5.4 Recursive Design Approach

Block Generation

Distortion with 90° Angles

Neighbourhood

Geometry Programmatic Distribution Top Down Differentiation

Programmatic Uses Re-organisation Bottom Up

Distribution

Cluster Footprint Distortion of Regular Grid

Block Generation

Cluster Catalogue

Aggregation with Emergent Spaces

Top Down

Neighbourhood Programmatic Uses

Bottom Up Bottom Up

Block Generation

Distribution

Distortion with Neighbourhood Cluster Footprint 90° Angles

Top Down Geometry Programmatic Distribution Differentiation

Distortion of Regular Grid

Neighbourhood

Aggregation with Block Generation Cluster Catalogue Bottom Up Re-organisation Emergent Spaces

Block Catalogue

Tessellation with Moderate Distortion

Neighbourhood Sub-division

Cluster Footprint Top Down Top Down

Neighbourhood Programmatic Uses

Fig 2.5.5 Emergent Public Spaces Bottom Up

Block Catalogue

Distribution Sub-division

Bottom Up

Tessellation with Moderate Distortion

Cluster Footprint

Cluster Footprint

Distortion of Regular Grid

Block Generation

Cluster Catalogue

Aggregation with Emergent Spaces

Neighbourhood

Top Down

Neighbourhood Bottom Up

Block Catalogue

Fig 2.5.6 Distortion levels are showed in different colours

Sub-division

Tessellation with Moderate Distortion

Cluster Footprint

[6] Wang, Z., WANG,L., Associative Design (2009) Available from: [http://http://www.dysturb.net/2007/ associative-design-berlage/], Berlage-Institute [Accessed on : 25 October 2013] [7] Nasseri,F., Mousavi, Y., Metabolism and Culture (2012), MArch Thesis, Emergent Technologies and Design, AA School of Architecture. [8] Chehab,A., Makkouk, M., Autonomous Infrastructures (2013), MArch Thesis, Emergent Technologies and Design, AA School of Architecture.

Domain

33


2.6 Conclusion The study and evaluation of the developmental trends and methods

to type of connectivity to such spaces is vital as different kind of open

form the basis of our design intervention. It provides understanding

spaces would require differential levels of connectivity, an aspect

of the drawbacks of the current trends and provides clues to resolve

not addressed in most modern day design proposals. Therefore the

and define a comprehensive approach. All cases of development seen

location of open spaces in the network of movement system plays an

in the first four chapters have one or the other, singular dominant

important role as it defines transitioning and travel experience. In

aspect which they try to achieve and therefore the design models seem

most proposals there is a hard boundary between the private and the

incomplete or inadequate. A successful design intervention would

public, marked by perimeter boundaries. This limits the interaction

be one that is able to balance and integrate density, functionality

between the built forms and the connecting routes which limits the

and space quality. Referencing space definition to social structures

prospects to form informal social spaces. This consideration would

existing in the place not only adds relevance to the design but also

significantly improve the social aspects of urban spaces.

justifies functionality. The analysis of precedents in evolutionary urban design shows the

34

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Significant effort is put into city and architectural design to

potential of addressing multiple design aspects as well as looking

accommodate high quality urban spaces. A conscious effort to

into the complexity. The design would be in context of modern

innovate in terms to locating green spaces is made so as to improve

urban situation where densification is persistent and inevitable. The

availability and accessibility to these open spaces. Defining quality

design proposal would be a medium rise, high density approach that

not only as aesthetically and environmentally suitable but also

gives high consideration to space quality and references its spatial

socially adaptive and relevant is essential. In this respect significance

logics to successful typologies in indigenous settings.


2.7 Architectural Ambition The architectural ambition gets informed by three core factors:

definition and spatial organisation. Semi-public spaces will be

the requirement of high density city fabrics, the depletion of open

integrated at multiple levels within the built form, to improve their

space ratios in new urban development and lack of high quality,

accessibility and functionality. At the same time, they will act as

accessible and socially relevant open spaces. Therefore, project

thresholds in transition from the public to the private spaces. The

aims at researching and developing a comprehensive design

organisation of all open spaces in the fabric would be on the basis to

methodology that incorporates the above mentioned aspects

create spatial hierarchy and connectivity. The connectivity network

with modern urbanisation. The primary focus will be on open

would be predominantly pedestrian; therefore proximities will be

space quality and their organisation. The project in this regard

an important factor to consider in all design decisions. Another

looks at testing and incorporating successful spatial attributes of

feature of the design would be to create a continuous pedestrian

indigenous settings in high density fabric of cities.

social network at the ground level, the activities of this would vary based on programmatic distribution in the locality.

There are two essential parametric aspects that the project has to consider: density and quality. Density refers to floor area ratio and

The design ambition would be to derive a system that generates

the aim would be to achieve the best possible value with certain

morphologies that account for socio-cultural, environmental and

desired quality. The quality aspects for the project would be

contextual factors. The system must be adaptable to different site

defined by the environmental parameters, experiential factors and

requirements and pressures so as to accommodate specificities

socio-cultural aspects. These aspects will be investigated on the

such as

built – open relations with respect to privacy, connectivity, space

conditions and programmatic aspects.

variable densities, socially relevant spaces, boundary

Domain

35


36

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3

METHODS

This chapter deďŹ nes the modes and methods adopted for various parts of the process: research, analysis, design and evaluation. It describes the logic system that will be tested in the thesis, the process driven urban design will utilise the tools at dierent stages of design development. Details of both digital tools and non-computational approaches that will be implemented in the process are explained, their limitations and the calibration appropriated to overcome the limitations are also discussed. 3.1 Proposed Design Methodology

38

3.2 Associative Techniques

40

Design Aspects

41

Parameters

43

3.3 Generative Techniques

46

Methods

37


3.1 Proposed Design Methodology

Case Studies INDIGENOUS SETTLEMENTS

Analysis

Catalogue

OPEN / SOCIALLY RELEVANT SPACES

Semi-public Spaces Ranwar Village Mumbai

Courtyard in Beijing Frontyard in Mumbai

Extracted Design Logic and Parameters

Public Spaces

Block Level Generation

Hutong in Beijing Chowk in Mumbai Nan Chi Zi Beijing

Process Overview The growing understanding of cities, coupled with the

The first part of the study identifies two sites which are

help us define our design strategy. The first stage of

desire to make them more efficient and effective has

culturally and environmentally different but experience

design will deal with building level implementation of

resulted in adopting Computational City Design as a

constant demographic pressures. In this context we

space logics and optimising aspects such as space quality,

way to design, analyse and simulate urban situations.

will study development in China and India where

environmental efficiency and density. By utilising an

The capacity of the system allows for simultaneous

urbanisation has taken its toll on urban space quality.

evolutionary engine, a catalogue of optimized buildings

processing of large amounts of data and generates more

In these locations, characteristics of ideal, indigenously

will be created. The generated options will then be

realistic and accurate solutions. It also generates a large

present social spaces are identified for integration into

evaluated against contemporary models. Following this,

number of possible morphologies for the evaluation

the urban model. To use the logic of these spaces in the

aggregation logics will be researched to develop clusters

process.

urban scenarios, information is needed in the form of

with different attributes. At this stage the focus will be

computable data. Therefore parameters that describe

on block-block relationship where interrelationships

The thesis proposes a combination of both top-down

such spaces in terms of morphology and organisation are

and organisational patterns at local level will be defined.

and bottom-up approaches which uses spatial logics,

extracted through various analytical tools. Parameters

Like in the case of blocks a higher level catalogue of

environmental responses and socio-cultural aspects as

include extraction of simple demographic data using

clusters will be defined.

parameters to inform the urban system. The combined

computational tools and organisational data to describe

analysis-and-design method will be used for conceiving

situation based aspects of these the spaces within the

These catalogues provide the skeletal features of blocks

high-density neighbourhoods that support these

wider fabric. The next part will focus on exercises and

and clusters. These would be tested by applying them

parameters and also mitigate present urban issues.

experiments with the extracted parameters which will

in neighbourhood level generation. The adaptability of

38

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Generation

Architectural Design

Analysis

Site Studies

DEMOGRAPHIC PRESSURES DENSITY GRADIENTS NETWORK PATTERNS Neighbourhood Level

PROGRAMMATIC ASPECTS

MUMBAI

Cluster Level Generation

MUMBAI SITE

DEMOGRAPHIC PRESSURES DENSITY GRADIENTS NETWORK PATTERNS Neighbourhood Level

PROGRAMMATIC ASPECTS

BEIJING SITE

BEIJING

Architectural Ambitions

Comparative Studies the pre-optimised blocks and clusters would be tested

The process of design extensively engages in comparative

between the derived catalogued morphologies with

in site specific scenarios. The system at this level would

studies at every design level to improve the system and

morphologies of contemporary urban scenarios and

be informed by primarily two aspects: the site context

highlight aspects that need to be considered at the

indigenous settings. This would help us not only to

in terms of network patterns, density gradients and

succeeding level. This comparative method is applied

shape the design ambitions but also provide us with

programmatic aspects; the other being the architectural

even in the case study analysis part to better inform the

insights into the limitations and possibilities within

ambitions, which would add a top-down approach to

system. To inform the design process two contrasting

the system. Again, at the neighbourhood level, to test

the system. Therefore the morphologies to adapt to each

sites are identified Nan Chi Zi in Beijing, China and

the applicability of the system, two contrasting urban

scenario would be constantly mitigated and modified

Ranwar Village in Mumbai, India as they provide

scenarios are adopted. The results of which would be

through the process. The logic would be tested for its

differentiated situations in terms of environmental and

compared to check the appropriateness of the design

ability produce differentiated results in different sites

socio-cultural factors, they hold spatial configurations

method in each location. These would be further

by adopting a combination of bottom – up emergent

that are unique, have the ability to encourage social

compared with existing urban scenarios to evaluate

method and top-down imposed ambition.

interactions in their own way and are important for their

the output. The recursive comparison at every stage of

respective cultural settings. Comparative study of these

design helps to critically review the results and adopt

two distinct sites would help us derive an extensive and

corrective methods in the following stages. It provides

comprehensive view on the subject.

a perspective into the practicality and functionality of the as well as understand the emergent aspects of the

Further in the process, comparisons would be drawn

design process. Methods

39


3.2 Associative Techniques Design Aspects

Environmental Responses

E Morphology M Of Built and Open Spaces

S

Socio- Cultural Aspects

Fig 3.2.1

40

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The research, analysis and design generation of the project is

focus will be made on open spaces and how the built morphology

based around interdependency of morphology, environmental

shapes around them. In terms of environmental parameters, the

responses and socio-cultural aspects (Fig 3.2.1). The parameters

analysis of the indigenous social spaces focuses on two aspects:

therefore are selected based on keeping these factors in context.

public spaces and semi-public spaces. Both typologies are analysed

A number of projects have been made on the subject where

and compared with each other to enable not only differentiation

interrelationship between climatology and built morphology are

of the respective attributes, by means of parameters, but also

explored. Open spaces and their relationship to socio-cultural

implementation occurring at different levels of the design process.

aspects are somewhat untouched as there are no direct tools to

They are assessed to strengthen justification of their use in site.

convert extracted information into applicable quantitative data

They are also evaluated with independent criteria that help us

informing a computational system.

further in the definition of these spaces in the new urban context.

The dissertation therefore attempts to relate factors such as privacy,

The following pages document how the various parameters and

views, connectivity, space definition and spatial organisation

organisational aspects found within the indigenous settings will be

to socio-cultural aspects essential for an urban intervention. A

studied and what part of the data will be extracted.


Aggregation & Dimension

Connectivity With Street Connectivity in the fabric is analysed following the idea that the street should be a part of the open space, as was the case in the past where streets were part of the social structure, allowing for travel, interaction and trade. This has changed dramatically in the current urban context where the streets are rationalised in a way so that they only allow transport. Scope for any informal activities along streets are limited. This also affects the relation between the

Fig 3.2.2 Semi-public space arrangement with two to four units

In a study of building aggregation around a space, it is crucial to understand how the semi-public and public spaces are formed at the local and cluster levels. This also will indicate the functional

defined network and open spaces, where a strong division exists between vehicular access and pedestrian access. The historical structure has a larger focus on open space acting as a core element;

a. Historical structure

a factor not seen in the modern day urban design (Fig 3.2.5).

aspect of the space. At the local level, the semi-public space is integrated as part of the block typology formed by an aggregation of buildings (Fig 3.2.2). At the same time, dimensions that are b. Modern structure

most common in the two selected sample patches are extracted

Fig 3.2.5 Historical and modern settlement structure [10]

checked if the function of these spaces suit the urban context. This measurement of spatial quality will help in the generation process as an input rule, to follow the same logic observed in the indigenous settlement.

Spatial Hierarchy

Fig 3.2.4 Semi-public and Public Spaces

Open spaces and the street should have a very similar quality, such that the street can essentially be part of the open space. By comparing two chosen settlements, we will investigate how the

Private

street’s used, as well as the street hierarchy and connectivity with the open space. Apart from quality, the open space should also

Covered semi-public

be designed keeping in mind its interaction and transition to its surrounding network.

Semi-public Public space

Programmatic Activity

Fig 3.2.3 Typical Street Hierarchies

To understand how semi-public and public spaces function, it is

Spatial hierarchy is referred to as ‘the rank or order of importance

In the work of Jan Gehl ‘Life between buildings’ , the relationship

(Fig 3.2.3). This is an important part of our

between open space, quality and activity is explained. Fig 3.2.6

analysis as it primarily defines the order and organisation of various

reflects how if the open space has a good quality, it directly influences

kinds of spaces. Hierarchy is based on two factors: visibility and

how people use the space for optional activities. Different functions

accessibility. This is an essential consideration as it determines the

and activities will be assessed with its respective implementation

desired functionality of the spaces. For the case studies the focus

within the day, in order to evaluate a critical time period. The

will be on analysis of the semi-public and public spaces. Learning

spatial environment will be addressed based on this time factor to

from the Belapur housing project (Page 24) in new Mumbai and

determine when the space needs optimisation of conditions such

Asian game village in New Delhi (Page 28), it is important to

as shaded area proportion or incident solar radiation. Different

understand how the spatial hierarchy would be determined and its

activities in these spaces will also provide information on the kind

affect on the quality of semi-public and public spaces.

of morphology that is desirable for that function.

of various spaces’

[9]

important to analyse their variant usage throughout the day. [3]

Fig 3.2.6 [11] Quality of the physical environment

[9] Spatial strategies: Hierarchy, Open Forum Available from: [http://spatialstrategies.webs.com/ apps/blog show/244 81011-spatial-strategies-hierarchy] [Accessed on : 20 June 2013] [10] Marshall, S., (2005) Street Patterns, Streets p. 20, Spon Press, Abingdon Oxon [11] Gehl, J., (1987) Life between buildings, p.13, Island Press,Washigton DC

Methods

41


Morphology and Sizes

plays an important role in defining environmental conditions and will be used as one of the main factors. These parameters will be defined based on the case studies and will provide the technical information for site specific building orientation.

Definition of Patch Size In the indigenous settlements the boundaries between different blocks or clusters are blurred and the demarcation between Typical Courtyard house

different communities are unrecognisable. To understand the Large Courtyard house

dynamic nature of public spaces better, the fabric will be divided into smaller cluster level organisations according to proximity of these public spaces from residences.

Fig 3.2.7

Building morphology will focus on how the built forms are organised with respect to the semi-public and public spaces

(Fig

Public Space

3.2.7). Different morphologies will be studied from case studies to analyse the relationship between aspects of quality and the built environment. Variations in these morphologies will be based on the existing environmental and socio-cultural factors in order to contextualise the project. Typical sizes of the spaces also will be part of the reference as they will define the functional and organisational attributes. Closest Public Space for Buildings

Orientation

North

West

East

West

East South

Building orientation is a fundamental factor that needs to be

42

SynchroniCity

Defining Clusters Fig 3.2.8

considered for environmental efficiency. In our design exercise we

A computational tool will be developed to connect the entrances

will use orientation to understand building position with respect

of the houses to the closest public space as shown in Fig 3.2.8.

to solar factors. Orientation of the built form will drive different

Proximity will be measured as the actual travel distance, not simply

responses based on the climatic conditions experienced.

a the distance connecting two points. The output will provide us

As the case studies will be in different climatic zones, it will

with information concerning the number of houses utilising the

be important to understand the building orientation and its

public space, the coverage distance and the longest travel distance

response to the environmental conditions. Surface area ratio

experienced in order to access a public space.


3.2 Associative Techniques Parameters

Shaded Area Proportion

Sky View Factor

To fully analyse the functions occurring in the open spaces, we

Sky view factor is the amount of sky visible when viewed from the

examine daily activity that may take place. Shaded area proportion

ground up (Fig 3.2.10) where generally a ‘fish-eye’ photograph is

to a large extent contributes to the quality of the space. A non-

taken from the street level. It is measured in the range of 0 to 1,

shaded area may be too uncomfortable to use in such hot climates

where a sky view factor of 1 denotes a completely visible sky, and

and an overly shaded area may become too dingy for usage (due to

0 denotes a sky blocked by obstacles. The higher the value, the

poor lighting conditions). The right amount of shading at the right

quicker the urban canyon will cool, because more sky is available

time of the day is essential for the space to perform efficiently.

to absorb the heat retained by the buildings. With a low sky view factor, the canyon can retain more heat during the day, creating a higher heat release at night.

Sun position

Shadow casting from blocks

Viewing angle from centre of open space

Shaded Area Proportion calculated Fig 3.2.9

Therefore, this parameter will be used as the percentage of area shaded at a particular time of the day (Fig 3.2.9). The 21st of June

SVF

will be used as the date of analysis, as the position of the sun is at its highest during the year. If using this factor, the design is able

Visible Sky Area Projected Area Fig 3.2.10

to retain the same, or provide a better, shading solution from the

Sky view in the context of semi-public and public spaces is directly

analysis of the case studies, we can ensure that the rest of the year

related to experiential and socio-cultural aspects. The built

period will be maintained with good shading quality. Several sun

morphology around the multi-level open spaces will be directly

positions will be taken from 12:00 to 14:00 hrs in the day, and the

governed by the sky view factor. Both connotations of the sky-view

percentage of the building shadow cast on the open space will be

factor, environmental and experiential aspects, will be used for

calculated.

design considerations. Methods

43


Open Space Ratio

Incident Solar Radiation

Open space ratio will provide the amount of open space required per household (Fig 3.2.11). For semi-public and public spaces to function, it is desirable that there is an active participation from users. Overcrowded or scarcely used spaces often don’t turn into interactive spaces; the right proportion of user participation is essential. To understand this aspect, two ratios will be calculated, semi-public space per built up area for the block level and public space per built up area for the cluster level. These ratios will be analysed from the vernacular settlements and their application in the proposed design evaluated.

Incident Solar Radiation analysis on Open Spaces and Pedestrian level. Average hour radiance value.

The semi-public and public spaces can only work if they are able to provide comfortable environmental factors. The capacity of these spaces to maintain or reduce temperature to a comfortable range is an important aspect to be considered. Therefore, apart from the shaded area proportion, incident solar radiation will be considered. Incident solar radiation, insulation, is measured as the energy received on surfaces during a selected time period. Calculation of this parameter will be based on hourly readings during the hottest periods from June to September. The solar radiation is converted

Semi-public Area

to thermal energy, increasing the overall temperature of the open space. These values will be used for generation and transformation of semi-public and public spaces in design to match the qualities found in case studies.

South Facing Surface Ratio

Total Built Area

South Facing Surface South facing surface area Total surface area Fig 3.2.14

South facing surface ratio

This parameter is based on building orientation. The ratio can be calculated using the south facing surface area divided by the total Semi-public space ratio

Semi-public Area Total Built Area

Fig 3.2.11

surface area (Fig 3.2.14). Due to the sun path, south facing surfaces receives a higher concentration of sunlight throughout the year. This aspect will be used for a passive solar design. A higher number of south faces will mean higher sun exposure, and therefore higher

44

SynchroniCity

At the block level, the modern urban development reduces the

solar gain [ideal situation for cold climates]. However, as the sites

semi-public space to increase density. Therefore, in order to

are located in hot climates, less south facing surfaces would be

maintain the desired open space ratio, this parameter will also be

optimal in a passive design. The indigenous settlements will be

applied in the generation process of the design logic.

studied with this aspect in mind.


Enclosure Value

Floor Area Ratio (FAR)

Semi-public Space Area

Total Built up Area

Plot Area

Facing Surface Area

Enclosure Value

Facing Surface Area Semi-public Space Area Fig 3.2.12

FAR

Total built up area Plot Area Fig 3.2.13

Enclosure is defined as the ratio of enclosing surfaces to the space

Floor area ratio is one of the widely accepted methods of calculating

enclosed (Fig 3.2.12). In simpler terms, it is the ratio of surrounding

the floor area density. It is the ratio of total built up area to the plot

surfaces to the semi-public or public space within.

area (Fig 3.2.13) specifically, the amount of built up available per unit plot space. This aspect will be used for calculating the density,

This parameter defines the morphology of contextualised

as population is directly proportional to the floor area available.

boundaries that is built around the open space. A higher enclosure value will mean a highly contained space disconnected from its

Different subsets of FAR would be used at different stage of the

neighbourhood, while a lower value of enclosure will denote a

process. FAR at block level would be the ratio of Floor area

well connected or exposed space. This would imply that a higher

accommodated in the plot, at cluster level it would be the ratio of

value would indicate an intrinsic space and a lower value would

total floor area in the cluster to the total area of the cluster and at

indicate an extrinsic space. Different cultural settings would

the neighbourhood level it would indicate the ratio of total floor

require different enclosure values for different kind of spaces.

area in the site to the total area of the site.

The enclosure is adopted as one of the parameters to describe the experiential aspects of the space. Methods

45


3.3 Generative Techniques

Block Generation Semi-public Space Building Units

Cluster Generation

Neighbourhood Generation

Public Space

Density / Network Patterns

Block Aggregation

Cluster Aggregation

Adjacent Block Relation

Block Differentiation

COMPUTATIONAL TOOLS Genetic Algorithm Ecotect/ Geco

Genetic Algorithm

Genetic Algorithm / Peripheral

Rectangle Packing

Packing / Geometry Reorganisation

Ecotect/ Geco

Ecotect/ Geco

The proposed design will be approached in three stages: the

pre-optimised parameters of blocks. At the end of the process,

block catalogue generation, and cluster catalogue generation and

the generation of a cluster catalogue will contain different cluster

neighbourhood level design development. Different parameters

organisations to suit various urban requirements.

are considered at different levels of development. The block level will deal with creating individual buildings incorporating

The neighbourhood level aims at testing the application of these

multilevel semi-public spaces, and looking at the built open area

catalogue morphologies in actual site conditions. The ability of

relationship in detail. Buildings are generated such that there is a

these morphologies to create differentiation to suit site specific

possibility to modify them at a later stage. This stage incorporates

requirements is the key exploration. Aggregation of clusters and

parameters independent of solar factors, so that their orientation

subsequent block differentiation to accommodate local aspects

can be modified at higher levels of the process. Iterations would

would be based on analysis of each site condition. Diversification

be generated with evolutionary solver and after evaluating these

will be added by modifying these individuals according to their

morphologies, individuals will be selected to create a catalogue.

situational location within the site. A feedback system will be

The catalogue will contain individuals with differential qualities

implemented, one that informs the block individuals to further

and density attributes.

adapt to global organisation. These modifications will be achieved in response to interaction with neighbouring buildings, design

The cluster level investigates aggregation of individual blocks to

ambitions, network criteria, increasing connectivity and improving

form a central public space, resembling the organisational features

quality of open spaces. The ambition would be to use the same

of indigenous settings. This stage aims at creating a well-connected

system for different site conditions but obtain different results for

community level aggregation with high spatial qualities. The

each by changing parametric and generation criteria.

stage looks at block – block relationships so as to not affect the 46

SynchroniCity


Grasshopper

Grasshopper

Fig 3.3.1 Cluster Footprint Aggregation

Autodesk® Ecotect®

Geco®

Autodesk® Ecotect®

Fig 3.3.2 Shadow Casting Analysis via Eco-Geco-Grasshopper link

Distortion Free Aggregation

Multi-software Data Transferring

Rectangle packing and Peripheral Packing is aggregation that

During to design process, Grasshopper will be adopted as the main

involves packing polygon geometries in a defined space. These

generative platform. However, analysis on spatial configuration or

computational tools are used in the process to efficiently pack the

environmental performances need to be simulated using specific

plots together so as to reduce redundant spaces (Fig 3.3.1).

softwares.

At the cluster and neighbourhood generation stage, these tools will

This leads to the issue that during the genetic algorithm process, it

define how the individual plots of different sizes are aggregated.

is unable to provide a simultaneous feedback analysis to Rhino and

The tools by default do not carry any additional information apart

Grasshopper, for the generation process to optimise.

from the plot sizes; information will be added later based on the derived result. The logic employed in the system will be to pack

Therefore it is crucial to create direct links between Rhino/

the selected rectangles into the smallest possible space, with least

Grasshopper models and analytical tools such as Autodesk®

number of pocket spaces and bounding area. This will provide two-

Ecotect® or Depthmap. And only by this way we are able to transfer

dimensional results that will vary depending on the start condition.

data between platforms in forms of iterative loops so that the

As this is not an algorithm based process, multiple different

spatial analyses are included not as a static evaluation but as a

iterations will be generated for a further evaluation process, where

dynamic aspect of the design process.

the best plot aggregation will be selected based on differentiated criterion.

Methods

47


Fig 3.3.3 Pattern diversification in the Hawaiian Drosophila

Genetic Algorithm The architectural design process is increasingly becoming more

individual buildings and cluster level to optimise aggregation of

complex and precise. The need to simultaneously consider a

these blocks.

number different parameters, analyse them and accordingly, generate optimised solutions, has brought Genetic Algorithm into

Research

architectural applications. Generative algorithms apply concepts

morphologies will attribute to a number of different parameters.

of Emergence through the use of evolution and morphogenesis

To take these factors into account and simultaneously generate

as a method of producing innovative forms. They provide

morphologies that are optimised across multi-parameters, we

further optimisation capabilities allowing for multi-parameter

will use this algorithm where three kinds of data will feed into

augmentation to derive a well-defined design solution.

the system. These are: fixed data that will be kept as a constant

detailing

successful

functioning

of

indigenous

throughout the generation process, variable data or genomes that

48

SynchroniCity

The thesis will attempt to demonstrate incremental improvement

can vary depending on situation, and Fitness Criteria which will

of prescribed design performances through a computational

evaluate the optimised form generations. The fitness criterion

process and emerging levels of complexity. The Genetic Algorithm

concerning enclosure value, sky view factor and solar shading will

greatly aids the simultaneous analysis-generation method to

be used for qualitative analysis, while FAR will be used for density

provide better building performances. The bottom up approach of

analysis. This process will generate a number of iterations where

urban design will be applied at two levels: block level to optimise

different inputs of variable data can provide different results.


l domain

min

max

none

all

r Remap

Criteria 1: number of Verandah connected to front yard

domain

domain l domain

0

1

0 max fittest value

min min

Original Domain

1

none 0°

max

all60°

r Remap alue Remap

domain domain

domain ll domain

0

threshold value

min min

Criteria 1: number of Verandah connected to front yard

Target Domain

1 0

x

1 fittest value max

Original Domain

90°

Amplify x 5

max

5x

0

1 0 95%

1 Criteria 3: Visibility Angle from front yard to street 60° 90° 100%

60% 95%

Criteria 4: Proportion of Area Overlapped 1 Criteria 3: Visibility Angle from front yard to street 100% 100%

0° 87% y

Amplify x 2

2y

z

Amplify x 1

z

alue Remap on Remap

domain domain

01

0

domain l domain

threshold threshold value value

min min

Target Domain1

max max

Before Amplification

domain domain

0 0 threshold value

min

Target Domain

11

Fig 3.3.4 Mathematical Principles of Remapping Strategies

domain

87% 0%

Original Domain

n Remap Remap

max

01

0

After Amplification

x

y

z

5x

0 0

2y

z

1 1

60%

Fig 3.3.5 Selective Amplification of Values

0%

Criteria of Area Criteria4:2:Proportion Footprint Area/ Overlapped Plot Area Ratio 100%

Remap

domain

Remapping Parameters 0

1

Differential Weighting

0

The parameters employed in the system will possess different

Differential weighting will be used when the parameters cannot

numeric domains. For comparison in analysis and scoring factors,

be computed by simple summation or averaging. It refers to

they will need to be remapped into a standard domain of 0 to 1 (Fig

weighting parameters differently based on their significance and

3.3.4).

priority to the process. Thus, the higher the priority, the more that

1

Criteria 2: Footprint Area/ Plot Area Ratio

parameter is weighted. This will be used as a method to optimise Various techniques will be used for different kinds of remapping, as

the result towards a desirable direction through differentiating

ranges of original numeric values are initially dissimilar. For certain

primary aspects from secondary aspects, by way of weighting. For

parameters, the higher value does not necessary equate to a higher

example, in a list of parameters, density is of the most significance

score. Therefore, all the parameters will be remapped such that

as the desirable result has to accommodate higher density. Based

numbers closer to 1 will mean a fitter score and numbers closer to 0

on this preference factor, weighting on density parameter will be

would reflect a lesser score. Threshold or toggle values will also be

set as highest and amplified with a higher multiplicative factor

set using this method wherein if the numeric value doesn’t surpass

(Fig 3.3.5). Differential weighting of fitness criterion works well

a certain limit, it will be substantiated as a 0. This is an important

with generative algorithms to generate different iterations of

step for the generation process as all the fitness criterion need to

morphology. The weighting criterion provides the architect with

be able to be compared with each other in the same domain, to

control over the results generated in the design process.

calculate a single numeric result. Methods

49


50

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4

ANALYSIS OF EXISTING INDIGENOUS SETTLEMENTS The aim of this chapter is to understand the indigenous settlements with respect to various parameters. It will focus primarily on the inter relationship between the built morphologies and the social spaces that are created at the local and regional level. Over time, the indigenous settlements have evolved out of necessity. They have adapted to their local aspects and thus prove to be more sustainable environmentally and socioculturally. Studying these will provide understanding of the working of social spaces which would be utilised to design a system that incorporates these aspects. For this purpose, two patches with notable social spaces have been chosen to investigate their respective adaptation strategies. 4.1 Indigenous Settlements : Overview and Design Logic

54

4.2 Indigenous Settlements : Parametric Study

67

4.3 Conclusion

81

Analysis

51


Fig 4.0.1 Indigenous Settlements of Medellin

52

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4.1

INDIGENOUS SETTLEMENTS OVERVIEW AND DESIGN LOGIC

The information from the case studies will form the basic inputs for

try to accommodate these spatial attributes in higher density situations

socio-cultural aspects of the design. The relationship between built and

which can provide an alternative for development of such locations.

open spaces will be extracted from these case studies to be used in the urban design model. To make a comprehensive study we identify two

The primary reason driving the selection of these sites for study, is that

sites for our study.

in both settlements open spaces form an essential part of their social and cultural system and are well integrated in the built fabric. The

Nan Chi Zi in Beijing, China and Ranwar Village in Mumbai, India are

relationship between built and open is such that informal interactions

selected as they provide contrasting situations in terms of environmental

are accommodated at multiple levels. There is a deďŹ ned hierarchy of

and socio-cultural factors. Both of these settlements are historic and are

spaces from absolute public to absolute private, which allows dierent

located in major cities, thus facing tremendous pressure of urbanisation.

kinds of activities to take place in these spaces. Therefore open spaces

Both settlements hold rich and unique spatial characteristics that

in these indigenous settings are better utilised as compared to modern

are exclusive to its respective indigenous settings, have the ability

urban scenarios. It is with this respect that public spaces and semi-public

to encourage social interactions and are important for their culture.

spaces, and their relationship with built form, will be studied. The motive

These spatial aspects will be lost if the current trend of urbanisation

of the design will be to use their spatial attributes informing the design of

dominates the existing conditions. The new model of design would

a higher density urban fabric. Analysis

53


4.1 Indigenous Settlements : Overview and Design Logic Case Study A Mumbai

Fig 4.1.1 Ranwar Village is situated in the suburb of Mumbai

Fig 4.1.2 Typical Residential Buildings in Ranwar Village

Patch Size: 350 x 300 m Area: 0.11 km2

Ranwar Village, Mumbai

Population: 250 people / Ha

Ranwar Village is a small indigenous settlement located in the

Number of buildings: 290

suburb of Bandra, isolated from the surrounding city of Mumbai

Building Height: 3- 7.5m

(Fig 4.1.1). This settlement dates back to 1850’s and still retains dwellings that are about a hundred years old. It retained its social characters and a strong sense of community until the 1960s and 1970s. After which the pressure of urbanisation and the subsequent demographic demands started a slow decline in its spatial character.

350m

This village is known for its numerous pocket social spaces spread over the site called ‘chowks’ or squares. The settlement is composed of low-rise buildings with heights ranging from 3m to 9m. Each of these buildings has shared open semi-public space or front yards on the ground level.

Entrances are typically through covered

semi-public spaces or porches which are vertically stacked in cases of houses at higher floors. The distinct spatial flow from public spaces to private spaces brings about gradient levels of privacy and involvement with the surroundings. The ever growing population and the increasing demand of living spaces lead to a dramatic 60% decrease in the number of

300m

traditional dwellings in the last decade, these traditional units have been replaced either by commercial or residential high rises. Over the years, the squares and the streets which act as public open spaces have remained the same but the semi-public spaces and primarily the private open spaces have been consumed to incorporate higher density. To study these spatial aspects of these socially relevant public and semi-public spaces in more detail and in order to understand the reason of their successful usage, this site 0

54

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100

is chosen to inform the design process.


Case Study B Beijing

Fig 4.1.4 Nan Chi Zi area is situated in the centre of Beijing

Fig 4.1.3 Typical Residential Buildings in Nan Chi Zi

Nan Chi Zi, Beijing

Patch Size :350 x 300m

The origin of Beijing City planning dates back to 14th century

Population: 400 people / Ha

and as it grew it followed the same pattern of radial growth from

Number of buildings: 470

the centre to the outskirts. The present city has grown over a

Building Height: 4- 20m

Area: 0.11 km2 300m

thousand times beyond the area of the old city. The site selected in Beijing is situated at the heart of the city that is on the east side of the inner city (Fig 4.1.4). As it is located between the traditional (Perking) and the modern infrastructure (commercial zone), the fabric encompassed by courtyard houses and hutongs or streets has changed dramatically. The traditional architecture is defined by courtyard houses; this area is also known for its social spaces which exist in the form of streets commonly known as ‘Hutong’. Typically, there would be two different types of Hutongs, where the secondary hutongs are pedestrian streets consisting of various household functions. These functions are an important factor not only for the neighbourhood to be self-sufficient but also as drivers of social life in the location. The height of buildings range from

350m

4m to 20m. The fabric comprises a variety of building typologies within it. The pressure of increasing density and modern development has forced the site to evolve and change in character in the past 20 years. Considering the presence of the traditional courtyard typologies in the prime location and the ever increasing demand for housing, nearly 50% of the traditional single story houses have been replaced by either commercial high rise or new housing typologies. This alteration has destructively disrupted the social spaces defined by traditional streets and housing typologies.

0

100 Analysis

55


Open Space Configuration Mumbai

Public space Semi-public space

Fig 4.1.5 : Chowk as Public Space

Fig 4.1.6 : Porch as Semi - Public Space

Semi Public Spaces Morphology

Derived Fig 4.1.9 : Typical Section of Porch

To understand the semi-public spaces with respect to the morphologies, different characteristics and their relationship Fig 4.1.11

The semi-public spaces from two to four units form a few different block typology

with surrounding built forms are further investigated. The semipublic spaces in Mumbai are usually formed by aggregating two to four units (Fig 4.1.11) while in Beijing minimum four units form the space (Fig 4.1.13). The semi-public spaces in Mumbai exist as

Public Spaces

partly covered to respond to environmental conditions. They are

Morphology

located within 1 -2 minutes’ walk from the closest public space and are offsets from the street. In Beijing, they are organised as intrinsic courtyards arranged linearly from the Hutong. The courtyard morphologies are designed to have passive ventilation to deal with extreme climate changes throughout the year. The Derived

average areas of open spaces in Beijing are almost 30 % larger than the ones in Mumbai. This means more area on the ground is covered with social spaces making the neighbourhood more connected. The extrinsic and intrinsic semi-public spaces have different social characteristics. As an experimental attempt, both types of morphology would be used to create variation in social

Fig 4.1.12

56

Public space shares more connection to the street and form part of the pedestrian network

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spaces. Both the patches in Mumbai and Beijing have distinct semipublic and public space responding to their specific cultural and


Beijing

Public space Semi-public space

Fig 4.1.7 : Huntong as Public Space

Fig 4.1.8 : Courtyard as Semi - Public Space

Semi Public Spaces Morphology

Fig 4.1.10 : Typical Section of Courtyard

Derived

environmental aspects. The characteristics of the morphologies are dependent on the dierent type of functions they serve. The courtyards in Beijing are used for household activities therefore visually segregated, whereas in India these building typology form spaces for casual interactions which they keep in moderate visual contact with the streets. The public spaces in Beijing exist in the form of hutongs that act as casual gathering spots (Fig 4.1.14)

Fig 4.1.13

The semi-public spaces in Beijing exist as courtyard as parof the block typology

Public Spaces Morphology

and in Mumbai they exist as chowk squares which can be used as mass gathering spots for festive occasions (Fig 4.1.12). These spaces are usually formed on the secondary roads. This shows the need for these places to keep easy accessibility, moderate visibility but still maintain some kind of seclusion from the main traďŹƒc. The Derived

morphologies from both studies would be used to build strategies for the design process. Mumbai Beijing Average area of semi-public space

50 m2

70 m2

Mumbai Beijing Average area of public space

251 m2

300 m2 Fig 4.1.14

Hutong is used as street network and public space Analysis

57


Morphology and Hierarchy Mumbai

Type 1

Fig 4.1.15 Spatial relation between semi-public and public space

Private: Semi-Public: Built: Floors: Used by: Population: Area: Open vs built: Semi-Public / person:

Type 2

51 m 5.2 m2 51 m2 1 Single Family 6 57 m2 10% 0.8 m2 / person 2

Private: Semi-Public: Built: Floors: Used by: Population: Area: Open vs built: Semi-Public / person:

Type 3

182 m 54 m2 94 m2 2 2 Families 10 126 m2 29% 5.4 m2 / person 2

Private: Semi-Public: Built: Floors: Used by: Population: Area: Open vs built: Semi-Public / person:

280 m2 104 m2 142 m2 2 2 Families 10 200 m2 38% 10.4 m2 / person Fig 4.1.16

The selected patches in Mumbai and Beijing are approximately 0.11 km2 each which makes a self-sufficient residential neighbourhood. One of the distinct features of indigenous settlements is the space continuity as they express in transition from totally public to totally private. In both examples, the social spaces exist as distinct hierarchy of public and semi-public spaces. Therefore, for better understanding the spatial hierarchy factor was investigated at both the local and regional levels. The configuration of built form at both these levels plays a huge role in defining the hierarchy of spaces. Levels of spatial hierarchy Public space

Regional Fig 4.1.17

58

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In Mumbai, the blocks with larger footprint area are always arranged around the public spaces and the smaller blocks are placed away from it (Fig 4.1.17). However, the porous tessellation of these

Semi-public space

smaller blocks forms a semi-public space that is shared by several

Covered semi-public

households. In Beijing the Hutongs which perform as public spaces

Private space

always open onto smaller secluded semi-public spaces which then


Beijing

Type 1 Private: Semi-Public: Built: Floors: Used by: Population: Area: Open vs built: Semi-public / person:

Type 2

260 m 155 m2 415 m2 1 Single Family 8 570 m2 37% 26 m2 / person 2

Private: Semi-Public: Built: Floors: Used by: Population: Area: Open vs built: Semi-public / person:

Type 3

690 m 350 m2 690 m2 1 Multiple families 40 1040 m2 50% 9 m2 / person 2

Private: Semi-Public: Built: Floors: Used by: Population: Area: Open vs built: Semi-public / person:

3200 m2 1000 m2 1600 m2 2 Multiple Families 30 2600 m2 63% 33 m2 / person

Fig 4.1.18

Fig 4.1.19 Spatial relation between semi-public and public space

lead to the private areas (Fig 4.1.19, Fig 4.1.20). In both the cases the entrance to the private spaces is always from these shared semipublic spaces. The study of local and the regional levels shows that the transition of spaces from the private to the public areas i.e. the spatial hierarchy is always maintained. The connection with these social spaces depends highly on the level of privacy. At the local level the tertiary streets are always connected to the semi-public spaces while the public spaces always exist on the secondary streets. These social spaces are always secluded from the primary network in order to achieve a certain amount of privacy. The dierent levels of hierarchy have indicated the change in visual privacy and accessibility. The organisation allows subtle transition from one level to another without major physical boundaries. The other aspect is the integration of tertiary streets as interaction spaces within the fabric. These are essential characteristics to consider in the design process.

Levels of spatial hierarchy Public space Semi-public space Private space

Regional

Fig 4.1.20

Analysis

59


Network Patterns and Associated Programmes Mumbai

Primary Road Secondary Road Tertiary Road

Primary Road

Fig 4.1.21 Network Hierarchies in Indigenous Settlements

Fig 4.1.22 Busy primary road that accommodates markets, shops and other household necessities

Secondary Road

Tertiary Road

There is a distinct hierarchy of streets visible in both the patches. In both the studies there is a dierentiable change in character of streets from primary to tertiary. In Mumbai, the primary road serves the entire neighbourhood with the basic necessities. Fig 4.1.23 Local Square located on Secondary route serves as religious or other mass gathering spot and doubles up as parking

Markets and commercial shops are present along this road (Fig 4.1.21). In Beijing primary roads are peripheral and the household retail units are located on the secondary routes. In both the cases the tertiary streets are generally pedestrian and become part of the extended household. They are used as interaction spaces where there is proximity to small commercial / retail activity. The distribution of the activities along these streets was studied

Fig 4.1.24 Small Local Square used as play area and spot for informal interactions 60

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to understand their functional relationship with social spaces. In Mumbai, the semi-public spaces are arranged in such a pattern that they are connected directly to either one or two streets depending


Beijing

Primary Road Secondary Road Tertiary Road

Primary Road

Wide Hutong Fig 4.1.25 Network Hierarchies in Indigenous Settlements

Narrow Hutong

Fig 4.1.26 Local household retail shop set up on the secondary routes

upon their location. The public spaces usually have 4 tertiary or secondary street connections making them well integrated and accessible. In Beijing, the semi-public spaces are always segregated to maintain privacy, while the hutongs are used as street network and public space. The secondary hutongs are pedestrian routes which then connect the semi-public spaces. These social spaces provide multifunctional usage in both the studies. The hutongs are

Fig 4.1.27 Informal interaction spot on the Tertiary Hutong

generally associated with small commercial activities which can be used on daily basis while the chowks act as religious spaces. Hierarchy of streets and their relationship with public and semipublic spaces is extracted from the case studies to inform the design process. Fig 4.1.28 Market set up on daily basis on Secondary Hutong Analysis

61


Building Orientation

Mumbai

Comparison of building orientation in Ranwar Village

Least area of bounding box

Rule 1: 30

30

Rule 2: a

Fig 4.1.29 Mumbai map showing the buildings (highlighted) having North - South Orientation to minimise South Face

b

b a

Beijing

1.2

Fig 4.1.31

Oriented along: Mumbai Beijing 25 % North - South 70 % East - West

30 %

75 %

Mumbai experiences an average annual temperature of 30oC, Beijing in contrast experiences extreme climate changes between summer and winter period. The buildings in Beijing have east- west orientation (Fig 4.1.30), so as to maximise the solar exposure during the winter. The courtyards houses also serve for passive ventilation which cools the building during the summer. According to the speciďŹ c environmental response, around 70% of the buildings in India are aligned along north-south axis to minimise the solar exposure on the building, and 75 % of the buildings in Beijing are aligned along east-west axis to maximise the solar exposure respectively. This makes it clear that the settlements respond to the local climate in order to create a distinct morphological arrangement. To test and calculate the number of buildings oriented in this fashion, the length - width ratio was considered to calculate the number of building oriented in this fashion.(Fig 4.1.31). The criterion for this calculation was a ratio of length and Fig 4.1.30 Beijing map showing the buildings (Highlighted) having East - West Orientation to maximise South Face 62

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width to be 1.2 and the buildings should lie in the range of -30 deg to +30 deg from the edge with respect to the axis.


Defining Cluster Sizes

Mumbai

Fig 4.1.33 Example of Mumbai Cluster

Fig 4.1.34 Mumbai map showing the buildings (highlighted) having North - South Orientation to minimise South Face

Beijing

Fig 4.1.32 Distance to closes public space defines the cluster

At the regional level, the clusters are defined with respect to the use of public space. Distance from the buildings to a public space was drawn and the closest ones were chosen. This process is applied to all the buildings, where the fabric gets divided into different clusters depending on proximity to the closest public space (Fig 4.1.32). The public space will be studied in detail at the regional level with respect to the clusters. The aggregation logics extracted from this study can inform the design process. In the preliminary studies, variation in density and the sizes of the clusters are observed in both the patches. The exercise shows that each public space in Mumbai had around four semi-public spaces surrounding it within 1-2 minute walk (Fig 4.1.34). While in Beijing, most of the semi-public spaces directly opened onto the public spaces (hutong). The clusters consistently cater to a diameter of 90-100 m which is about 1 to 2 minutes walk (Fig 4.1.36). Fig 4.1.35 Example of Beijing Cluster

Fig 4.1.36 Mumbai map showing the buildings (highlighted) having North - South Orientation to minimise South Face Analysis

63


Design Logic

Analysis

Mumbai

Extracted Logic

Beijing

Morphology and Sizes (Building morphology)

Proposed block typology includes both courtyard and frontyard of semi-public space Private: Semi-public: Population: Semi-public / person:

280 m2 104 m2 10 10.4 m2 / person

260 m2 155 m2 8 26 m2 / person

Size and morphology are important aspects for a space to work. An average size of 60 m2 (average of the size in Mumbai and Beijing) is considered for the design process. These semi-public space are generally shared by 3 to 4 families. For the Public spaces the size cannot be directly adopted from indigenous settlements as urban scale application would require larger areas. However, the proportion between

Extrinsic frontyard semi-public space Redial arrangement of blocks to form a public Mumbai space

the public space and the cluster size is taken into consideration.

Mumbai Beijing

Aggregation and Dimension (Semi-public and public space)

Average area of semi-public space 50 m2 Intrinsic courtyard semi-public space Beijing

Mumbai Beijing Average area of public space

Public space Semi-public space Covered semi-public space

70 m2

Transition from private to public space follows the spatial hierarchy logic.

251 m2

300 m2

Public space Semi-public space Private space

Private space

Spatial Hierarchy

The integration of semi-public spaces within the

Mumbai

built fabric is one of the primary aspects of the Public space

Beijing

64

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design phase. This is essential to create a hierarchy of spaces in the proposed urban design similarly to

Semi-public space

the indigenous settlements. Semi-public spaces is

Private space

introduced as a transitional space between the public and private areas.


Design Logic

Analysis

Extracted Logic

The semi-public and public spaces should be integrated with the streets to not only improve functionality but also

Connectivity With Street

add quality to the streetscapes. Mumbai

Different functions and activity take place in the same space at different times of the day. Therefore, spaces should

Programmatic Activity

be designed to accommodate variable activities. Oriented along: Mumbai Beijing 25 % North - South 70 %

Orientation

East - West

Radius 85 -95 M

30 %

75 %

Mumbai and Beijing show exactly opposite requirements for orientation. This aspect allows the same block to be used in both the sites just by changing the orientation.

Radius 90 -100 M

By analysing the blocks surrounding the public spaces, Defining Cluster Size

logic can be extracted to inform cluster sizes, proportions and aggregation of public spaces

Conclusion Comparing the two samples it was clear that there

At the same time, the relation between the semi-public

always maintained. From this chapter, several design

is a variation in density and sizes of clusters. The

and public space to the street were investigated and

logic are looked into, to understand the different

aggregation of blocks was a key part to be extracted from

they are closely linked with the cultural activity that is

environmental and cultural factors with respect to the

both case studies, which shows that each semi-public

happening in these spaces. Different types of activities

use of social spaces.

space is formed by two or more buildings to generate the

not only shaped the social spaces around them, they

courtyards and frontyards. It was also interesting to see

are also closely linked with the specific environmental

Following this study, the parameters will be further

how the semi-public spaces are spread on the ground

condition. Even though both samples have distinctly

analysed to extract numerical data that can help to

in Beijing while they are stacked one above the other in

different characteristic of the semi-public spaces, the

define the spatial qualities and design strategies which

Mumbai, in response to the local climate and activities.

transition of spaces in terms of spatial hierarchy is

can be applied in evolutionary computational. Analysis

65


66

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4.2

INDIGENOUS SETTLEMENT PARAMETRIC STUDY This part of the analysis chapter deals with extracting numeric parameters from the indigenous settings for application in the computational design process. The numeric data will be analysed and compared within the two case studies. The best identiďŹ ed aspects will be used in the design logic. The study will be presented in two levels, a block level that studies and investigates characteristics of semi-public spaces and a cluster level that explores the relationship between the built form and public spaces.

Analysis

67


4.2 Indigenous Settlement : Parametric Study Local Analysis Mumbai

Semi-public space are located on the ground and even distributed at higher levels

Fig 4.2.1 Samples of Indigenous Settlements in Mumbai

Sample 1

Sample 2

Sample 3

Sample 4

Sample 7

Sample 1

Sample 2

Sample 3

Sample 4

Sample 5

Sample 6

Sample 7

Sample 8

Average

Open Semi-public space area (m2)

55

57

135

28

92

64

53

92

103

Covered Semi-public space area (m2)

219

131

328

20

51

92

146

76

133

Patch area (m2)

708

722

1016

405

683

762

515

583

Total built area (m )

543

356

894

124

241

712

419

153

Footprint (m )

343

254

588

144

292

547

192

207

674 430 321

Number of oors

1 to 3

1 to 3

1 to 3

1

1

1 to 3

1 to 3

1

2

Fig 4.2.3 Table showing Statistics from Mumbai Samples SynchroniCity

Sample 6

Mumbai

2

68

Sample 5

Sample 8

Semi-public space


Beijing

Semi-public space are located on the ground only Fig 4.2.2 Samples of Indigenous Settlements in Beijing

Sample 1

Sample 2

Sample 3

Sample 4

Sample 5

Sample 6

Sample 7

Beijing

Sample 1

Sample 2

Sample 3

Sample 4

Sample 5

Sample 6

Sample 7

Sample 8

Average

Semi-public space area (m2)

185

104

81

56

110

45

150

96

103

Patch area (m2)

541

393

334

256

417

211

705

470

Total built area (m )

712

289

253

200

297

166

555

274

Footprint (m )

356

289

253

200

297

166

555

374

415 343 311

Number of oors

2

1

1

1

1

1

1

1

2

2

Sample 8

Semi-public space

Fig 4.2.4 Table showing Statistics from Beijing Samples

Analysis

69


Shaded Area Proportion

Mumbai

Beijing

Time 21st.June

S1 (55 m2)

S2 (57 m2)

S3 (135m2)

S4 (28 m2)

S5 (92 m2)

S6 (64 m2)

S7 (53 m2)

S8 (58 m2)

Average percentage

S1 (185 m2)

S2 (104 m2)

S3 (81 m2)

S4 (56 m2)

S5 (110 m2)

S6 (45 m2)

S7 (150 m2)

S8 (96 m2)

Average percentage

Average percentage covered from 12:00-14:00

23%

27%

7%

25%

25%

27%

36%

29%

25%

33%

33%

13%

23%

17%

24%

16%

24%

23%

40% 35% 30% 25% 20% 15% 10% 5% 0%

The analysis of shaded area proportion on the semi-public spaces shows that, even at the hottest period of the day, (12:00 to 14:00 hrs), at least 23% to 25% of the area remains shaded (Fig 4.2.5). This information also reveals that completely shaded, dark spaces do not convert into interaction spaces. The appropriate Mumbai Beijing 1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

balance of shaded and exposed area contributes to creating good environmental condition. The numeric data would be used as guideline for the design process as to how much minimum value is expected. This factor would be essential to create a comfortable condition for summer period so that these spaces can be converted into interaction spots.

Fig 4.2.5 Average percentage covered vs. samples

Incident Solar Radiation

Mumbai

500 450 400 350 300 250 200 150 100 50 0

Average hourly value from May- Sept on semi-public space

Average hourly value from May- Sept on semi-public space

Time May-Sept

S1 (55 m2)

S2 (57 m2)

S3 (135m2)

S4 (28 m2)

S5 (92 m2)

S6 (64 m2)

S7 (53 m2)

S8 (58 m2)

Average percentage

S1 (185 m2)

S2 (104 m2)

S3 (81 m2)

S4 (56 m2)

S5 (110 m2)

S6 (45 m2)

S7 (150 m2)

S8 (96 m2)

Average percentage

Incident Solar Radiation ( Wh/m2)

357

263

411

384

420

347

310

459

378

207

292

311

275

320

245

295

216

276

Incident solar radiation is a part of environmental aspect, and it is closely linked to shaded area proportion but not completely dependent on it. These values are extracted during the summer months in both sites. In Mumbai, the average value is approximately 380 Wh/m2 (Fig 4.2.6), a reduction from the city average

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

Fig 4.2.6 Incident Solar Radiation ( Wh/m2) vs. samples 70

Beijing

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Mumbai Beijing

of 518wh/m2. A similar result is seen in Beijing where the average radiation received is approximately 276 Wh/m2 when the city averaged around 400 Wh/m2. The target value of incident solar radiation should be site speciďŹ c and similar to the case studies for the design process.


Sky View Factor

Mumbai

Beijing

Time 21st.June

S1 (55 m2)

S2 (57 m2)

S3 (135m2)

S4 (28 m2)

S5 (92 m2)

S6 (64 m2)

S7 (53 m2)

S8 (58 m2)

Average percentage

S1 (185 m2)

S2 (104 m2)

S3 (81 m2)

S4 (56 m2)

S5 (110 m2)

S6 (45 m2)

S7 (150 m2)

S8 (96 m2)

Average percentage

Sky view factor for semi-public space

0.63

0.53

0.74

0.49

0.68

0.52

0.62

0.57

0.60

0.41

0.50

0.78

0.34

0.53

0.48

0.37

0.46

0.50

0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0

The sky view factor for semi-public spaces goes up to 0.7 in Ranwar Village whereas this ratio is consistently maintained at around 0.5 in Nan Chi Zi (Fig 4.2.7). Although Mumbai has better sky view factor value than Beijing, they are relatively higher than any urban scenario. This means that a significant part of 1 2 3 4 5 6 7 8

Mumbai Beijing

1 2 3 4 5 6 7 8

the interaction spaces are opened with buildings of only 1 to 3 floors around them. Maintaining this sky view factor in the design process could provide an essential aspect of quality both environmentally and experientially.

Fig 4.2.7 Sky view factor vs. samples

Enclosure Value

Mumbai

Beijing

S1 (55 m2)

S2 (57 m2)

S3 (135m2)

S4 (28 m2)

S5 (92 m2)

S6 (64 m2)

S7 (53 m2)

S8 (58 m2)

Average percentage

S1 (185 m2)

S2 (104 m2)

S3 (81 m2)

S4 (56 m2)

S5 (110 m2)

S6 (45 m2)

S7 (150 m2)

S8 (96 m2)

Average percentage

Building facade (m2)

74

115

81

51

90

98

68

107

544

279

254

165

283

122

324

238

Enclosure value

1.37

2.00

0.60

1.82

0.98

1.53

1.28

1.16

85 1.34

2.94

2.82

3.14

2.94

2.57

2.71

2.16

2.48

276 2.70

4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0

The study reveals that spaces in Nan Chi Zi have a higher value of enclosure. This means that the spaces here are more closely bound by surrounding surfaces as compared to Ranwar village. It also describes the degree of privacy expected. From the values it can be observed that the spaces in Beijing are more

1 2 3 4 5 6 7 8

Fig 4.2.8 Enclosure Value vs. samples

1 2 3 4 5 6 7 8

Mumbai Beijing

intrinsic and spaces in Mumbai are more extrinsic (Fig 4.2.8). The range of enclosure values would be used to inform the system. This would help to design culture specific spaces and therefore have higher appropriateness to the context. Analysis

71


Open Space Ratio

Built Area Covered Semi-public Area Open Semi-public Area

Mumbai S1 (55 m2)

S2 (57 m2)

Beijing S3 (135m2)

S4 (28 m2)

Semi-public space to 26% 22% 22% 18% total build up area ratio 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 Fig 4.2.9 Semi-public space ratio vs. samples

S5 (92 m2)

S6 (64 m2)

S7 (53 m2)

S8 (58 m2)

Average percentage

S1 (185 m2)

S2 (104 m2)

S3 (81 m2)

S4 (56 m2)

S5 (110 m2)

S6 (45 m2)

S7 (150 m2)

S8 (96 m2)

Average percentage

31%

23%

29%

32%

25%

18%

28%

24%

20%

29%

19%

19%

27%

23%

The aspect about semi-public space per person essentially defines the quality of living. In both the indigenous settings, the amount of semi-public space is considerably high compared to modern urban models. This range would be the target value considered to be achieved for our design proposal. These kinds of spaces encompass essential socio-cultural values, by means of activities that take place in them. The semi-public spaces are about 25% of built up area in Ranwar Village and 23% in Nan Chi Zi (Fig 4.2.9).

Mumbai Beijing

These high ratios are attributed due to the functions they serve. These spaces are a part of the household and a large number of daily activities take place in them.

South Facing Surface to Volume Ratio

Mumbai

Beijing

S1 (55 m2)

S2 (57 m2)

S3 (135m2)

S4 (28 m2)

S5 (92 m2)

S6 (64 m2)

S7 (53 m2)

S8 (58 m2)

Average percentage

S1 (185 m2)

S2 (104 m2)

S3 (81 m2)

S4 (56 m2)

S5 (110 m2)

S6 (45 m2)

S7 (150 m2)

S8 (96 m2)

Average percentage

South facing surface (m2)

196

210

208

90

84

109

145

214

415

237

294

327

241

256

184

174

Total surface (m )

1504

1152

2008

643

442

641

690

1338

161 1052

2235

1264

1410

1721

1004

826

681

725

266 1233

South facing surface to total surface area

0.13

0.18

0.10

0.14

0.16

0.15

0.18

0.14

0.15

0.18

0.19

0.21

0.19

0.24

0.30

0.27

0.24

0.23

2

0.30 0.25 0.20

Building orientation remains one of the low cost approaches to improve environmental performance of

0.15

buildings. The analysis has shown that most of the buildings in Mumbai are oriented north–south with longer edges of the building facing east – west (Fig 4.2.10). This considerably reduces the south face and

0.10

there by reducing solar gains. An important aspect to be considered while designing for hot climates. Even

0.05 0

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

Fig 4.2.10 South facing surface ratio vs. samples 72

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Mumbai Beijing

in courtyard typologies in China most of the important rooms are located on the north with longer edges facing the south. This increases the solar gain on the building. This aspect is important in cold climate of Beijing; however this effect is redundant when the temperatures go up to 400 in summer.


Summary Sheet Parameters

Shaded Area Proportion

Analysis (Regional)

Extracted Data

Time: 21st.June

Mumbai

Beijing

A minimum of 20% - 25% of the semi-public space

Average percentage covered from 12:00-14:00

25%

23%

afternoon (12:00 hr to 14:00 hr ) every day.

needs to be shaded for at least 2 hours during the

Depending on the location, the values for solar Incident Solar Radiation

Time: May-Sept

Mumbai

Beijing

Incident Solar Radiation ( Wh/m2)

378

276

incident radiation on the semi-public spaces have to be compared to the values seen in the indigenous settlements. This aspect would be essential to create a comfortable environment.

A minimum of 0.5 sky view factor threshold has been set Sky View Factor

Sky view factor for semipublic space

Mumbai

Beijing

for the design as seen from the indigenous settlements.

0.60

0.50

The criteria would be set as higher the sky view better the quality.

Enclosure values were distinct for each site. Therefore, Enclosure Value

Enclosure Value

Mumbai

Beijing

1.34

2.70

to design in a similar environmental condition as Mumbai, the enclosure should be in range of 1.1 - 1.5 and to design in Beijing the enclosure value should be 2.5 - 2.9.

The indigenous settlement shows a high percentage Open Space Ratio

Semi-public space /total build up area

Mumbai

Beijing

of this ratio. For the urban context and the multi-

25%

23%

semi-public space ratio would be considered at 10%

level integration of semi-public spaces, the built to minimum.

South Facing Surface to Total Surface Area Ratio

South facing surface to total surface area

Mumbai

Beijing

0.15

0.23

With response to the local climate the samples in Mumbai reduce the surfaces facing south while in Beijing they try to maximise the same.

Analysis

73


4.2 Indigenous Settlement : Parametric Study Regional Analysis Mumbai

Fig 4.2.11 Samples of Indigenous Settlements in Mumbai

Sample 1

Sample 2

Sample 3

Mumbai

Sample 1

Sample 2

Sample 3

Sample 4

Sample 5

Sample 6

Average

269

250

489

270

236

264

Patch area (m2)

6392

5685

7400

6731

5946

6248

Total built area (m )

3900

5990

4111

4124

4862

4359

Footprint (m )

3004

3578

2590

2487

2305

2610

296 6400 4558 2762

Number of oors

1 to 3

1 to 3

1 to 3

1 to 3

1 to 3

1 to 3

2

Fig 4.2.13 Table showing Statistics from Mumbai Samples

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Sample 5

Public space area (m2)

2

74

Sample 4

Sample 6

Public space


Beijing

Fig 4.2.12 Samples of Indigenous Settlements in Beijing

Sample 1

Sample 2

Sample 3

Beijing

Sample 4

Sample 5

Sample 1

Sample 2

Sample 3

Sample 4

Sample 5

Sample 6

Average

Public space area (m2)

386

278

977

279

250

237

Patch area (m )

4310

3048

6894

3091

3701

2598

Total built area (m )

2950

2394

4809

2812

3451

3361

Footprint (m2)

2950

2394

4809

2812

3451

3361

401 4253 3296 3296

Number of oors

1

1

1

1

1

1

2

2

Sample 6

Public space

Fig 4.2.14 Table showing Statistics from Beijing Samples

Analysis

75


Shaded Area Proportion

Mumbai

Beijing

Time 21st.June

S1 (269 m2)

S2 (250 m2)

S3 (489 m2)

S4 (270 m2)

S5 (236 m2)

S6 (264 m2)

Average percentage

S1 (386 m2)

S2 (278 m2)

S3 (386 m2)

S4 (279 m2)

S5 (250 m2)

S6 (237 m2)

Average percentage

Average percentage covered from 12:0014:00 on 21st June

20%

24%

10%

15%

18%

21%

18%

12%

13%

15%

10%

16%

11%

13%

20% 15% 10% 5%

The shaded area proportion is analysed for both type of public spaces. It was observed that during the course of the

Mumbai Beijing

0

1 2 3 4 5 6 1 2 3 4 5 6 Fig 4.2.15 Average percentage covered vs. samples

day, in Mumbai the minimum shaded area is 18% and that of Beijing is 13% (Fig 4.2.15). These values are affected by the different configurations they hold at each location.

Incident Solar Radiation Radiance Wh / m2 500 450 400 350 300 250 200 150 100 50 0

Mumbai

500 450 400 350 300 250 200 150 100 50 0

Average hourly value from May- Sept on public space

Average hourly value from May- Sept on public space

Time May-Sept

S1 (269 m2)

S2 (250 m2)

S3 (489 m2)

S4 (270 m2)

S5 (236 m2)

S6 (264 m2)

Average percentage

S1 (386 m2)

S2 (278 m2)

S3 (386 m2)

S4 (279 m2)

S5 (250 m2)

S6 (237 m2)

Average percentage

Incident Solar Radiation ( Wh/m2)

378

349

403

384

420

347

380

301

276

346

275

320

245

293

50 % of the Public space area in Mumbai are shaded for 4 hours daily during summer. 70 % of the Public space area in Beijing are shaded for 6 hours daily during summer.

The values for incident solar radiation on public spaces are similar to the ones seen at the level of semi-public spaces. Mumbai Beijing 1 2 3 4 5 6

1 2 3 4 5 6

Fig 4.2.16 Incident Solar Radiation ( Wh/m ) vs. samples 2

76

Beijing

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As mentioned in the earlier analysis, the morphology is able to mediate the solar radiation values to keep them in similar range. This proves that, to create comfortable environment this range needs to be followed for the design stage. The range is different in both the samples, firstly due to the environmental factors which are site specific (Fig 4.2.16) and also due to the organisational aspects which are governed by the activity it is used for.


Sky View Factor

Mumbai

Beijing

Time 21st.June

S1 (269 m2)

S2 (250 m2)

S3 (489 m2)

S4 (270 m2)

S5 (236 m2)

S6 (264 m2)

Average percentage

S1 (386 m2)

S2 (278 m2)

S3 (386 m2)

S4 (279 m2)

S5 (250 m2)

S6 (237 m2)

Average percentage

Sky view factor for semi-public space

0.64

0.63

0.82

0.67

0.72

0.79

0.71

0.73

0.48

0.52

0.50

0.54

0.57

0.56

0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0

The values of sky view factor at public level is very similar to those of semi-public spaces. They show similar trend of

1 2 3 4 5 6 1 2 3 4 5 6 Fig 4.2.17 Sky view factor vs. samples

Mumbai Beijing

higher sky view of 0.7 in Mumbai as compared to 0.56 in Beijing (Fig 4.2.17). This is because the public spaces in Mumbai are generally surrounded by buildings of 2 to 3 floors as opposed to Beijing, where the buildings are single floored. Logics for design is similar to semi-public spaces, higher the sky view better is the quality but the threshold is 0.5.

Enclosure Value

Mumbai

Enclosure Value

Beijing

S1 (269 m2)

S2 (250 m2)

S3 (489 m2)

S4 (270 m2)

S5 (236 m2)

S6 (264 m2)

Average percentage

S1 (386 m2)

S2 (278 m2)

S3 (386 m2)

S4 (279 m2)

S5 (250 m2)

S6 (237 m2)

Average percentage

130

86

128

142

98

88

112

231

150

194

167

186

218

191

250 200 150 100

Hutongs and Chowks present different morphologies for public spaces. Enclosure is a parameter to calculate the bounding surfaces of the space. The results of the analysis cannot be compared realistically as Hutongs are narrow

50 0

1 2 3 4 5 6

1 2 3 4 5 6

Fig 4.2.18 Enclosure value vs. samples

Mumbai Beijing

streets and Chowks are squares (Fig 4.2.18). However, both the features are relevant for social life and can be applied to a single design without affecting each other. Therefore, the social spaces in the form of Hutongs and Chowks could be adopted for the design, with the requirement of the respective enclosure for the site. Analysis

77


Open Space Percentage

Built Area Covered Semi-public Area Open Semi-public Area

Mumbai Public space /Total build up area

Beijing

S1 (269 m2)

S2 (250 m2)

S3 (489 m2)

S4 (270 m2)

S5 (236 m2)

S6 (264 m2)

Average percentage

S1 (386 m2)

S2 (278 m2)

S3 (386 m2)

S4 (279 m2)

S5 (250 m2)

S6 (237 m2)

Average percentage

7%

4%

12%

7%

5%

6%

7%

13%

12%

20%

10%

9%

7%

12%

20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0

The open space ratio calculates the amount of public space per built up area. The range of the public space varies between 7 to 12 % with the odd one going up to 20% (Fig 4.2.11). The ratio shows the amount of population a public space should support. A space too small would always be crowded and thereby reduce quality. An excessively large space would not be intimate enough to generate user participation and interaction. The

1 2 3 4 5 6 1 2 3 4 5 6 Fig 4.2.19 Public space ratio vs. samples

Mumbai Beijing

public spaces are seen to support a number of activities apart from casual gatherings like small commercial activities and workshops, these aspects would have to be accommodated to make the space active. The study has given a good judgement of the amount of area required to generate such spaces.

Semi-Public & Public Space Percentages

Mumbai

Beijing The indigenous settings are completely distinct from each other in terms of their environmental and socioDensity Semi-public Public

Mumbai

Beijing

cultural aspects. At the same time they show numerous similarities in their organisation and arrangement of

250 ppl/ha 18% 9%

400 ppl/ha 18% 8%

their social spaces. The distinct character of the semi-public and public spaces provide different levels of social

Fig 4.2.20 Semi-public Spaces and Public Spaces Comparison

78

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activities. The interesting fact being that, although they are two completely different locations, the area dedicated for their public and semi-public spaces are very similar (Fig 4.2.12). The differences being that semi public spaces exist only on the ground in Beijing, whereas in Mumbai they are evenly distributed at higher levels as well.


Summary Sheet Parameters

Shaded Area Proportion

Analysis (Regional)

Extracted Data

Time: 21st.June

Mumbai

Beijing

Average percentage covered from 12:00-14:00

18%

13%

A minimum of 15-20% of the public space needs to be shaded for at least 2 hours during the afternoon (12:00 hr to 14:00 hr ) every day.

Depending on the location, the values for solar incident Incident Solar Radiation

Time: May-Sept

Mumbai

Beijing

Incident Solar Radiation ( Wh/m2)

380

293

radiation on the public spaces have to be compared to the values seen in the indigenous settlements. This aspect would be essential to create a comfortable environment.

A minimum of 0.5 (50%) sky view threshold has been Sky View Factor

Sky view factor for semipublic space

Mumbai

Beijing

set for the design as seen from the indigenous settings.

0.71

0.56

The criteria would be set as higher the sky view better

Mumbai

Beijing

Enclosure values can be used to design the tertiary

112

191

streets and public spaces with respect to the specific

Mumbai

Beijing

For the urban context the public space would be set at

7%

12%

the quality.

Morphologies are completely distinct for each site. Enclosure Value

Open Space Ratio

Enclosure Value

Public space /Total build up area

location.

approximately 10% of the built area.

The percentage of semi-public and public space serve Semi-Public & Public Space Percentages

Density Semi-public Public

Mumbai

Beijing

as reference for the design. The attempts would be

250 ppl/ha 18% 9%

400 ppl/ha 18% 8%

extremely difficult as in the urban situation due to the

to achieve these numbers, however this would be need to accommodate the density the open spaces may get sacrificed to some extent.

Analysis

79


80

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4.3 Conclusion The study of the semi-public and public spaces in the two different

and features of Chowk can be applied to central or common open

locations helped derive the parameters to be used in the design

spaces. The third kind was related to the environmental aspects

logic. It also streamlined the use of parameters of semi-public

and was completely site specific, therefore can be applied only

spaces at block level and public spaces at cluster level.

when the site was chosen.

Three kinds of parametric data were derived from the study: First

A few of the parameters which were extracted from the case

kind of data was common in both case studies like the percentage

studies could not be used for the generative process directly. To

of public and semi-public space. The second kind was completely

simplify this information, a few experiments will be conducted to

distinct but can be applied to two different aspects of design without

derive morphological implications that can be used for the block

affecting each other and thereby improving quality of both. For

generation process. These aspect will be discussed in detail in the

example, features of Hutongs can be applied to tertiary level streets

following chapter. Analysis

81


82

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5

BLOCK CATALOGUE GENERATION This chapter focusses on the building block level of design and explores the possibilities of integrating high quality semi-public spaces at multiple levels within the built form. The chapter illustrates the development of a design logic for the blocks which is informed by a number of different experiments and studies. The goal is to create a system that would yield differentiated blocks by varying parametric weightage of criteria. A catalogue of fit individuals would be created that show variation in terms of quality and density. 5.1 Design Inputs

84

5.2 Experiments

86

5.3 Block Generation

89

5.4 Block Level Catalogue

96

5.5 Conclusion

99

Block Catalogue Generation

83


5.1 Design Inputs Indigenous Settlement

Design Input

Urban Infrastructure

Extracted From Case Studies

Extracted From Case Studies

Block Footprint

Design Logic

Aggregation and Dimension

Indigenous Settlement

Semi-public Space Size

Connectivity With Street

Plot Size

Block Footprint Floor Overhang

Floor Overhang

Semi-public Space Size

Mumbai Beijing Average area of semi-public space

Design Input

50 m2

70 m2

Fig 5.1.1 Semi-public sizes adopted from indigenous settlements

For the design process, certain preliminary data was required in terms of sizes of semi-public spaces and the sizes of plots. The sizes of semi-public spaces have been adopted from the case studies which shows an average of 50 m2 (Fig 5.1.1). The initial dimension is taken as 8m x 8m which can be modified during the design process. According to this, to start with each semi-public would be 64 m2, this will be used as an input for the design logic to be applied on different floors within the block.

It was observed that as the block grows higher, the build-able area available on the subsequent floors reduces drastically. As a counter measure overhang logic would be used where the building is allowed to project over the empty space within the plot by 2m to 4m. The primary result of this logic is that it creates additional buildable area and thereby increases the capacity of the building to accommodate higher built up area. It also creates shaded spaces, which could serve a number of different informal functions like small markets, sit outs spaces etc., this would add spatial variation in the streetscape.

Plot Size

Footprint 32

20 Small Plot

Medium Plot

40

Large Plot Fig 5.1.2 Three different footprint sizes

84

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To induce certain shade pocket spaces along the network, the

The plot areas are defined depending on the number of semi-public

footprint area would be allowed certain flexibilities. The footprint

spaces and the proportional built up area that can be supported.

in the medium and large plots would be in the range of 60% to 90%.

The plot sizes are generated considering 1, 2 and 3 semi-public

This with the overhang logic would be used to create canopied

spaces and the plot area required to support the corresponding

spaces which eventually would become part of the pedestrian

built up area. Plot sizes of 24m x 24 m, 32 m x 32 m and 40 m x

network during the aggregation stage.

40m will be used in the design process.


5.2 Experiments Indigenous Settlement

Experiments

Design Input

Experiment 1

Floor Offset Logic

Experiment 2

Semi-public Space Location

Extracted From Case Studies

Sky view Factor

Floor Offset Logic

Semi-public Space Proximity

FAR : 0.87 EV : 1.75 SVF : 0.52

Fig 5.2.1 The upper block is placed exactly over the lower block

Furthest Point

2-4 M

2-4 M FAR : 0.91 EV : 1.38 SVF : 0.54

Fig 5.2.2 The upper block was offset by 2-4m from the lower block

Random Point

SVF : 0.36

FAR : 1.35 EV : 1.17 SVF : 0.63

SVF : 0.69

Closest Point Examples from the iterations showing the difference in SVF

Examples of from space proximity experiment

Experiment 1 Floor Offset Logic

Experiment 2 Semi-public Space Proximity

The aim of the experiment was to develop a relationship between

As the building ascends, the semi-public spaces on higher floors

the built morphology and sky view factor parameter. The idea was

cause the buildable area to reduce. This occurs primarily due to

to create a simple geometrical rule that informs the system such

the Floor Offset logic implemented in the previous experiment.

that, the minimum quality remains preserved while increasing

Therefore, it was important to investigate and experiment with

the density of the block. The simple strategy of offsetting the built

the location of semi-public spaces at upper levels. The aim of the

form on a higher level was investigated and the subsequent change

experiment is to develop a logic that could decide the location of

in sky view factor was noted. Iterations with offset logic were able to

the subsequent semi-public space in order to increase the density

create many more options for the surrounding built morphology.

of the block. Three different conditions were experimented: at

A direct relationship between quality and the amount of offset was

the nearest possible point, at the furthest possible point and at a

observed. A range of offset (2m - 4m) was required per floor to

randomly selected point. The results showed that the block was

maintain the same spatial quality. This would bring some diversity

able to generate higher built up area when the semi-public spaces

on the created morphologies and have a drastic impact on density

were located at the closest point on the subsequent floor. However,

(Fig 5.2.1, Fig 5.2.2).To maintain minimum quality while having a

this would create a specific terraced building typology. Therefore,

high density, the range of offset was adopted between 2m to 4m on

in order to have variation in blocks, other conditions were also

the succeeding floor.

allowed but with lower weightage.

Logic: The range of floor offset from semi-public space

Logic: Most of the semi-public spaces at higher levels

would be between 2m to 4m on the succeeding floor.

would be located near the closest available positions. Block Catalogue Generation

85


5.2 Experiments Indigenous Settlement

Experiments

Design Input

Experiment 3

Semi-public Space Distribution

Extracted From Case Studies

Sky view Factor

Semi-public Space Distribution

1

2

3

4

32

FAR : 2.3 SVF : 0.81

FAR : 2.11 SVF : 0.84

FAR : 1.67 SVF : 0.86

FAR : 1.17 SVF : 0.85

FAR : 2.07 SVF : 0.70

FAR : 1.86 SVF : 0.72

FAR : 2.08 SVF : 0.81

FAR : 1.65 SVF : 0.83

40

Fig 5.2.3 Flexible Number Of Semi-public Space/ Floor

Experiment 3 Semi-public Space Distribution The experiment is to find the most efficient distribution of semi-

programmed to generate the best possible densities.

public spaces in the building in terms of number, location and arrangement. The logic tries to achieve the highest density in

The results show that when two semi-public spaces are placed at

each size of plot. Once the semi-public space gets located, it

the ground level for the large plot, a much higher density could

generates the proportional built up area around it. The iterations

be achieved. This would be the same with the medium plot when

of different morphologies are checked for both the sky view factor

only one semi-public space is used as a starting point. These results

and enclosure as quality criteria and FAR as density criteria. The

have also contribute to the design logic where the footprint could

first set of experiments are tested with fixed number of semi-

be varied between 70% - 100%.

public spaces per floor with 1,2 and 3 starting points. The second set of experiments are tested with variable number of semi-public spaces per floor (Fig 5.2.3). These tests are carried out on different plot sizes with all possible options of starting points. They are 86

SynchroniCity

Logic : To generate a higher block density, variable number of semi-public spaces per floor would be allowed.


Experiments

Design Input

Experiment 5

Connectivity Logic

Connectivity Logic

Rule 1 : Connecting Semi-public Spaces

12 m Fig 5.2.4 Linking Adjacent Semi-public Spaces

Fig 5.2.5 Linking Semi-public Spaces Close to Boundary

Before Optimising CL : 73 m

After Optimising CL : 44 m

Before Optimising CL : 77 m

After Optimising CL : 58 m

Fig 5.2.6 Blocks with Longest Circulation Length > 60m

Fig 5.2.7 Blocks with Longest Circulation Length < 60m

Experiment 4 Connectivity Logic Connectivity logic deďŹ nes how the various semi-public spaces

the shortest route that connects the top most semi-public space

would be interconnected within the block. This aspect was derived

to ground.

from the case studies where the semi-public spaces are part of the network and are located at the entrances of the houses. This aspect

In accordance with this logic, a ďŹ tness criterion is derived where

would be adopted in the design of the block.

the connection from the top most semi-public space to the ground level has to be the shortest possible length i.e. below 60m (Fig

The logic of connection is based on the location and proximity of

5.2.7). This number is based on 1 or 2 minute walking distance. This

semi-public spaces from each other as well as the ground level. Two

ensures that the circulation length is optimized and the spaces are

semi-public spaces could be connected if the distance between the

well connected and easily accessible.

centroids is in the range of 12m (Fig 5.2.4). If they are on the same face within the range of 20m they get connected by a ramp or a staircase externally (Fig 5.2.5). These connections need to form Block Catalogue Generation

87


5.3 Block Generation Logic Urban Infrastructure

Indigenous Settlement

Experiments

Extracted From Case Studies

Block Level

Design Input

Design Logic

Aggregation and Dimension

Semi-public Space Size

64 m2

Plot Size

Block Footprint

Connectivity With Street

Floor Overhang

Parameters

Sky View Factor

Experiment 1

Floor OďŹ&#x20AC;set Logic

Experiment 2

Semi-public Space Location

Experiment 3

Semi-public Space Distribution

Floor OďŹ&#x20AC;set Logic

Semi-public Space Proximity

Semi-public Space Distribution

Design Logic

Fitness Criteria

Orientation Parameters

South Facing Surface Ratio

South Facing Surface Ratio

Open Space Ratio

Open Space Ratio

Q Quality

Enclosure Value

Enclosure Value

Experiment 5

Connectivity Logic

Connectivity Logic

Density Requirement

Density Requirement

Evaluation Parameters

Sky view Factor

Sky view Factor SVF value > 0.5

Architectural aspects

88

SynchroniCity

D Density


Block Generation Design Input Plot Size Medium

Large

40 x 40 m 1600 m2

32 x 32 m 1024 m2

Three different sizes of plots would be the base area for the block generation.

Small

24 x 24 m 576 m2

Multiple fitness criteria are used to generate the block. It would be necessary to test the fitness criteria with differential weighting in order to achieve optimum result.

Fitness Criteria Q Quality

Genetic Algorithm

Typology 1

Typology 2

8 Blocks

Q

D Density

Typology 3

8 Blocks

Q

D

80% 20%

8 Blocks

Q

D

50% 50%

D

20% 80%

Evaluation 1

The sky view factor evaluation will eliminate any block with SVF value below 0.5.

Evaluation 2

This evaluation criteria takes into account modifications to blocks at higher design levels, criteria are therefore based on this aspect.

Sky View Factor

Architectural aspects

Typology 2

Typology 1 4 Blocks

Typology 3

4 Blocks

4 Blocks

Block Generation Catalogue Large

Plot Size Medium

Small

Typology 2

4

4

4

Typology 3

4

4

4

Typology 1

4

Based on the fitness criteria of quality and density and different weighting strategies, three different typologies would generated. 8 fittest individuals will be selected from the GA generation for evaluation.

4

At the end of generation process, there would be 4 fittest individuals selected for each plot size

The block catalogue would constitute 36 fittest individuals with variable density and quality.

4

Block Generation Logic Plot Aggregation Cluster Generation

The generation process draws inputs from three sources: the urban infrastructure, the case studies and experiments. Information like plot sizes, offset logic, semi-public space distribution are fed into an evolutionary solver as inputs to generate the possible options of morphologies. These morphologies will be optimised by the selected quality and density factors. The aspects that are difficult to compute computationally are set as evaluation criteria. The aim will be to create a catalogue of three different typologies of blocks with different weighting on quality and density factors. Block Catalogue Generation

89


5.3 Block Generation Fitness Criteria Urban Infrastructure

Experiments

Indigenous Settlement

Fitness Criteria

Extracted From Case Studies

South Facing Surface Ratio

South Facing Surface Ratio

Open Space Ratio

Open Space Ratio

Enclosure Value

Experiment 4

Enclosure Value

Experiment 5

Connectivity Logic

Enclosure Study

Connectivity Logic

Density Requirement

Density Requirement

< 1.0

1.0

3.0

2.5

Q Quality

D Density

> 3.0 Original Domain

FAR

0 < 10%

0.75

10%

14%

Target Domain

1 20%

> 20%

OSR

0 > 80 m

1

0.4

80 m

Large Plot Typology 2

30 m 20 m < 30 m

Q

D

50%

50%

CL

0 > 16%

1

16%

8%

11%

< 8%

0

1

0.38

Score

2.5

0.75

OSR

14%

0.40

CL

20 m

1.00

Remapping Parameters 2.1

1.1

Value

FAR

Floor Area Ratio (FAR) Open Space Ratio(OSR) Circulation Length (CL) SFR 11% 0.38 South Facing Surface Ratio (SFR) EV 1.1 0.52 Enclosure Value (EV) Fig 5.3.2 Scores of Each Spatial Parameter

SFR

0

Fitness Criteria

3

Parameters addressed are either density or dwelling qualities, where they are remapped into identical [0, 1] domain (Fig 5.3.1). DiďŹ&#x20AC;erentiated strategies are used to remap, the lower limit was always considered as the minimum score while the upper limit

EV

maximum. By introducing the remap strategy, various criteria could be compared with diďŹ&#x20AC;erentiated magnitudes and units via a 0

0.52

1

Fig 5.3.1 Parameters after remapped into a singular numerical domain

90

SynchroniCity

numeric value illustrating discrepancy between the achieved score and the target score (Fig 5.3.2).


OSR = 14% CL = 20M SFR = 11 EV = 1.1

Threshold Value

0%

VD/VQ 0

20%

100%

Toggle Value

1

Fig 5.3.3 Adopted Boolean Remapping Strategy

0.75

Amplify x 5

3.75

FAR Genetic Algorithm 0.40

Amplify x 2

0.80

Q

D

Quality

Density

OSR

OSR

FAR

CL 1.00

Amplify x 1

SFR

1.00

CL

0.38

EV

Amplify x 1

0.38

Amplify x 1

0.52

Different criteria categorised base on quality and density

SFR

0.52 EV

Q

D

= FAR x 5 x VD + OSR x 2 + CL x 1 + SFR x 1 + EV x 1 x VQ

50% 50% = 0.75 x 5 x

1

+ 0.40 x 2 + 1.00 x 1 + 0.38 x 1 + 0.52 x 1 x

1

Fig 5.3.4 Mathematical Expression of Fitness Value

Differential Weighting Differential weightings are given to each parameter. In a block

mathematical expression:

consisting of equal weighting on quality and density, the remapped FAR score is amplified by 5 while OSR, CL, SFR and EV are

Total Score (QD) = ((FAR*5) + (OSR*2) + (CL*1) + (SFR*1) +

multiplied by 2, 1, 1 and 1 respectively (Fig 5.3.4), so that the total

(EV*1))* VQ * VD

score of the density and spatial qualities is identical. Meanwhile toggle values (VQ/ VD) are applied to ensure at least 20% of the

Differential amplified factors are given to quality, quality-density

upper limit values are achieved (Fig 5.3.3).

and density blocks. The expression is linked to an Evolutionary

The strategy mentioned above can be concluded with a

solver as the fitness value to maximise. Block Catalogue Generation

91


CL = 20M SFR = 11 EV = 1.1

5.3 Block Generation Differential Weighting D

Quality

Density

OSR

FAR

CL SFR

Typology 1 Original Weighting

Q

EV

Typology 2

Q

= FAR x 3 x

1

+ OSR x 2.8 + CL x 1.4 + SFR x 1.4 + EV x 1.4 x

1

QD = FAR x 5 x

1

+ OSR x 2 + CL x 1 + SFR x 1 + EV x 1 x

1

D

= FAR x 7 x

1

+ OSR x 1.2 + CL x 0.6 +SFR x 0.6+ EV x 0.6 x

1

Q

= FAR x 2 x

1

+ OSR x 3.2 + CL x 1.6 + SFR x 1.6 + EV x 1.6 x

1

QD = FAR x 5 x

1

+ OSR x 2 + CL x 1 + SFR x 1 + EV x 1 x

1

= FAR x 8 x

1

+ OSR x 0.8 + CL x 0.4 +SFR x 0.4+ EV x 0.4 x

1

Q

D

70% 30%

Q

D

50% 50%

Q

Typology 3

D

30% 70%

Floor Area Ratio (FAR) Open Space Ratio(OSR) Circulation Length (CL) South Facing Surface Ratio (SFR) Enclosure Value (EV)

Modified Weighting

Typology 1

Typology 2

Q

D

80% 20%

Q

D

50% 50%

Q

Typology 3

D

D

20% 80%

Fig 5.3.5 Differentiated Weightages for Q/QD/D Blocks

Original OSR Range

Open Space Ratio (OSR) Semi-public Space 64 m2

Total Built Area

OSR = 10%

640 m2

8m 4m

8m

4 m x 40 Units

OSR = 9% - 11% 64 m2

OSR = 9%

The aim of the process is to create three typologies of blocks generations are experimented to calibrate the system to yield

Modified OSR Range

8m 4m

8m 64 m2

OSR = 10%

4m

8m

OSR = 11%

4m

x 44 Units

typologies that are considerably different. To achieve this, differential weighting is applied to the parameters in order to generate three different typologies (Fig 5.3.5): first typology having higher weighting on quality, second with equal weighting

640 m2

8m

64 m2

with different quality and density attributes. A number of test

710 m

2

4m

and third with higher weighting on density. Different weighting x 40 Units

strategies are were attempted, a few examples are shown in Fig 5.3.6. Fitness criteria were amplified to different amounts based on the differential weightings strategies of each typology. To increase

582 m2

the difference input parameters were also modified to give more flexibility in terms of the open space ratio (Fig 5.3.5). The result

8m 8m Fig 5.3.5 Modification on OSR Value 92

SynchroniCity

4m

4m

x 36 Units

shows difference in both FAR value and the building morphologies. The adopted weighting would be : Quality preferred blocks : (Q:80 D:20), Equally weighted blocks : (Q:50 D:50), Density preferred blocks : (Q:20 D:80).


Q

D

60% 40%

Q

D

70% 30%

Q

D

80% 20%

Typology 1 Quality option

Average FAR:

1.29

1.70

1.62

Highest FAR:

1.35

2.01

1.83

Q

D

50% 50%

Q

D

50% 50%

Q

D

50% 50%

Typology 2 Quality-Density option

Average FAR:

1.42

1.85

2.05

Highest FAR:

1.49

2.02

2.28

Q

D

40% 60%

Q

D

30% 70%

Q

D

20% 80%

Typology 3 Density option

Average FAR:

1.37

1.97

2.99

Highest FAR:

1.50

2.00

3.29

Fig 5.3.6 Differentiation of Q/QD/D Blocks after Modification Block Catalogue Generation

93


5.3 Block Generation Evaluation

0.96

0.91

Evaluation 1 Sky View Factor The aim of the evaluation is to measure the fitness level of the generation and short-list the best ones for further research. Sky view factor is chosen for the first part of the evaluation. This was

0.76

because it was computationally difficult to analyse all the semi-

0.80

public spaces for sky-view and other parameters simultaneously. For evaluation, each of the semi-public space in the block is

0.45 < 0.5

checked with for sky view factor and the lowest value was taken into consideration. If any one of the value is less than 0.5, that individual was eliminated. This would ensure that all the semielevated corridor network

extension capacity public spaces always have some minimum quality.

Based on this SVF, the individual would be eliminated Fig 5.3.7 SVF Values of Each Semi-public Space

Evaluation 2 Architectural Aspects

elevated corridor network

extension capacity

The second evaluation criteria is analysed based on the architectural aspects. This evaluation would help the design at the cluster level that deals with block aggregation. The blocks are evaluated on four aspects which allow small modifications or additions to improve the blocks performance at the cluster level.

1

Elevated network

2

Informal path-way

The first criterion is the ability of the block to generate elevated network through the corridor like spaces. Second is the ability to create intra-building informal passageway that connect with different blocks. The blocks also should have extension capacity by performing simple extrusions from flat surfaces. Lastly the block

elevated corridor network

should have capacity to connect to other blocks to create inter-

extension capacity

informal passway

building passageways. interconnection capacity

All the individuals are evaluated and if any block has two or more of these aspects, that would be selected for the block catalogue.

3 Extension capacity Fig 5.3.8 Emergent Architectural Characteristics as Evaluation Criterion 94

SynchroniCity

extension capacity

4 Interconnection capacity


Evaluation Process Evaluation 1

Sky View Factor L_D_01 elimination of blocks not satisfying the sky view factor

Evaluation 2

Architectural Aspects L_D_01

Selected Individuals

elimination of blocks not satisfying 2 of the architectural considerations

SELECTED EVALUATION CHART FOR LARGE PLOTS (40 M X 40 M)

Large Plot Typology 1 (Q) Typology 2 (QD) Typology 3 (D)

INDIVIDUALS

Evaluation Criteria 1 Sky View Factor Sky View Factor Sky View Factor

Generated Block Individuals

Average

L_Q_01

L_Q_02

L_Q_03

L_Q_04

L_Q_05

L_Q_06

L_Q_07

L_Q_08

0.59

0.53

0.53

0.61

0.59

0.72

0.59

0.60

L_QD_01

L_QD_02

L_QD_03

L_QD_04

L_QD_05

L_QD_06

L_QD_07

L_QD_08

0.69

0.65

0.52

0.62

0.48

0.65

0.68

0.70

L_D_01

L_D_02

L_D_03

L_D_04

L_D_05

L_D_06

L_D_07

L_D_08

0.54

0.48

0.53

0.50

0.38

0.42

0.42

0.51

0.60 0.62 0.47

L_Q_01

L_QD_06

L_Q_03

L_QD_07

L_Q_05

L_D_01

L_Q_07

L_D_03

L_QD_03

L_D_04

L_QD_04

L_D_08

M_Q_01

M_QD_06

M_Q_02

M_QD_07

M_Q_03

M_D_05

M_Q_05

M_D_06

M_QD_01

M_D_07

M_QD_04

M_D_08

S_Q_01

S_QD_06

S_Q_02

S_QD_08

S_Q_04

S_D_02

S_Q_06

S_D_06

S_QD_01

S_D_07

S_QD_05

S_D_08

EVALUATION CHART FOR MEDIUM PLOTS (32 M X 32 M)

Medium Plot Typology 1 (Q) Typology 2 (QD) Typology 3 (D)

Evaluation Criteria 1 Sky View Factor Sky View Factor Sky View Factor

Generated Block Individuals

Average

M_Q_01

M_Q_02

M_Q_03

M_Q_04

M_Q_05

M_Q_06

M_Q_07

M_Q_08

0.76

0.63

0.72

0.61

0.63

0.67

0.74

0.74

M_QD_01

M_QD_02

M_QD_03

M_QD_04

M_QD_05

M_QD_06

M_QD_07

M_QD_08

0.64

0.74

0.71

0.63

0.68

0.74

0.67

0.66

M_D_01

M_D_02

M_D_03

M_D_04

M_D_05

M_D_06

M_D_07

M_D_08

0.45

0.43

0.43

0.47

0.74

0.66

0.71

0.66

S_Q_01

S_Q_02

S_Q_03

S_Q_04

S_Q_05

S_Q_06

S_Q_07

S_Q_08

0.83

0.82

0.87

0.79

0.81

0.62

0.83

0.81

S_QD_01

S_QD_02

S_QD_03

S_QD_04

S_QD_05

S_QD_06

S_QD_07

S_QD_08

0.73

0.79

0.81

0.72

0.85

0.81

0.77

0.63

S_D_01

S_D_02

S_D_03

S_D_04

S_D_05

S_D_06

S_D_07

S_D_08

0.84

0.71

0.75

0.80

0.86

0.76

0.80

0.84

0.69 0.68 0.57

EVALUATION CHART FOR SMALL PLOTS (24 M X 24 M)

Small Plot Typology 1 (Q) Typology 2 (QD) Typology 3 (D)

Evaluation Criteria 1 Sky View Factor Sky View Factor Sky View Factor

Generated Block Individuals

Average

0.80 0.76 0.80

Block Catalogue Generation

95


40Q EV

CL

EV

CL

SFR

SFR

FAR

FAR

5.4 Block Level Catalogue SVF

OSR

EV

OSRQ SVF

SVF

SVF D

EV

CL

CL

SVF

EV

CL

L_Q_01 OSR

FAR 1.81 EV

Q SFR

D

CL 5.29

5.43

SVF

EV

SVF

EV

OSR

SVF

SVF

OSR

SVF

CL

EV

CL

EV

EV

CL

EV

SFR

OSR SVF

CL SFR

FAR

FAR

40QD

OSR

EV

EV SFR

OSR

SVF FAR

FAR

OSR SVF

SVF

CL

EV

CL EV

EV

3.95 SFR

EV

SVF

40QD

SFR

CL

CL

EV

SFR

SVF

CL EV

EV

OSR

FAR

CL EV SFR

L_Q_05 SVF FAR

FAR

EV EV

D SVF

EV

OSR

SFR

SVF FAR FAR

SVF OSR SVF OSR CL

OSR

4.97SFR

SFR

SVF

EV

SFR CL

EV

FAR

32QD OSR

SVF

OSR SVF

SFR FAR FAR SVF

SFR FAR

32D OSR

EV

CL EV

EV

CL

CL SFR FAR

SFR FAR

FAR OSR

CL

SVF

L_QD_03 FAR OSR

SVF

SVF

FAR 1.94

SVF

CL 2.81

EV

EV EV

SFR

OSR

Q SFR

D SFR

CL

9.70

40Q

EV

FAR

FAR

OSR SVF

FAR

EV

CL

SFR

SFR

FAR

FAR OSR

EV

EV

CL SFR FAR

SVF

OSR SVF

SVF FAR

OSR SVF

SVF

OSR SVF

SVF

40QD FAR 2.07 OSR

CL

EV

CL EV

EV

Q D

OSR SVF

SVF

CL

EV

SFR CL

10.35 SFR

CL EV

EV

OSRQ SVF

OSR

SVF

SFR

CL

EV

OSR SVF

OSR SVF

OSR

FAR

OSR OSR SVF SVF

32QOSR FAR 2.03 EV SFR

OSR

EV

SFR

CL

EV

EV

SFR

SFR

FAR L_D_01

FAR SVF

OSR

FAR 3.10

EV

D SFR

SFR

FAR OSR SVF

Q CL

EV

EV

SFR

OSR

OSR SVF

SVF

21.71

D Density

EV

CL SFR

OSRFAR SVF

FAR

SVF

SVF

OSR CL

SFR FAR FAR SVF

CL EV

SFR

EV

D

SFR

CL OSR

SFR SVF

CL SFR

EV

FAR

SFR FAR OSR

CL

CL EV

EV

EV

SVF

21.21 SFR

CL EV

EV SFR

CL

CL EV SFR

SFR

CL

EV SFR

OSR SVF OSR SVF OSR CL

SFR

24Q OSR FARSVF OSR OSR SVF SVF 40Q CL EV CL SFR

CL

FAR

FAR

EV

EV

FAR SVF FAR

FAR OSR

SFR FAR

FAR

FAR

32QOSR

SFR EV CL EV SFR CL

CL

EV

SVF OSR SVF OSR SVF OSR

EV

EV

SFR

SFR

24O

SVF OSR

EV CL

SVF SVF OSR OSR

CL EV CL SFR EV SFR CL FAR

SFR SVF FAR FAR SVF

24QD 40QD SVF

SVF

SFR

FAR OSR SVF FAR FAR FAR SVF

OSR

32QD OSR

SVF

CL

EV

SFR EV CL EV CL

CL EV CL EV

Q

CL

EV

EV

EV

SFR

CL

CL EV CL SFR EV EV SFR CL FAR

EV

EV 2.70

CL

Q

EV

EV CL EV CL EV CLSFR SFR FAR

D

SFR FAR

SFR

EV CL

9.90

SFR

SFR

FAR

OSR

EV

CL C SFR

EV

FAR

SVF

CL

SFR SVF FAR FAR SVF OSR

EV CL EV CL EV CLSFR

EV

EV SFR OSR FAR

FAR

SVF

EV

SFR FAR

C

FAR

24QO

SVF OSR

OSR

CL

SFR EV FAR

D

SFR FAR

32Q

EV

CL

CL SFR EV CL EV SFR CL

SVF OSR

SVF

FAR

CL

EV

EV

EV CL

SFR FAR

CL

EV

CL

CL

EV

SFR CL EV CL EV SFR

SFR FAR

FAR

SVF OSR

19.18 SFR SFR

SFR FAR

FAR

SVF OSR

SVF

CL EV

EV

CL EV CL EV

SVF

EV SFR

EV CL

FAR

FAR

SFR

CL

EV

SFR

OSR SVF SVF OSR OSR SVF FAR FAR

SFR EV

FAR

Q

CL

1.29 EV CL

21.57 CL SFR

FAR

SVF

CL

CL EV CL EV

EV SFR

FAR

OSR

SFR

SVF SVF OSR OSR

SFR EV SFR CL

3.08

SFR FAR

SFR

SVF OSR CL

SVF OSR

D SFR

OSR

SFR

EV

SFR SFR L_D_08 FAR FAR

CL EV CL EV CL EV SFR

SFR

OSR

SVF

SVF

CL EV CL EV CL EV

SFR

C

SFR CL SFR

SFR

32QD

EV

EV

40QD

CL EV CL SFR EV SFR CL EV

1.42

SVF SVF OSR OSR

SFR

O

CL SFR C SFR

EV CL EV CL EV CLSFR

SFR CL SFR

SFR

CL EV

CL

24Q FAR SVF SVF OSR SVF OSR OSR

EV CL

OSR

SVF

SFR EV SFR CL FAR

OSR EV

CL

SFR EV FAR

CL

40D

SVF OSR

EV SFR

SFR OSR FAR

32QD 32D SVF OSR SVF SVF 1.98 SVF OSR SVF OSR OSR OSR SVF OSR OSR FAR SVF FAR

EV

SVF OSR SVF SVF OSR OSR

EV

O

24D

SVF

OSR

SFR L_QD_07 SVF FAR FAR OSR FAR

FAR

EV

FAR 2.74 SVF

C SFR

FAR FAR SFR SFR SVF OSR SVF OSR OSR SVF OSR FAR FAR FAR FAR FAR FAR FAR FAR FAR FAR FAR SVF OSR OSR SVF OSR SVF SVF OSR SVF OSR O SVF OSR OSR SVF OSR OSR SVF SVF SVF SVF SVF SVF SVF OSR OSR OSR SVF OSR OSR

CL EV CL SFR EV SFR CL

SFR L_D_04 FAR FAR

C

EV

SFR

CL SFR

2

O

SVF FAR FAR

OSR FAR

SFR FAR

FAR

EV SFR

FAR

EV

EV

EV

FAR

SVF OSR SVF OSR

SVF

CL

CL

CL

SFR

OSR SVF FAR FAR FAR SVF

OSR

CL EV

CL

SFR

SFR

SFR

SVF OSR SVF OSR

FAR

24QD OSR SVF SVF SVF OSR OSR 40QD

EV CL SFR EV CL EV CL

EV CL

EV SFR

EV

CL CL EV CL EV CL EV SFR

EV

SFR

CL SFR

SFR

FAR

EV

EV

CL

SFR

CL

OSR CL

SFR

Floor Area Ratio (FAR) FAR FAR EV FAROpen Space Ratio(OSR) Circulation Length (CL) SVF Surface OSR SVF Ratio (SFR) OSR South Facing Enclosure Value (EV)

EV

EV

40Q

FAR OSR

FAR

CL

32D

EV

CL

CL

EV CL CLSFR CL EV SFR

EV

EV CL

EV

SFR SFR SFR SFR SFR SFR SFR SFR SFR FAR SFR FAR SFR FAR SFR FAR FAR FAR FAR FAR FAR FAR FAR FAR FAR SVF OSR SVF OSR OSR SVF OSR OSR FAR FARSVF SVF FAR OSR SVF OSR OSR FAR SVF SVF FAR OSR OSR SVF SVF FAR OSR OSR SVF SVF FAR OSR OSR SVF SVF FAR

CL EV CL SFR SFR EV FAR

5.43SFR SFR

EV

SFR CL EV SFR CL

SFR

SFR CL EV SFR CL

EV

1.16 CL EV

FAR

EV

SVF

CL

CL

SFR

SVF

32Q

CL

32QD 32D SVF SVF SVF OSR OSR SVF OSR OSR

40D

SVF OSR

EV

SFR

SVF OSR SVF FAR FAR

OSR

40D

EV

FAR OSR SVF

OSR

SVF 1.81

FAR

FAR SVF FAR OSR Large PlotTypology 3 (D) 40D 40QD SVF OSR

FAR

SFR

SFR

FAR 3.03

SynchroniCity EV

EV

SFR

OSR OSR SVF SVF

Q

CL EV

CL

FAR L_D_03 FAR

FAR

SVF

CL EV SFR

1.18

Q Quality

96

CL

CL

EV

CL EV CLSFR CL EV

SVF CL

SFR FAR

SVF

OSR SVF

CL

EV 5.48

D

OSR

10.15SFR

SFR FAR

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CL

EV

EV

FAR OSR 1.51 FAR

Q

4.17

SVF

D

7.55 CL

CL

CL EV

SFR

SFR FAR SVF OSR

EV

SFR

FAR

SFR

CL

OSR

SVF OSR

OSR

EV

CL SFR FAR

OSR

CL

EV

FAR

SVF OSR

SVF OSR

CL

24D

EV

OSR

CL SFR

SFR

FAR OSR

SVF

OSR

SVF

CL

EV

CL

EV

FAR

OSR OSR CL

SFR

CL

SFR FAR

SFR FAR

FAR

SVF

SFR

FAR

SVF

SFR

OSR

CL

SFR

24QD

FAR SVF OSR

CL EV

CL

FAR

OSR SVF OSR

CL

FAR

EV

FAR FAR

OSR

OSR

SFR

24Q

EV

EV

CL

OSR

SVF OSR

SFR

SVF

SFR

EV

FAR

SVF OSR OSR SVF FAR SVF

EV

FAR

SFR

SFR

SFR

OSR

OSR SVF OSR

CL

CL

SVF SVF

EV

SFR FAR

OSR

24QFAR OSR

OSR

SVF

OSR

EV

SFR SFR

SVF FAR

CL

EV SFR SFR FAR

OSR

SVF OSR

CL

CL

EV

SVF

CL

EV

SFR

SFR

FAR

FAR 24QD OSR

SVF

EV

24D

CL SFR FAR

FAR

SVF OSR

OSR

CL SFR

CL

EV

OSR

SVF

FAR OSR 1.82 FAR

SVF

OSR

SVF

Q

2.30

D SFR

12.74 CL

CL

SVF OSR

CL

OSR

CL SFR

EV

FAR

EV

FAR

OSR OSR

EV

CL SFR

SVF

OSR

CL SFR

CL

SVF

OSR

EV

CL SFR

EV

CL SFR

SFR FAR OSR

EV

CL SFR FAR

SFR FAR

FAR

EV

CL

EV

CL EV

SFR CL

SVF

SFR

EV

SVF OSR

OSR

FAR

EV

SFR SFR FAR S_D_08

OSR

SVF

CL EV

CL

SFR FAR

2.52

D 14.49EV SFR CL

OSR SVF OSR

CL

CL CL

FAR

OSR OSR 2.07SVF FAR

CL

EV SFR

EV EV

SFR S_D_07 FAR

SFR

CL

FAR

CL

SFR

CL

SFR

FAR

CL EV CL EV

CL SFR

SFR FAR

Q

SFR FAR

CL EV

EV

CL EV CL EV

CL

SFR SFR FAR

CL

SFR

OSR SVF

OSR

SFR

CL

EV

Circulation Length (CL) OSR OSR SVF SVF OSR SVF SVF SVF Ratio (SFR) OSR OSR Surface South Facing Enclosure Value (EV)

SFR

CL EV

SFR SFR S_QD_08 FAR FAR OSR

CL EV CL EV

CL EV EV

OSR

EV

FAR

CL EV CL EV

SFR CLSFR

FAROpen Space Ratio(OSR) EV

CL

CL

EV

SVF

SFR FAR

3.95

SFR

OSR SVF OSR CL

CL

FAR

OSR CL

FAR

FAR

SVF

D CL 9.72 EV

EV

32D

FAR OSR

CL EV CL EV

EV

OSR SVF OSR SVF OSR SVF

SFR CL

SVF

2.03

CL

SFR

CL SFR

SFR FAR

CL EV

SFR FAR

FAR

SVF FAR SVF OSR OSR1.82 FAR

Q

SFR

SVF FAR 32QD OSR

S_D_06 FAR

OSR

SVF SVF OSR OSR

SFR

SFR CL

OSR

SVF

CL

CL

OSR SVF

CL

CL

SVF OSR FAR SVF SVF OSR OSR

SFR EV

SVF OSR

EV

OSR

FAR

SFR

CL

SFR FAR

OSR

SVF

FAR

FAR

CL CL

EV SFR

SFR FAR

OSR

SVF CL EV

CL EV CL EV SFR

OSR FAR SVF FAR

CL EV CL EV CL EV

EV CL

EV

SFR FAR

32Q

SVF OSR CL

1.76

OSR D Density

SFR

FAR

EV CL SFR SFR SFR CL

EV

Quality

SVF OSR

SFR FAR

EV

CL CL

EV

FAR

32D

SFR S_QD_06 FAR FAR

Q

CL EV CL EV

CL

OSR SVF

FAR

CL SFR

SFR

Typology 3 (D) 24D 24Q Small Plot-24QD OSR

EV

EV CL CLSFR CL EV SFR

SVF

CL

CL

CL

SFR

24QD

CL

FAR

CL EV CL EV

FAR OSR SVF

24DOSRSVF OSRSVF FARSVF FAROSR1.34 FAR SVF OSR OSR SVF

FAR

32D

EV

CL

EV SFR

OSR OSR SVF OSR

CL

EV

EV

SFR FAR

EV

EV

FAR

OSR

SFR

CL EV

CL

OSR

SVF

9.10 EV SFR

FAR FAR SVF FAR

32QD

EV CL

SFR

SFR

CL EV

OSR

EV

CL CL

EV SFR

OSR OSR SVF SVF OSR SVF

OSR

SFR FAR

24QD1.82 Q

SFR

CL

EV

CL CL EV CL EV SFR

SFR FAR

FAR FAR OSR SVF FAR

EV

S_QD_05 FAR FAR

OSR

CL

EV SFR FAR

SFR

FAR SVF OSR SVF OSR SVF SVF OSR FAROSR

EV CL EV CL EV CLSFR

EV CL

SFR

SVF

CL EV

CL

CL

24Q

SFR

OSR

Small Plot- Typology 2 (QD) 24Q 24QD

EV EV

32Q

EV CL SFR SFR CL SFR

EV

FAR

OSR SVF

SVF OSR

EV

EV

FAR OSR

SVF

CL EV

CL

CL

SVF

FAR

FAR

32D

FAR

FAR OSR SVF

5.71

Q CL EV CL EV CL EV

FAR

SVF

24QD 24D SVF OSR SVF OSR OSR SVF SVF FAR SVF OSR OSR OSR 1.04

EVCL

SFR

FAR

OSR

SFR

SFR

24D

CL

FAR

32Q

CL

EV CL EV CL EV CLSFR

EV

EV

FAR

EV SFR SFR CL FAR

40D FAR OSR

FAR SVF OSR SVF OSR FAR SVF OSR

SFR

D SFR

SFR

SFR

FAR

CL

SFR SVF FAR FAR

EV

CL EV CL EV CL EV

OSR

SVF

EV

SFR

SFR FAR S_D_02 FAR

FAR

SFR CLSFR

SFR

CL

CL EV

CL

EV CL

5.74

Q

EVCL

SFR

FAR

FAR OSR OSR SVF SVF

24QD

SFR

SVFOSR

SVFOSR SVF OSR SVF SVF OSR OSR FAR

EV

32QD

OSR SVF

SVF OSR

OSR

EV CL EV CL EV

SFR FAR

FAR

FAR OSR

SVF OSR

SVF

EV

CL

EV

FAR OSR

24Q1.04

SVF

EV

FAR

OSR

EV

CL SFR

SFR SFR SFR SFR FAR FAR OSR FAR FAR FAR SVF OSR FARQ FAR D FAR FAR FAR FAR FAR SVF OSR SVF OSR SVF OSR SVF OSR OSR SVF SVF OSR OSR SVF OSR OSR SVF OSR OSR SVF 20% 80% SVF SVF SVF SVF SVF OSR OSR OSR SVF OSR SVF OSR SVF OSR

SFR FAR

EV

EV

EV CLSFR CL EV SFR

EVOSR

SFR

SFR EV CL EV SFR CL

SVF

CL

CL

CL

SFR

OSR

SVF CL EV

SFR EV

SFR FAR

CL

SVF FAR 40QD OSR

OSR SVF

EV

8.80

D EV

EV

EV SFR

FAR

CL

EV CL EV CL EV CLSFR SFR FAR

CL

CL

FAR

FAR

OSR

CL 3.50

Q

SVF EV

CL EV

S_Q_02 FAR SVF FAR FAR

EV

SFR

CL

40D

32QD

SFR FAR

EV

SVF

32QOSR

CL

CL

SFR

SFR S_QD_01OSR FAR FAR

EV CL SFR

SFR FAR

EV

EV

CL EV

CL

OSR SVF FAR FAR FAR SVF

SVF SVF SR OSR

FAR OSR

OSR

OSR SVF OSR SVF

EV

SFR

FAR

FAR

24D

CL

CL SFR

SFR

24QD OSR 24D OSR FAR FAR SVF SVF SVF SVF FAR OSR OSR 40QD OSR SVF SVF40D FAR OSR

EV

SFR

EV

32D OSRSVF FAR SVF OSR 1.76 FAROSR SVF

EV CL EV CL EV FR

SFR

SFR

SVF OSR

CL

SFR FAR

CL

FAR

SFR SVF FAR FAR

SVF OSR SVF SVF OSR SR

FAR

EV

EV

FAR

SFR

SFR

FAR

32Q

EV CL SFR CL EV

EV SFR SFR CL FAR

SFR FAR OSR

SFR

EV

OSR Plot- TypologyOSR Small 1 (Q)SVF 24Q 32D

32QD 80% 20%

CL

FAR

FAR OSR SVF

FAR FAR FAR FAR FAR FAR FAR FAR FAR FAR FAR OSR SVF OSR SVF FAR OSR Q D FAR FAR FAR FAR SVF OSR SVF OSR SVF OSR SVF OSR SVF SVF 50% 50% OSR OSR SVF SVF SVF SVF OSR OSR SVF SVF OSR OSR SVF SVF OSR OSR SVF SVF OSR OSR OSR SVF OSR SVF OSR SVF OSR SVF OSR SVF OSR OSR

EV

EV

EV

CL

SVF OSR OSR SVF SVF OSR

SVF CL

CL

SVF D

SFR

FAR

FAR

CL EV

OSR Q SVF

CL

24QD OSR SVF SVF SVF OSR OSR 40QD

EV CL EV CL EV CLSFR

EVCL

FAR OSR

SFR FAR

SR

SFR EV

FAR

EV SFR

FAR

CL SFR

EV

FAR

FAR OSR

SFR

EV

3.02 SFR

CL SFR

SFR

CL EV

EV

FAR SVF FAR

CL 4.85

D SFR

EV

EV

FAR

R

S_Q_01 FAR FAR OSR

SFR

40D

EV

SVF

SFR

EV CL EV CL EV CLSFR

SVF CL

EV

FAR

Q

EV

SVF OSR FAR FAR SVF

CL

CL

FAR

FAR

SVF

32Q

CL

CL SFR

CL EV CL SFR

FAR

SVF OSR

EV

OSR SVF

32QD 32D SVF OSR SVF OSR OSR SVF OSR OSR SVF SVF SVF OSR SVF OSR 1.01 FAR

SFR

OSR

EV

24Q OSR SVF OSR OSR SVF 40Q

EV

SFR FAR

CL

FAR

FAR SVF FAR

OSR

SFR CL EV CL EV

R

SFR

SFR

FAR

R

FAR FAR SVF

OSR

40D

SFR

FAR

FAR

24D

CL

SFR

FAR

SVF

SFR

SFR

OSR OSR SVF SVF

OSR SVF

CL

SFR

FAR

FAR

24QD

EV

CL

SFR

OSR

40QD

24Q

EV

CL

FAR

FAR

32D

EV

SFR

FAR

D

32QD


5.5 Conclusion Comparative Analysis

Parameters

Ranwar Village

Plot Area Total Built Up Area (m2)

Q

Q

Medium Plot

Typology 1

D Typology 2

D Typology 3

314

1600

1600

572

7798

50

Q

Q

Small Plot

Typology 1

D Typology 2

D Typology 3

1600

1024

1024

9849

14364

5609

1248

1379

1724

0.61

1.62

2.05

25%

16%

15%

9%

Semi-public Space Area (m2) FAR

Large Plot

Q

Q

Typology 1

D Typology 2

D Typology 3

1024

576

576

576

5965

6570

2062

2762

3300

954

835

1117

619

580

726

2.99

1.83

1.94

2.14

1.19

1.60

1.91

14%

12%

17%

14%

17%

30%

21%

22%

11%

12%

11%

11%

11%

12%

11%

11%

Semi-public Space Per Built Up Ratio South Facing Surface Ratio

FAR 3.00

Semi-public Space Per Built Up Ratio 30%

2.50

25%

2.00

20%

1.50

15%

1.00

10%

0.50

5%

0

Samples

0

South Facing Surface Ratio Samples Ranwar Village T1 T2 Large Plot T3

20%

T1 T2 Medium Plot T3

10%

0

Samples

T1 T2 Small Plot T3

Samples

Fig 5.3.9 Scores of Spatial Parameters

For the comparative analysis of quality aspects for the semi-public spaces, block level morphologies are investigated. Quality criteria have been extracted from both the indigenous settings. But the design focuses over a speciďŹ c environmental condition similar to Ranwar Village in Mumbai. Thus the case study of this settlement is chosen to compare with the proposed block and cluster design. From the proposed block design, all three typologies and their sub-categories of large, medium and small plots are considered for comparison with settings in Ranwar Village. The comparison of density criteria shows that, the value of FAR in the typology 3 is highest for all the three sizes of plots. In the largest plot size, the

Ranwar Village

Proposed Block Design

value is signiďŹ cantly high at 2.99. Looking at the semi-public space per built up ratio, the small plots have the highest ratio with an average of 14% which is the closest that the system is able to achieve to the 25% in the Ranwar Village. Block Catalogue Generation

99


5.5 Conclusion Comparative Analysis

Parameters

Ranwar Village

Large Plot Q

Q

Medium Plot

Typology 1

D Typology 2

D Typology 3

Q

Q

Small Plot

Typology 1

D Typology 2

D Typology 3

Q

Q

Typology 1

D Typology 2

D Typology 3

Enclosure

1.34

1.11

1.11

1.39

1.08

1.21

1.40

0.88

0.95

1.11

Sky View Factor

0.60

0.60

0.62

0.47

0.69

0.68

0.57

0.80

0.76

0.80

25%

22%

24%

27%

20%

21%

23%

19%

21%

21%

Shaded Area Proportion

Sky View Factor 1.00

Enclosure 1.40

30%

1.20

0.80

25%

1.00 0.60

20%

0.80

10%

0.40

0.20 0

Samples

0

T1 T2 Small Plot T3

5%

0.20

Samples

0

Samples Ranwar Village T1 T2 Large Plot T3 T1 T2 Medium Plot T3

15%

0.60

0.40

Shaded Area Proportion

Samples

Fig 5.3.9 Scores of Spatial Parameters

The other quality criteria are compared with each other and only the south facing surface ratio has a overall lower value. This could be due to the square geometry of the plot which during the generation process makes it diďŹ&#x192;cult to achieve a high proportion of south facing surfaces for the built morphology. In other criteria, the result has shown that the quality in terms of the parameters that were chosen has been well maintained with signiďŹ cant increase in density. For certain other quality criteria the overall quality value in the proposed design exceeds the value found in Ranwar Village. 100

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Third Floor

Second Floor

First Floor

Ground Floor

Block Example

Exploded View of a Block

5.5 Conclusion The generation process was able to create distinct individuals

The variation in the block typologies ensured differentiation of

that prioritise different aspects of the fitness criteria. The three

spaces at every level even in the residential typologies, which is not

typologies within the catalogue showed variation in terms of plot

common in urban scenarios.

sizes, density and quality. The FAR values of individuals varied from a minimum of 1.01 to a maximum of 3.08. Understandably, the

Depending on the site requirements in terms of density and

quality decreases as the density increases. The threshold values of

quality different individuals from the catalogue can be selected

different fitness criteria ensured a certain minimum value of quality

for aggregation at the cluster level. Evaluation criteria 2 ensure

within the individuals, and similar threshold values for density

that the blocks have a possibility to aggregate and make small

(FAR 1) ensured that it was also at par with the urban demands.

modifications or additions to improve the spatial quality. Block Catalogue Generation

101


102

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CLUSTER GENERATION

Step I PLOT / FOOTPRINT AGGREGATION

6

CLUSTER CATALOGUE GENERATION The cluster level focusses on developing aggregation with relation to the local public level spaces. The selected individuals from the block design catalogue would be aggregated to form the cluster. The aim is to generate three types of cluster with differential weighting on quality and density. The design for clusters will be processed in two steps: Different plot layouts will be arranged and evaluated to yield one arrangement for each type of cluster in the first step of design. The second step will deal with selecting the appropriate block from the catalogue for each plot location depending on various environmental and density factors. Block – block relationship will also be considered in this step.

Step II POPULATING THE CLUSTER PLOTS

These generated clusters will be further evaluated based on architectural aspect. These individuals will form the cluster design catalogue that can be adopted for larger scale design development. 6.1 Aggregation

104

6.2 Design Inputs

110

6.3 Generation Process

114

6.4 Cluster Level Catalogue

119

6.5 Conclusion

121 Cluster Catalogue Generation

103


6.1 Aggregation Inputs Urban Infrastructure

Indigenous Settlement

Design Input

Derived From Case Studies

Public Space Area

Public Space Size Design Logic

DeďŹ ning Cluster Size

Cluster Size

Derived From Case Studies

Community Level Public Space

Mumbai

Average area of public space

60 m Minimum radius: 30 m Walking distance: about 1/2 min

Based on uses of activity

251 m

Average area of public space 600 m2

2

100 m Maximum radius: 50 m Walking distance: about 1 min

120 m

200 m

Minimum radius: 60 m Walking distance: about 1 min

Maximum radius: 100 m Walking distance: about 2 min

Fig 6.1.1 Spatial Organisation of Typical Clusters in Mumbai

Public Space and Cluster Size

Fig 6.1.2 Dahi Handi Festival in Mumbai

The design attempts to utilize the distinct cultural characteristic

spaces were converted into gathering spots for festive occasions.

of having a central public space surrounded by the built forms

These activities are accommodated within a radius of 20m - 25m,

to work like Chowks in Mumbai. In the previous study of the

an aspect that was seen in most of the squares. This study would

indigenous settings, the fabric was divided into smaller clusters

help to form a basis for considering the area requirement of the

depending on the proximity of buildings to the closest public

community level public space as a starting point.

space. Various clusters from these settings were studied and an observation was made that these public spaces cater to a diameter

The sizes of central public space to be used in the design stage

from 60m to 100m (Fig 6.1.1). This distance was equivalent to about

would be 25m x 25m. As this size was larger than the ones found in

1 to 2 minutesâ&#x20AC;&#x2122; walk.

the indigenous settlements, the built forms that it caters to should also increase proportionally. However, to maintain walk-ability to

Fig 6.1.3 Holi festival in Mumbai

A parallel study was carried out to understand the area

this space, the walking distance should not exceed 3 to 4 minutes,

requirements of a community level public space. Various squares

which is approximately a radius of 95m - 100m.

in the Mumbai urban setting were studied, they were mapped for their daily and seasonal activities that take place in these sites. These squares were the cultural hubs for their locality. A number of activities like informal market settings, informal interaction spaces and workshops are accommodated on daily basis. These Fig 6.1.4 Workshops and events in Mumbai 104

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spaces not only holds religious signiďŹ cance, occasionally these


Aggregation type 1 : Small plot in the centre

a

a’

SVF : 0.91

121o Section a - a’ Minimum radius: 75 m Walking distance: Around 1 min

Aggregation type 2 : Large plot in the centre

Minimum radius: 90 m Walking distance: Around 1.5 min sample 1

c

c’

(3L, 3M, 3S)

SVF : 0.75

(4L, 4M, 4S) 15 Plots (5L, 5M, 5S)

sample 4

sample 5

Average

17671

17671

17671

17671

17671

17671

Plot area (m2)

12271

12000

11709

11276

11947

11841

Percentage Covered

69%

68%

66%

64%

68%

67%

Coverage Area (m )

25447

25447

25447

25447

25447

25447

2

12 Plots

sample 3

Coverage Area (m ) 2

9 Plots

sample 2

Maximum radius: 95 m Walking distance: Around 2 min

Plot area (m )

15777

15268

16032

15268

16286

15726

Percentage Covered

62%

60%

63%

60%

64%

62%

Coverage Area (m2)

28353

28353

28353

28353

28353

28353

Plot area (m )

20981

21265

19847

20698

19847

20528

Percentage Covered

74%

75%

70%

73%

70%

72%

2

2

85o Section c - c’ Fig 6.1.5 Sky View Factor Comparison

Result of three plot aggregation experiments

Experiment 1 Position of Plots

Experiment 2 Plot Numbers

The earlier study informed the sizes of public space that the plots

This experiment was conducted to derive the most efficient

can be aggregated around. Each plot is given an offset width of 4m

number of plots to be aggregated around a single public space

in order to create the initial street width. In order to achieve the

within a distance of 60m - 100m, which is an ideal walking distance

maximum sky view factor on the central public space, different plot

to a public space.

aggregation strategies were experimented. The figure shows two of

To investigate this, 9, 12 and 15 plots were aggregated around a

the examples, one with the largest and other with the smallest plots

public space with equal number of small, medium and large plots,

closest to the public space. Typical blocks were selected from the

thereby simplifying the complexity of choosing the plots from the

block design catalogue for these experiments. All the results have

catalogue. The 9, 12 and 15 plots catered to a radius of 75 m, 90 m

shown that the sky view factor is either similar or better than the

and 95 m respectively. It was observed that for a public space of

indigenous setting. It is essential to mention that the best sky view

the size 25m x 25m, the 75m radius would be too small to create

factor was needed at this stage so when the individual blocks get

a sufficient cluster size. Further, the packing of the 12 and 15 plots

modified and increase in height where they don’t affect the quality

did not drastically change the radius to which it catered. However

of public space dramatically.

the aggregation with 15 plots was more efficient as the percentage covered was consistently around 72% of the area of walking radius.

It was observed that to maintain a higher sky view factor similar to the indigenous settings, smaller plots need to be closest, and the

It was concluded that the aggregation of 15 plots provided the most

larger plots furthest away from the central open space. This would

efficient packing catering to a diameter of 95m which is within a

be informed to aggregate the plots in the next stage.

range of 1-2 minutes walking distance. Cluster Catalogue Generation

105


6.1 Aggregation Logic

Urban Infrastructure

Indigenous Settlement

Design Input

Experiments

Derived From Case Studies

Public Space Area

Public Space Size

25 m 25 m

Design Logic

Cluster Size

DeďŹ ning Cluster Size

Radius: 90 m

Experiment 2 Plot Numbers

Plot Numbers Large Medium Small

Parameter Parameters Public Space

Sky view Factor

Experiment 1 Position of Plots

Position of Plots

Evaluation Pocket Space Area

Distribution Of Pocket Space

Geometry of Pocket Space

106

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Good Geometry

Irregular Geometry


Plot Aggregation

Block Design Catalogue

Design Input

Q

Block Size Large Medium Small

Number of Blocks In Cluster 15 Blocks x5 Large Block

Q

Cluster 1 15 Blocks

x5 Medium Block

Q

Rectangle Packing Algorithm

D

D

Typology 3 4 4 4

Small Block

D

D

Cluster 3

15 Blocks

Parameter

Q

Typology 2 4 4 4

x5

Cluster 2

Position of Blocks

Typology 1 4 4 4

15 Blocks

10 Site Plot

Evaluation 1

Largest

Q Cluster 1

Pocket Space Area Average

Q

D Cluster 2

Smallest

D Cluster 3

Evaluation 2

Distribution Of Pocket Space

Evaluation 3

Geometry of Pocket Space

Aggregation Logic

Plot Generation Q

Cluster 1 1 Site Plot

Q

D

Cluster 2 1 Site Plot

D

Cluster 3 1 Site Plot

Aggregation of plots is the ďŹ rst stage of this design stage, which aims to generate the plot layouts for each typology of cluster. Each aggregation only holds the information of the plot sizes of blocks (24 x 24 M, 32 X32 M & 40 X 40M). Rectangle packing algorithm is used to generate a number of iterations which could be selected depending on the evaluation criteria. This is described in the

Cluster Generation

following topic.

Cluster Catalogue Generation

107


6.1 Aggregation Generation & Evaluation D Cluster 3

C_01

C_02

C_03

C_04

C_05

C_09

C_10

Plot

Pocket space

Q

Q Cluster 1

D Cluster 2

C_07

C_06

C_08

Plot

Pocket space

Q

D

108

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D

Q

C_01

C_02

C_03

C_04

C_05

C_06

C_07

C_08

C_09

C_10

Average

Plot area (m2)

20991

20474

20448

20760

19401

20053

20148

21253

20601

20592

21623

Pocket space area (m2)

1391

803

819

1148

459

403

819

1343

980

971

914


Plot Evaluation Evaluation Criteria

Q Cluster 1

1 Pocket space area

Q

D Cluster 2

D Cluster 3

Largest

Average

Smallest

2

Distribution of pocket space

Well distributed

Well distributed

Well distributed

3

Geometry of pocket space

Square shape preferred

Square shape preferred

Square shape preferred

Q Cluster 1

1

Large Plot 40 m

32 m 40 m

Q

Medium Plot

Small Plot 24 m

32 m

24 m

D Cluster 2

D Cluster 3

C_01

C_04

C_08

C_02

C_03

C_07

C_09

C_10

C_05

C_06

1391 m2

1148 m2

1343 m2

803 m2

819 m2

819 m2

980 m2

971 m2

459 m2

403 m2

Poor Distribution

Good Distribution

Poor Distribution

Good Distribution

Poor Distribution

2

3

Good Distribution

Good Distribution

More liner space compare to C_08

Good square shaped spaces

More pocket space created compare to C_03

C_08

C_07

Good Distribution

Very liner space geometry compare to C_07

C_05

Fig 6.1.2 Spatial Organisation of Generated Q/QD/D Clusters

Evaluation / Selection of Plot Aggregations Various aggregations with 15 plots are generated on the basis of

pocket spaces. Well distributed and numerous pocket spaces were

the first two experiments. The aim of this evaluation is to select

preferred to few and large concentrated ones. The last evaluation

different aggregation for different applications. The evaluation

criteria looked into the geometry of these spaces. Narrow and long

checks the kind of porosity suitable for each kind of cluster. Three

pocket spaces would potentially have poor spatial quality compared

packing arrangements are desired on the basis of porosity.

with the square shaped, which could be utilised to create better social spaces within the cluster.

Each of the aggregated plots is evaluated on three evaluation criteria. The plots are first evaluated on the basis of the amount

Three plot aggregations are selected after the evaluation process,

of porosity i.e. the area of pocket spaces formed. The aggregation

one for each cluster typology. These would be used in the cluster

with higher porosity would be used for quality preferred typologies.

generation stage.

Packing with low porosity can be used for high density typologies. They were also evaluated on the basis of the distribution of the Cluster Catalogue Generation

109


6.2 Design Input Parameters

Q

Quality Criteria Cluster

Block

Semi-public Space

Selection Commands

Public Space

Selection Commands

Non Uniform Scaling

Adjacent Block Relation

Mirror Vertical, Horizontal Rotation 180o

Circulation length (CL)

Circulation length (CL)

Circulation length (CL)

Circulation length (CL)

Sky View Factor (SVF)

Sky View Factor (SVF)

Sky View Factor (SVF)

Sky View Factor (SVF)

South Facing Surface Ratio (SFR)

South Facing Surface Ratio (SFR)

South Facing Surface Ratio (SFR)

South Facing Surface Ratio (SFR)

Floor Area Ratio (FAR)

Floor Area Ratio (FAR)

Floor Area Ratio (FAR)

Floor Area Ratio (FAR)

Enclosure Value (EV)

Enclosure Value (EV)

Enclosure Value (EV)

Enclosure Value (EV)

Open Space Ratio (OSR)

Open Space Ratio (OSR)

Open Space Ratio (OSR)

Open Space Ratio (OSR)

Shaded Area Proportion (SAP)

Shaded Area Proportion (SAP)

Criteria affected Criteria not affected

Cluster Generation

Criteria Optimisation

Using the previous plot aggregations with the operational flexibility

A number of different parameters have been considered for testing

and variable block selection from the block design catalogue, three

the spatial quality. The logic diagram above shows the operations

types of clusters will be generated. Cluster with high weighting on

considered with respect to the block and the cluster generation,

quality, clusters with equal weighting of quality and density and

which also describe the levels at when the criteria was adopted.

the last one with high weighting on density.

Experiments were conducted to understand the effect of the subsequent operation on the various parameters. At the same time,

110

SynchroniCity

Different quality and density parameters will be used as fitness

attempts were made to minimise this effect on the quality criteria

criteria on all of the clusters. Depending on the criteria, a set of

produced during the previous stages. To adapt to the selection

individual blocks would be selected to be placed on the designated

process, each block had the flexibility to optimise the sky view

plots obtained from aggregation. The process would look into block

factor in order to develop the adjacent block relations. Parameters

- block relationship so as to not affect the parameters optimised

like shaded area proportion would be used at the final stage of

during the block generation stage.

cluster generation after the orientation of the blocks gets fixed.


Experiments South Facing Surface Ratio

(Scaling in 2 dimensions )

Original

SFR : 0.15

2.5 %

SFR : 0.16

5%

SFR : 0.18

7.5 %

SFR : 0.20

10 %

12.5 %

15 %

SFR : 0.23

SFR : 0.25

SFR : 0.28

Circulation Length

(Scaling in 2 dimensions )

Original

CL : 55 m

+ 2.5 %

CL : 57 m

+5%

CL : 59 m

+ 7.5 %

CL : 61 m

+ 10 %

CL : 64 m

+ 12.5 %

CL : 67 m

+ 15 %

CL : 70 m

Sky View Factor

(Scaling in 2 dimensions )

Original

SVF : 0.56

- 2.5 %

SVF : 0.54

-5%

SVF : 0.52

- 7.5 %

SVF : 0.49

- 10 %

SVF : 0.44

- 12.5 %

SVF : 0.42

- 15 %

SVF : 0.36

Fig 6.2.1 Comparison between Original/ Scaled Geometries

Experiment 3 Block Scaling The aim of this experiment is to understand and analyse the effect

that specific rule of scaling. Thus, these parameters were tested for

on the quality criteria of semi-public spaces when the blocks are

their specific operations.

imposed with non-uniform scaling. All the criteria are considered, however the open space ratio, enclosure factor and floor area ratio are not affected by this operation.

Conclusion

Circulation length would only be affected when the block is scaled

Non uniform scaling does not affect three of the quality parameters.

up as the length increases, it would also exceed the optimised value.

It was also observed that the block can be scaled by 5% without

When the block is scaled down, the visible area to the sky decreases

affecting the quality criteria drastically. This aspect allows higher

thus the sky view factor would decrease. The south facing surface

flexibility to blocks for achieving better packing.

ratio would be affected majority if the ratio of the south facing surface increases over the sum of other faces, this was tested with Cluster Catalogue Generation

111


6.2 Design Inputs Block Selection

Large

Large x5

40 m

x5

40 m

40 m

Large x5

40 m

40 m

Q

Q

40 m

D

D

Cluster 1

Cluster 2

Cluster 3

5

5

5 2

2 2,1,0

0,1,2

3

3 3

L_Q_01

L_Q_03

L_Q_05

L_Q_07

L_QD_03

L_QD_04

Q Typology 1

Q

Q

Block Size Large Medium Small

Large Block

L_QD_06

L_QD_07

L_D_01

L_D_03

D Typology 2

Q

D

D

Typology 1 4

Typology 2 4

Typology 3 4

4

4

4

4

4

4

Block Design Catalogue

15 Blocks x5 Large Block

Block Selection Large Block x 5

for cluster 1 which has a high quality weightage, (Fig 6.2.1), more typology 1 blocks with high weighting on quality would be used. The rest of the plot footprints have the ďŹ&#x201A;exibility to choose from typology 2 or typology 3 blocks. This process was continued for all the three sizes of blocks aggregated to form each cluster.

112

SynchroniCity

x5

x5

Medium Block

Q

Small Block

Q

D

D

Cluster 1

Cluster 2

3

2 1 0

2 1 0

Typology 2 Q D

2 1 0

3

0 1 2

Typology 3

0 1 2

0 1 2

3

typology of individual blocks in the cluster is varied. For example,

40 m

L_D_08

D Typology 3

Depending on the desired result in terms of density and quality, the 40 m

L_D_04

Typology 1

Fig 6.2.1

Q

D

Cluster 3


Block - Block Relationship Rotate 180o

Original

Flip Y Axis Flip X Axis

SVF: Sky View Factor

Block selection and modification

SVF: 0.49

SVF: 0.54

SVF: 0.64

SVF: 0.70

Fig 6.2.2 Spatial Parameters Optimised by Geometry Modifications

Block Selection and Modification The generation system is based on flexibility to select blocks

aggregation is minimum.

and arrange them synchronously with the given rule set. An evolutionary solver is introduced to evaluate the condition of

In order to expand the block design catalogue and increase the

adjacent façades. The diagram demonstrates four blocks that needs

number of options in the selection process, different modification

to be aggregated. With different aggregations, the sky view factor

operations were introduced. Each blocks could rotate 1800, mirror

of the selected semi-public space varies. The algorithm attempts

in X and Y axis or perform both the options at the same time. These

to optimise sky view factor and the block-block relationship by

operations were carefully chosen as none of the parameters with

rearranging or modifying the blocks. These operations are aimed

respect to environmental quality criteria get affected. With the

at improving spatial quality by maintaining the sky view factor of

addition of modification command in the selection process, each

each semi-public space so that the impact on overall quality during

block can have up to four possible typologies to select from. Cluster Catalogue Generation

113


6.3 Generation Process Logic

Urban Infrastructure

Indigenous Settlement

Experiments

Design Input

Derived From Case Studies

Cluster Generation Block Selection And ModiďŹ cation Mirror

Street OďŹ&#x20AC;set Experiment 3

Original

15% scale in x,y

Block Scaling

Block Scaling

H W

Fitness Criteria Adjacent Block Relation

Parameters Semi-public Space

Sky view Factor

Block Edge Condition

Public Space Quality

Parameters Public Space

Solar Radiation

Solar Radiation

Q Quality

Enclosure

Shaded Area Proportion

Enclosure

Shaded Area Proportion

D

Cluster Density

Density Requirement

Density

FAR

Evaluation Design Logic

Connectivity With Street

114

SynchroniCity

Pedestrian Network


Plot Generation Q

Q

Cluster 1

Block Design Catalogue

D

Cluster 2

1 Plot

Q

D

Block Size

Cluster 3

1 Plot

Large Medium Small

1 Plot

Typology 1 4 4 4

Q

D

Typology 2 4 4 4

D

Typology 3 4 4 4

The cluster generation process can select the individuals from the block generation catalogue based on the criteria

Cluster Generation Design Input 15 Blocks x5

x5

Large Block

Medium Block

Large Block x 5 Typology 1 Typology 2

Q Q

D D

Genetic Algorithm

Typology 3

x5 Small Block

Q

Q

D

D

Cluster 1

Cluster 2

Cluster 3

3

2 1 0

2 1 0

2 1 0

3

0 1 2

0 1 2

0 1 2

3

Fitness Criteria

10 Clusters

Evaluation 1

4 Clusters

Ground network

The blocks can be selected depending on the desired result in terms of density and quality in each cluster

10 clusters were generated that could be further evaluated.

Cluster Generation Catalogue Q

Q

D

D

Cluster 1

Cluster 2

Cluster 3

4 Clusters

4 Clusters

4 Clusters

Cluster Generation Logic Three clusters with diďŹ&#x20AC;erential weightings on quality and density

Patch Generation

will be generated based on the design inputs discussed in the early part of the chapter. These clusters will be evaluated on the ground pedestrian network on aspects that form part of the design ambitions. A few parameters which could not be optimised at the block level will be considered higher design levels. This would help the blocks to adapt to the cluster generation, thus provide the ďŹ nal cluster design catalogue. Cluster Catalogue Generation

115


6.3 Generation Process Fitness Criteria

Urban Infrastructure

Fitness Criteria

Indigenous Settlement Derived From Case Studies

Parameters Semi-public Space

Adjacent Block Relation

Block Edge Condition

Sky view Factor Public Space

Public Space Quality

Solar Radiation

Solar Radiation

Enclosure

Enclosure Shaded Area Proportion

Q Quality

Shaded Area Proportion Cluster Density

Density Requirement

FAR

< 1.0

D Density

1.0

1.91 2.0

> 3.0

Fitness Criteria

Value

Score

FAR

1.91

0.98

BEC

8%

1.00

ISR

410

0.38

SAP

10%

0.92

EV

0.66

0.08

Original Domain

FAR

0

0.98 10%

Floor Area Ratio (FAR) Block Edge Condition(BEC) Incident Solar Radiation (ISR) Shaded Area Proportion (SAP) Enclosure Value (EV)

Target Domain

0.98 1

> 10%

D

Cluster 3

8%

Amplify x 5

4.90

Amplify x 2

2.00

Amplify x 1

0.38

Amplify x 1

0.92

Amplify x 1

0.08

FAR

< 10% 1.00

BEC 0.38

BEC

ISR 0.92

0 > 430

SAP

1 430

0.08

370

410

EV Parameters were weighted by the given amplification values

< 370

Q

ISR

Cluster 1 Q 0

0.38

D

Cluster 2

1

= FAR x 5 + BEC x 2 + ISR x 1 + SAP x 1 + EV x 1

D 8%

> 8%

10%

< 8%

Cluster 3

Remapping Parameters and Differential Weighting

SAP

Similar to the strategy introduced at block level, parameters that address either density or quality are first remapped into a [0, 1] 0 >0.70

0.83

0.700.66

0.45

1 <0.45

domain (Fig 6.4.1) and then given differentiated weights. During this process, FAR criterion is amplified by 5 while Block Edge Condition(BEC),Incident Solar Radiation (ISR), Shaded Area Proportion (SAP),Enclosure Value (EV) are multiplied by 2, 1, 1 and 1 respectively. Thus mathematical expression can be concluded as: Total Score = (FAR x 5) + (BEC x 2) + (ISR x 1) + (SAP x 1) + (EV x 1)

EV

Identical amplification factors are given to all the generated cluster morphologies and an Evolutionary Solver will be linked to this

Fig 6.4.1 116

SynchroniCity

0 0.08

1

expression which will aim to maximise the weighted sum value.


Evaluation Indigenous Settlement

Evaluation

Derived From Case Studies

Design Logic

Connectivity With Street

1 Centralised network pattern

Pedestrian Network

2 Continuous ring streets

Fig 6.4.2

Fig 6.4.3

Evaluation Criteria Pedestrian Network One of the project ambitions is to create an informal pedestrian

longest continuous ring streets that could improve connectivity

network similar to Chowks in Mumbai and Hutongs in Beijing,

around the boundary. More variation of pedestrian network would

therefore the clusters get evaluated on the ground network on

be created in the cluster, which could provide more diversity in

these two factors, centralised network pattern (Fig 6.4.2), and the

social spaces.

continuous ring streets (Fig 6.4.3). The analysis took into account not only the pedestrian networks, but also the informal passages that were formed within the individual blocks. The ďŹ rst criteria centralised network pattern, would measure on the connectivity of the public space to the edges of the cluster. The second criteria would help to select clusters which consist of the Cluster Catalogue Generation

117


6.3 Generation Process Evaluation

GENERATED ITERATIONS

EVALUATION Centralised Pattern and Longest Continuous Ring Street

SELECTED CLUSTERS FOR CATALOGUE

Public Space Area (m2)

Population

FAR

Incident Solar Radiation (Wh/m2)

Enclosure Value

Shaded Area

C_Q_01

892

1282

1.21

371

0.45

16%

C_Q_01

C_Q_01

C_Q_02

974

1253

1.18

398

0.61

6%

C_Q_02

C_Q_02

C_Q_03

1007

1289

1.21

388

0.57

8%

C_Q_03

C_Q_03

C_Q_04

925

1293

1.22

390

0.50

8%

C_Q_04

-

C_Q_05

1100

1288

1.21

389

0.41

8%

C_Q_05

-

C_Q_06

1049

1234

1.16

384

0.52

9%

C_Q_06

C_Q_06

C_Q_07

960

1188

1.12

380

0.42

6%

C_Q_07

-

C_Q_08

1030

1295

1.22

377

0.58

9%

C_Q_08

-

C_Q_09

1000

1213

1.14

390

0.68

7%

C_Q_09

-

C_Q_10

1065

1298

1.22

360

0.61

12%

C_Q_10

-

Enclosure Value

Shaded Area

Public Space Area (m2)

Population

FAR

Incident Solar Radiation (Wh/m2)

C_QD_01

849

1517

1.51

392

0.64

9%

C_QD_01

C_QD_01

C_QD_02

1032

1499

1.49

387

0.78

10%

C_QD_02

-

C_QD_03

865

1513

1.50

413

0.51

6%

C_QD_03

C_QD_03

C_QD_04

847

1493

1.48

420

0.51

5%

C_QD_04

C_QD_04

C_QD_05

862

1452

1.44

413

0.50

6%

C_QD_05

-

C_QD_06

860

1521

1.51

406

0.62

5%

C_QD_06

-

C_QD_07

887

1513

1.50

399

0.62

7%

C_QD_07

-

C_QD_08

835

1520

1.51

412

0.49

6%

C_QD_08

-

C_QD_09

888

1507

1.50

400

0.55

8%

C_QD_09

-

C_QD_10

866

1501

1.49

413

0.50

7%

C_QD_10

C_QD_10

Enclosure Value

Shaded Area

Public Space Area (m2)

Population

FAR

Incident Solar Radiation (Wh/m2)

C_D_01

867

1918

1.98

403

0.51

8%

C_D_01

-

C_D_02

815

1854

1.91

410

0.61

7%

C_D_02

-

C_D_03

818

1857

1.91

410

0.66

10%

C_D_03

-

C_D_04

889

1843

1.90

385

0.72

11%

C_D_04

C_D_04

C_D_05

818

1849

1.91

408

0.72

6%

C_D_05

-

C_D_06

878

1830

1.89

375

0.78

10%

C_D_06

-

C_D_07

890

1843

1.90

385

0.72

11%

C_D_07

C_D_07

C_D_08

894

1905

1.96

394

0.65

9%

C_D_08

C_D_08

C_D_09

866

1918

1.98

401

0.63

8%

C_D_09

-

C_D_10

860

1953

2.01

391

0.67

9%

C_D_10

C_D_10

118

SynchroniCity


SAP(shaded area proportion)

FAR FAR

FAR FAR

EV EV

Q

BEC BEC

SAP SAP

Cluster 1

ISR ISR

FAR FAR

C_Q_01 EV EV

BEC BEC

SAP SAP

EV EV

BEC BEC

0.55

D

ISR ISR

SAP SAP

FAR FAR BEC BEC

SAP SAP

ISR ISR

Q SAP SAP D

BEC BEC

EV EV

3.19 ISR 0.45 ISR

QD

ISR ISR

SAP SAP

BEC BEC

BECEV BECEV

ISR ISR

Q

SAP SAP

BEC BEC

EV EV

ISR ISR

D

2.70 ISR

BEC BEC

EV EV

ISR ISR

FAR 1.52

SAP SAP D

BEC BEC

QD D Q1 BEC BEC

SAP SAP

ISR ISR

BEC

ISR ISR

EV EV

FAR 1.49

BEC

EV

BECEV

0.40 ISR

SAP

ISR

FAR FAR

BEC BEC

ISR ISR

EV EV

SAP

Q 10 BEC BEC

SAP SAP

QD

SAP

D

SAP SAP

SAP SAP

3.08

D

2.50 ISR

SAP

BEC

EV

BEC BEC

EV EV

ISR ISR

SAP SAP

FAR FAR

BECEV EV

BEC BEC

EV EV

BEC BEC

SAP

II

FAR FAR

Cluster Catalogue Generation ISR

I

FAR FAR

EV EV

SAP SAP

I I

FAR

FAR 1.51

FAR FAR

SAP

EV EV

FAR FAR

ISR ISR

I I

FAR FAR

QD SAP D

BEC BEC

FAR

EV

EV

ISR

SAP SAP

ISR ISR

ISR 2.40 ISR

ISR ISR

Q

EV BEC BECEV

SAP SAP

EV EV

3.61

SAP SAP

BEC

I I

FAR FAR

BEC BEC

FAR FAR

FAR FAR

EV EV

ISR ISR

Floor Area Ratio (FAR) SAP(shaded area proportion) EV Block Edge Condition(BEC) FAR FAR Incident Solar Radiation (ISR) Shaded Area Proportion (SAP) Enclosure Value (EV) EV BEC EV BEC

ISR

EV EV

FAR C_QD_10

Q

I I

SAP SAP

BEC BEC

EV EV

FAR

FAR FAR

FAR FAR

FAR FAR

ISR ISR

I I

SAP SAP

ISR ISR

SAP SAP D

BEC BEC

FAR

EV

ISR ISR

SAP SAP

D

FAR C_QD_04 FAR

BEC BECEV EV

SAP SAP

ISR

BEC BEC

FAR FAR

EV EV

EV EV

2.65 ISR

SAP SAP

BEC BEC

FAR FAR

BEC BEC

3.10

EV EV

ISR ISR

SAP SAP

ISR ISR

SAP SAP

FAR FAR

BECEV BECEV

SAP SAP

EV EV

ISR ISR

Q

I I

FAR FAR

QD

FAR FAR

EV EV

SAP SAP

BEC BEC

SAP SAP

ISR ISR

SAP SAP

ISR ISR

ISR ISR

EV EV

FAR FAR

3.66

Q

2.61

FAR

Q

FAR FAR

FAR

FAR 1.17

SAP SAP

Q

EV EV

FAR C_QD_03

BECEV BECEV

SAP SAP

BEC BEC

FAR FAR

ISR ISR

I I

FAR FAR

FAR 1.54

SAP SAP

BEC BEC

SAP SAP

ISR ISR

EV EV

FAR FAR

EV EV

FAR

EV EV

FAR FAR

FAR FAR

SAP SAP

FAR FAR

ISR ISR

SAP SAP

ISR ISR

SAP SAP

BEC BEC

FAR FAR

D Density

SAP SAP

ISR ISR

Q

0.55 ISR ISR

SAP SAP D

SAP D

SAP

D

EV EV

C_QD_01

2.73

Q

Q

ISR

SAP SAP

BEC BEC

FAR FAR

EV EV

FAR C_Q_06

BEC

EV EV

FAR FAR

FAR 1.23

SAP SAP

Q Quality

BEC BEC

BEC BEC

FAR FAR

Q1

ISR ISR

BECEV BECEV

SAP SAP

ISR ISR

EV EV

FAR C_Q_03 FAR

EV

ISR ISR

SAP SAP

BEC BEC

SAP SAP

EV EV

BEC BEC

FAR FAR

EV EV

FAR FAR

EV EV

ISR ISR

Q1

SAP SAP

FAR FAR

Q1

EV EV

FAR FAR

FAR FAR

FAR FAR

FAR 1.20 Q

EV EV

D SAP SAP

BECEV BECEV

SAP SAP

BEC BEC

FAR

SAP(shaded area proportion)

Q

Cluster 2

EV EV

ISR ISR

EV EV

FAR C_Q_02

Q 10

ISR ISR

FAR FAR

FAR FAR

EV EV

EV EV

BEC BEC

FAR FAR

6.4 Cluster Design Catalogue

SAP SAP

FAR

BEC BEC

EV BEC BECEV

EV EV

ISR ISR

FAR FAR

3.76

Q

Q1

Q 10

SAP(shaded area proportion) FAR

FAR 1.22

SAP SAP

EV EV

FAR FAR

FAR FAR

ISR

EV EV

119 SAP

I


SAP

ISR

SAP

FAR 6.4 Cluster Design Catalogue EV

BEC

SAP

EV

ISR

Cluster 3 FAR

Q 10

BEC

SAP

FAR

EV

BEC

ISR

FAR

EV

D

ISR

SAP

FAR

BEC

SAP

ISR

ISR

FAR

C_D_04

EV

BEC

EV

FAR 1.95 Q

SAP

ded area proportion)

ISR

SAP

FAR

SAP

D

FAR

EV

BEC

SAP

ISR

BEC

SAP

FAR

BEC

BEC

SAP

ISR

ISR

FAR C_D_07

EV

BEC

EV

FAR 1.96 Q

SAP

ISR

SAP

FAR

Q 10

BEC

SAP

ded area proportion)

SAPD

ISR

FAR

EV

BEC

SAP

FAR

BEC

SAP

ISR

SAP

ISR

FAR C_D_08

EV

ISR

BEC

SAP

FAR

EV

EV

FAR 2.01

SAP

SAPD

ISR

BEC

EV

ISR

QD SAP

FAR FAR

BEC

EV

BEC BEC

BEC BEC

Q 10

FAR FAR

EV EV

ISR

FAR 2.06

120

SAP SAPD

ISR ISR FAR FAR

EV EV

BEC BEC

BEC BEC

2.02

Cluster Design Catalogue

5.00 ISR ISR

Clusters in the catalogue contain variation in density, quality with

FAR FAR

EV EV

BEC BEC

well distributed semi-public spaces at elevated levels within them.

EV EV

BEC BEC

The modified clusters have a population varying from 500 people/ ha in cluster 1 to 1000 people/ha in cluster 3. These clusters form a

SAP SAP

ded area proportion)

SAP SAP

BEC

C_D_10 FAR FAR

EV EV

ISR ISR

4.95 ISR

SAP

ISR

Q SAP SAP

2.19

D

FAR FAR

EV EV

BEC

FAR

FAR

Q

4.85 ISR

BEC

Q

Q1

2.67

EV

FAR

EV

BEC

FAR

EV

ISR

4.85 ISR

EV

FAR

EV

1.77

FAR

EV

ISR

BEC

ISR ISR

SAP SAP

FAR FAR

SynchroniCity EV EV

ISR ISR

SAP SAP

FAR FAR BEC BEC

EV EV

design catalogue with variable density and quality configurations

ISR ISR

that can be aggregated to develop a larger urban scenario.

FAR FAR BEC BEC

EV EV

BEC BEC


6.5 Conclusion Comparative Study

Density Score (FAR)

Quality Score

2.50

2.50

Parameters

2.00

2.00

1.50

1.50

Shaded Area Proportion

1.00

Sky View Factor

Cluster 3

0

Cluster 2

0.50

Incident Solar Radiation

Cluster 1

Cluster 3

0

Cluster 1

0.50

Cluster 2

1.00

Enclosure Value

Total Built Up Area (m2) Semi-public Space Area (m2) Public Space Area (m2) Area (m2)

Parameters

Living Area

Proposed Cluster Design Q

Q

D

D

Per Person (m2) Population

Cluster 1

Cluster 2

Cluster 3

Population

Density Score

1.20

1.52

2.00

Density (p/ha)

Quality Score

2.39

2.02

2.05

FAR

Overall Score

3.59

3.54

4.05

The three clusters show great variation in the density that they are able to achieve. The density of Cluster typology 1 is almost half of that of typology 3. The increase is consistent from quality preferred to density preferred. Such is not the case for quality where the density preferred clusters were able to achieve a higher average quality that the equally weighted ones. This is an anomaly that may be attributed to the plot aggregation. Although all 3 typologies of clusters reach around 40% of the open space per person ratio of the Ranwar Village, it has been able to increment the population density by 300% - 400%. The cluster 1 shares a similar size and height with the Ranwar Village, and the previous comparisons have proved that it was able to maintain similar quality while insigniďŹ cantly increasing density. To compare it with the cluster 2

Q

Parameters

Q

D

D

Cluster 2

Cluster 3

21253

20148

19401

25594

30551

38739

7157

8397

8817

2324

1676

1292

9481

10073

10109

20

20

20

1280

1528

1937

602

758

998

1.20

1.52

2.00

Cluster 1 Plot Area

Open Space Overall Density Quality Comparison

Proposed Cluster Design

Proposed Cluster Design Q

Q

D

D

Cluster 1

Cluster 2

Cluster 3

5.59

5.50

4.55

1.82

1.10

0.67

7.41

6.59

5.22

385

410

389

10%

7%

10%

Semi-public Space Per Person (m2) Public Space Per Person (m2) Open Space Per Person (m2) Incident Solar Radiation (wh/m ) 2

Shaded Area

and 3, the scale of built form is larger with building height over 20

Proportion

meters while maintaining a similar quality value. Overall, Cluster 2

Enclosure Value

0.54

0.54

0.69

Sky View Factor

0.84

0.81

0.79

has a much better scale of the built morphology, and it was able to create more pocket spaces similar to Cluster 1.

Cluster Catalogue Generation

121


6.5 Conclusion

Cluster 1

Plot Area: 21253 m2 Coverage Radius: 93 m Number of Semi-public Space: 93

This cluster typology is closest to the indigenous settings in terms of parametric quality aspects. It however supports the least density. The distribution of semi-public spaces and open space per person ratio is highest in this type of cluster. Therefore it is ideal for locations where density is not the primary concern. Fig 6.5.1 Isometric View of Cluster 1

Cluster 2 Plot Area: 20148 m2 Coverage Radius: 79 m Number of Semi-public Space: 79

This cluster typology shows the best balance of density and quality attributes. It is a intermediate version of Cluster typology 1 and Cluster typology 3. Therefore has the most ďŹ&#x201A;exibility in application

Fig 6.5.2 Isometric View of Cluster 2

Cluster 3 Plot Area: 19401 m2 Coverage Radius: 69 m Number of Semi-public Space: 69

This cluster typology is the preferred choice for high density scenarios. The building proportions in this cluster start relating to the contemporary urban morphologies but still maintain high proportion of open semi-public spaces. Fig 6.5.3 Isometric View of Cluster 3 122

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+12 +6 +0

Q Cluster 1

C_Q_02

+18 +12 +6 +0

Q

D Cluster 2

C_QD_01 +24 +18 +12 +6 +0

D Cluster 3

Fig 6.5.4 Isometric Sections of Cluster 1/2/3

C_D_04

6.5 Conclusion The process of cluster generation was able to achieve an evolutionary

could be generated with the ability of blocks to adapt to different

urban morphology consisting of varied streetscapes that provide

organization. The sectional study enforces this point where similar

different spatial experiences and shaded pocket spaces.

blocks were able to generate variety of spaces, it also showed a very even distribution of semi-public spaces within the built form. The

These aspects were similar to the indigenous settings and can

elevated network was not only able to integrate the semi-public

be converted into interesting social spaces and mid-rise block

spaces but was also able to make the spaces more accessible. The

typologies with multi-level open spaces. The morphology of

generated catalogue consisting of clusters that show noticeable

the clusters were able to achieve spaces that could be used for

variation in spaces, density and quality can be used for designing

informal activities and the different block typologies were able

of urban patches with different requirements.

to add further spatial variation. Different cluster morphologies Cluster Catalogue Generation

123


124

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7

NEIGHBOURHOOD LEVEL DESIGN DEVELOPMENT

The neighbourhood level of design looks at application of blocks and cluster catalogues in global aggregations. The aim is to test the system logic in two distinct sites to understand and study the system flexibility of adapting to different specific scenarios. In the first part of this chapter, initial experiments are conducted to understand the aggregation logics. The subsequent part discusses about understanding the two distinct site conditions and outline different ambitions for the locations. The third part deals with the design process which includes aggregation, morphology modifications and introducing programmatic variation according to socio-cultural and climatic requirements. 7.0 Process Review : Catalogues & Parameters

128

7.1 Initial Experiments

136

7.2 Site Analysis

147

7.3 Aggregation

161

7.4 Network Generation

177

7.5 Block Differentiation

189

7.6 Programmatic Adaptation

201 Neighbourhood Level Design Development

125


Block Catalogue Typology 1 Clusters

Typology 2 Clusters

Q

Typology 3 Clusters

QD

D

The three typologies of individual blocks in the block catalogue input into the Cluster level aggregation

12 Blocks

12 Blocks

12 Blocks

Cluster Catalogue Typology 1 Clusters

Typology 2 Clusters

Q

Typology 3 Clusters

QD

D

The three typologies of clusters in the cluster catalogue input into the neighbourhood level generation.

4 Clusters

4 Clusters

4 Clusters

Neighbourhood Level Design

126

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MUMBAI SITE

BEIJING SITE

Thane, City Outskirts

Rongshu, City Core

Aggregation

Aggregation

Density, Network Gradients

Density, Network Gradients

Network Generation

Network Generation

Local Street Character, Boundary conditions, Hierarchy

Local Street Character, Boundary conditions, Hierarchy

Block Differentiation

Block Differentiation

New Parameters, Architectural Features

New Parameters, Architectural Features

Programmatic Variation

Programmatic Variation

Site Specific Programmatic Use, Block Modification and Alternate Block Generations

Site Specific Programmatic Use, Block Modification and Alternate Block Generations

The differentiated site conditions are analysed to generate separate design ambitions for each.

This level deals with populating the selected site with cluster typologies with the aim to achieve the desired density.

Cluster Morphologies are mitigated to associate with local characteristics, site requirements and design ambition.

A recursive step to re-analyse the block level and modify certain features to relate to local social and architectural aspect.

Addition and modification of blocks to create typological and programmatic variation to suit the site context.


Neighbourhood Level Design Approach The design stage at neighbourhood level constitutes of four major

negotiated with the existing ones to achieve an overall high fitness.

sections: aggregation, network generation, block differentiation

Architectural features specific and suiting to the environmental /

and programmatic differentiation. In the first part of the process the

cultural requirements of the site are also introduced at this stage.

aggregation of the clusters is tested for adaptability of the system

The final stage of design investigates in detail the ability of the

to accommodate ambitions to achieve different types of density

system to introduce programmatic and morphological variations

requirements and density gradients, the emergent characteristics

needed at each site situation.

are studied and analysed for differentiated programme adaptability. To proceed in the design process, the catalogues are reviewed In the subsequently stage, network patterns are analysed and

to check the flexibility they can accommodate to suit the

consequently negotiated by block adjustments to derive network

neighbourhood level modifications. We also review the parameters

patterns befitting the design ambitions. The network patterns

that are introduced in the design for generation of these catalogues

are referenced back to the characteristics of indigenous settings

and identify the parameters that can be added or need to be

to suit the site context. The third design stage recursively looks

re-optimised during the neighbourhood level of design. The

back at the block levels and introduces site specific parameters not

parameters are also checked for their ability to accommodate small

considered during catalogue generation. The new parameters are

modification that may occur during the process. Neighbourhood Level Design Development

127


7.0 Process Review Catalogues Block Catalogue This segment of the chapter reviews the block level catalogues and analyses the ability of individual blocks to adapt to site - specific scenarios. 3 basic adaptations inherent in the blocks are mentioned below, others would be explored during the design process.

South Facing Surface Mumbai

Beijing

Rotate 90o

South Facing Percentage : 23 % Minimise South Facing Surfaces Fig 7.0.1 Mumbai block requires the south facing surface to be minimum. This aspect was optimised in the catalogue process. Therefore the block can be used directly.

South Facing Percentage : 47 % Maximise South Facing Surfaces Fig 7.0.2 The same block needs to be rotated 90 degrees to suit the conditions for Beijing where maximum south face is required during harsh winter months.

By employing the catalogue morphologies, we can guarantee that

adapt to desired site conditions. Flexibility are in need in terms of

certain level of qualities and characteristics are always maintained

relocation of semi-public spaces and simultaneous modification to

beyond the threshold value through the design process.

block geometry to aid the optimisation process. This modification would take into account the existing quality and density attributes

Modifications to be undertaken must ensure that the optimised

and ensure that these don’t get drastically affected. The relocation

qualities are not sacrificed. To optimise parameter of South facing

flexibility would also help incorporate the desired spatial aspects

surface geometry needs minimal modification. As the geometry in

to suit localised cultural requirements. For example, the Mumbai

the block level is adapted to Mumbai conditions to reduce south

blocks may require more extroverted spaces whereas Beijing may

facing façades, its implication is that its east and west faces are

require introverted spaces that have higher enclosure value. The

increased. Therefore to optimised for Beijing the geometry just

value can vary between 70% - 100%.

needs to rotate by 90 degrees. The parameter of incident solar radiation is differentiated according to the geographical location of the sites. The semi-public spaces would need to relocate to 128

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Incident Solar Radiation Block Prototype

Operational Flexibility for Semi-Public Spaces MOVE Semi-Public Space by 16 m SCALE by 1.5 times in 1 Dimension

Mumbai

Fig 7.0.3 The blocks in Mumbai need to react to the hot and sunny climatic condition of Mumbai. The semi - public spaces and blocks would need ďŹ&#x201A;exibility to generate shaded spaces.

Beijing

Fig 7.0.4 The blocks in Beijing need to optimise for cold winters by generating spaces that maximise solar factors

Semi - Public Space ConďŹ guration / Enclosure Mumbai

Beijing

Fig 7.0.5 The semi public spaces mitigate their position to suit the local requirements. In Mumbai these may deal with extroverted spaces with lower enclosure.

Fig 7.0.6 In Beijing there is higher probability of spaces to convert into courtyard type spaces to suit local socio-cultural conditions.

Neighbourhood Level Design Development

129


7.0 Process Review Catalogues Cluster Catalogue This segment of the chapter reviews the cluster level catalogues and analyses the ability of these to adapt to site - specific scenarios. The clusters refer back to public spaces in each site location and explore the possibility relate the designed morphologies to these.

Chowks , Ranwar Village

Hutongs, Nan Chi Zi

Fig 7.0.7 Consolidated, convex and centralised spaces act

Fig 7.0.8 Linear, orthogonal and intimate small streets

like local level public spaces in Mumbai

convert into local level informal public spaces in Beijing.

In order to enable morphological differentiation at neighbourhood

spaces. The network system would be orthogonal to suit Beijing’s

level, local public spaces are referenced. The exercise looks into

existing city fabrics and the Hutong-like spaces would be located

possibility of modifying morphologies to match characteristics

on secondary or tertiary routes.

of Chowks in Mumbai and Hutongs in Beijing. The morphology and location of these would be in relation to the network and its

To establish control over the network system, new parameters

hierarchy.

like detouring, network density and cul-de sac proportion would be introduced. These aspects would be discussed further in the

For Example, In Mumbai a centralised pattern leading towards the

network generation chapter. As the block-block relationship,

Chowk with off-setted streets would be considered in detail, the

previously optimised, may get affected during the re-organisation

location of Chowks would be preferably on secondary or tertiary

process, a recursive step would need to be introduced that looks

routes. Contrary to Mumbai, the public spaces In Beijing would

back at block level.

convert into linear arrangements which relate to local Hutong 130

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Prototype Block

Mumbai Option : Centrality emphasised with radial street patterns and large central space created by block reorganisation

Beijing Option : Linearity emphasised with blocks reorganising to form orthogonal street pattern Neighbourhood Level Design Development

131


7.1 Initial Experiments

Categorisation of Criterion

Category 1 Geometry based Criterion

Original

Deformed

Aggregated

Geometry

Geometry

Geometries

12.0%

10.5%

12.0%

Open Space Ratio

* Open space ratio

Semi-public Area Total Built Area

Prototype

As we look into the quantified spatial parameters we realised that those parameters are to be divided into several categories and should be introduced in different stages of the design as their characteristics varies from each other. The spatial parameters could be divided into three categories: geometrical related, geometrical & adjacent condition related and site specific ones. The geometrical related parameters are those who will be only affected by

Geometry Distortion

local geometry deformation however will remain constant in value afterwards during aggregation process. Therefore for these parameters, the optimisation is a once-and-for-all process and they don not need to be re-adjusted at a later level.

Geometrical Related Parameters: South Facing Surface Ratio Enclosure Value Open Space Ratio

Adjacent Condition

Circulation length

Block Level Open Space Ratio

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Cluster Level Open Space Ratio

Neighbourhood Level Open Space Ratio


Category 2 Geometry + Adjacent Condition based Criterion

Sky View Factor

* Sky View Factor

Original

Deformed

Aggregated

Geometry

Geometry

Geometries

0.74

0.61

0.59

Visible Sky Area Projected Area

Prototype

The second type is related to both geometrical deformation and adjacent conditions during the aggregation. Therefore those parameters are to be taken care of at every stage. For example, the sky view factor is a intuitive parameter that describes the openness of a space. It could be aďŹ&#x20AC;ected when the block geometry is nonuniform scaled or because of occlusion geometries adjacent to it. Geometry Distortion

Geometrical & Adjacent Condition Related Parameters: Sky View Factor Shaded Area Proportion Adjacent Condition

Block Level Sky View Factor (SVF)

Cluster Level Sky View Factor (SVF)

Neighbourhood Level Sky View Factor (SVF)

Neighbourhood Level Design Development

133


Category 3 Site Specific Criterion

Prototype

Geometry

Geometry

Geometry

in Beijing

in Mumbai

Incident Solar Radiation

102 kWh/m2

131 kWh/m2

The last category are those parameters which are site specific and can only be applied when site, location and the neighbouring morphology are fixed. Those parameters are to be addresses in the last design stage as they are the primary factors of the morphological differentiation. Take incident solar radiation for example, the block geometries in Mumbai are aimed to minimise solar accessibility during the overheated period while the opposite is true for blocks in Beijing. According to such difference in local climate, distinct target values are set respectively according to the local climatic statistics.

Site Specific Parameters: Incident Solar Radiation Local Architectural Features

Block Level Incident Solar Radiation

Incident Solar Radiation 134

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Cluster Level Incident Solar Radiation

Neighbourhood Level Incident Solar Radiation


Distribution of Parameters in the Process

Block Level

Cluster Level

Sky View Factor (SVF)

Sky View Factor (SVF)

Sky View Factor (SVF)

Open Space Ratio (OSR)

Enclosure Value (EV)

Programmatic Distribution

Connectivity

Network/ Spatial Hierarchy

South Facing Surface Ratio

Neighbourhood Level

Circulation length (CL)

Solar/ Shading Property

Quality Attributes

Floor Area Ratio (FAR)

Floor Area Ratio (FAR)

Floor Area Ratio (FAR)

Density Attributes

The three categories of criteria come into play at different stages of generation. The analysis informs us about which kind of modification or condition would affect which parameter. The logics in the process would therefore be considerate of these aspects that vary for each parameter. The diagram above shows the distribution of parameters that has been followed in the process so far. It shows the parameters that need to be optimised at each level and also the kind of parameters that need to be considered for the neighbourhood generation. Neighbourhood Level Design Development

135


Bottom Up Emergence

AGGREGATION LOGIC

Convergence

Experiment I Experiment II

Top - Down Control

ARCHITECTURAL AMBITION

Experiment III

DIFFERENTIATED AGGREGATION

BEIJING SPECIFIC CRITERIA

136

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MUMBAI SPECIFIC CRITERIA


7.1 INITIAL EXPERIMENTS

In the experiments we aim at understanding the attributes of density and quality which may get aďŹ&#x20AC;ected during the aggregation process. These experiments are intended to shape the design logic for the neighbourhood level generation. Aggregations that vary in terms of organisation of clusters, number of clusters and typology of clusters are tested for various conditions and emergent properties. 7.1.1 Experiment I - Density Variation and Emergent Spaces

138

7.1.2 Experiment II - Cluster Adjacencies in Aggregation

142

7.1.3 Experiment III - Cluster ModiďŹ cation for Boundary Adaptation

144

Neighbourhood Level Design Development

137


7.1.1 Experiment I

Density Variation and Emergent Spaces Experiment set up

Aim :

To calculate the highest possible density in terms of Floor Area Ratio and Population Density

Inputs :

Interstitial Void Spaces / Emergent Spaces The area and attributes of emergent spaces formed due to aggregation of blocks / clusters

Typology 2 (Equal Weightage of Quality and density), Typology 3 (Weightage on Density)

Variables : Type, Number and Location of Clusters

Neighbourhood Floor Area Ratio (FAR)

Total Built Area Plot Area

Built Area of Cluster Typology 2 : 30551 sq.m

Criteria :

Built Area of Cluster Typology 3 : 38739 sq.m

Qualities Analysed : Neighbourhood Floor Area Ratio (FAR) Population Density (ppl / Km2) Interstitial Void Spaces or Emergent Spaces

Population Density

Number of Residents 1 Sq.Km

Population in Cluster Typology 2 : 1528 Population in Cluster Typology 3 : 1937

Aggregation Method : Peripheral Packing

Fig 7.1.1 Peripherial Packing Logic

Experiment 1

138

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The experiment adopts the peripheral packing method to

The aggregations consistently show a maximum FAR of 1.8 which

generate aggregations that vary in terms of density and the kind

would accommodate a residential population density of up to

of interstitial spaces. The experiment establishes the theoretical

70,000 persons / sq.km. This density is comfortably higher than

maximum density that the system can achieve in terms of both

the city wide densities of Mumbai and Beijing however as the area

Floor Area Ratio and Population Density. For experimentation

does not include non-residential functions the density value is

typical examples of Typology 2 and Typology 3 Clusters are used

only theoretical. High density is seen in most of the tightly packed

for the generation as these provide the highest cluster densities.

generations which show the higher packing efficiency and lower

The geometrical iterations are also measured in terms of the

void spaces. The other loosely packed generations show emergence

interstitial spaces which are the by-products of the generation.

of interstitial spaces. Depending on the distribution, configuration

The experiment analyses iterations that vary in terms of number

and area of these, the spaces can be used to induce programmatic

of clusters, typology of clusters and the type of interstitial spaces.

and spatial variation. This aspect is discussed further in the next

Examples for the iterations are shown in the figure.

topic.


Footprint Ratio :

97%

Footprint Ratio :

91%

Footprint Ratio :

81%

Approx FAR :

1.84

Approx FAR :

1.66

Approx FAR :

1.48

Approx Density :

76646 ppl / sq.km.

Approx Density :

69148 ppl / sq.km.

Approx Density :

61650 ppl / sq.km.

Emergent Space Area : 2100 sq.m

Emergent Space Area : 5800 sq.m

Emergent Space Area : 6148 sq.m

Footprint Ratio :

96.7%

Footprint Ratio :

90.7%

Footprint Ratio :

81%

Approx FAR :

1.83

Approx FAR :

1.59

Approx FAR :

1.42

Approx Density :

77067 ppl / sq.km.

Approx Density :

68841 ppl / sq.km.

Approx Density :

61480 ppl / sq.km.

Emergent Space Area : 3100 sq.m

Emergent Space Area : 9339 sq.m

Emergent Space Area : 12885 sq.m

Footprint Ratio :

97.2%

Footprint Ratio :

89.9%

Footprint Ratio :

76%

Approx FAR :

1.77

Approx FAR :

1.62

Approx FAR :

1.39

Approx Density :

75865 ppl / sq.km.

Approx Density :

68520 ppl / sq.km.

Approx Density :

58792 ppl / sq.km.

Emergent Space Area : 4728 sq.m

Emergent Space Area : 12131 sq.m

Emergent Space Area : 26867 sq.m

Footprint Ratio :

97.8%

Footprint Ratio :

87.8%

Footprint Ratio :

78%

Approx FAR :

1.81

Approx FAR :

1.58

Approx FAR :

1.39

Approx Density :

73730 ppl / sq.km.

Approx Density :

65815 ppl / sq.km.

Approx Density :

57901 ppl / sq.km.

Emergent Space Area : 7081 sq.m

Emergent Space Area : 16321 sq.m

Emergent Space Area : 31500 sq.m Neighbourhood Level Design Development

139


7.1.1 Experiment I

Analysis of Emergent Spaces

Example of Aggregation Type 1

Example of Aggregation Type 2

Aggregation Type 1 Characteristics

Aggregation Type 2 Characteristics

140

Tight Packing with higher FAR value of 1.8

Packing with relatively lower FAR of 1.6

Even Distribution of Emergent Spaces

Linear conďŹ guration of central Emergent Space

Sizes similar to Local Squares already existing in the clusters

Adds another level of hierarchy to public spaces

Emergent Spaces can be used for open spaces like parks, avenues, gardens

Spaces can be used for new small scale functions like household retail, parks, restaurants

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Example of Aggregation Type 3

Emergent Space Analysis

Aggregation

Aggregation

Aggregation

Type 1

Type 2

Type 3

Average FAR

1.8

1.6

1.4

% Area of Emergent Space to Plot

6%

11%

20%

Even Distribution, Small Average Size

Linear Space, Single Medium size space

Central Space, Single Large Size space

Local Squares with Parks or Gardens

Small Blocks like household retail shops

Blocks with large usable areas like malls

Spatial Attribute of Emergent Space

Possibility of Added functions

Experiment 1 shows emergence of interstitial spaces that vary in terms of area, distribution and configuration. The spaces generated can be assessed and divided into three categories according to their characteristics. The types of spaces form a gradient from small evenly distributed spaces to large central spaces. Each type could be adopted to serve a different programmatic function. The table shows the comparative analysis of three kinds of aggregation that generate the three kinds of spaces. The table shows the functional aspects that can be adopted in each type of space.

TYPE 1 SPACES :

Spaces that are similar in size to central squares in clusters. These can accommodate local squares, small parks and gardens.

TYPE 2 SPACES :

Spaces that can accommodate single buildings or group of small blocks for entertainment or small commercial functions like shopping centres and malls.

TYPE 3 SPACES :

Large Spaces that can accommodate groups of buildings like Commercial Centres, Commercial Offices and Hotels.

The spaces can accommodate a range of functions that vary from small retail Aggregation Type 3 Characteristics Packing with FAR of 1.4 Centralised configuration of Emergent Space

outlets to large and central business centres. Depending on the desirability of programmatic use on the site, different kinds of spaces can be emphasized in the design logic. A deliberate attempt to generate these kinds of spaces would be made, as these offer opportunities to embed programmatic differentiation

Adds another level of hierarchy to public spaces

within the neighbourhood fabric. This is important to the design logic as

Additional functions can be plugged in as commercial, entertainment or retail blocks

morphologies generated so far are only residential. Neighbourhood Level Design Development

141


7.1.2 Experiment II

Cluster Adjacencies in Aggregation

Experiment set up Aim :

To calculate the effect on the parameters

Parameters Not Affected by Adjacent Blocks

of quality at block level when different typologies of clusters are aggregated.

Inputs :

Semi-public Area Total Built Area

Enclosure Factor (EV)

Facing Surface Area Semi-public Space Area

Circulation Length (CL)

Shortest route to connect the topmost semi-public space to ground

Clusters of Typology 1, 2 and 3

Variables : Location of Clusters and the typology of adjacent cluster.

Criteria :

Open space Ratio (OSR)

Qualities not affected by Non Uniform Scaling :

South facing Surface ratio ( SFR)

South facing surface area Total surface area

Parameters Affected by Adjacent Blocks

Open Space Ratio (OSR) Enclosure Factor (EV) South Facing Surface (FAR) Circulation Length (CL) Qualities affected due to location of an adjacent cluster: Sky View Factor (SVF) Solar Shading (SVF)

Sky View Factor (SVF)

Visible Sky Area Projected Area

Section a - a’

Proportion of Area shaded during the day

Section b - b’

Experiment 1 Position of Plots The experiment is aimed at investigating cluster – cluster

Depreciation up to 10% is tolerable in most of the clusters of

relationship and effects on quality attributes during aggregation.

Typology 1 (Q) and Typology 2 (QD) as these have higher weighting

The semi-public spaces occurring on the periphery of the cluster

on quality. However the same is not applicable on clusters of

have a high probability of getting affected when two clusters are

Typology 3 (D) which possess only threshold level quality. Also

placed adjacent to each other.

as the height of Cluster Typology 3 is more, the depreciation seen when two of these clusters are placed adjacent is 14% which is

The parameters like sky-view factor and solar shading are the

comparatively high. For the design it is therefore advisable to avoid

most affected by surrounding context. The experiment documents

these clusters to exist next to each other.

various permutations – combinations of adjacencies of different cluster typologies and positions of these clusters. The parameters are checked for the affected spaces and the percentage depreciation of quality is noted. 142

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1.1

1.2

Cluster Type : Q - Q

Cluster Type : Q - QD

Number of Affected Semi - Public Spaces : 9 Change in Average Score for SVF : -0.03 Change in Average Score for Shading : -0.01 Change in Score for Cluster Quality : -0.1

PERCENTAGE CHANGE IN QUALITY

- 01 %

Number of Affected Semi - Public Spaces : 10 Change in Average Score for Shading : -0.02 Change in Score for Cluster Quality : -0.1

1.4

Cluster Type : Q - D

Cluster Type : QD - QD

Number of Affected Semi - Public Spaces : 13 Change in Average Score for SVF : -0.07 Change in Average Score for Shading : -0.03 Change in Score for Cluster Quality : -0.3

PERCENTAGE CHANGE IN QUALITY

- 03 %

1.5

Change in Average Score for SVF : -0.12 Change in Average Score for Shading : -0.14 Change in Score for Cluster Quality : -0.8

Number of Affected Semi - Public Spaces : 9

- 01 %

PERCENTAGE CHANGE IN QUALITY

Change in Average Score for SVF : -0.09 Change in Average Score for Shading : -0.05 Change in Score for Cluster Quality : -0.5

- 04 %

1.6

Cluster Type : QD - D Number of Affected Semi - Public Spaces : 15

IN QUALITY

Change in Average Score for SVF : -0.03

1.3

PERCENTAGE CHANGE

Cluster Type : D - D PERCENTAGE CHANGE IN QUALITY

- 08 %

Number of Affected Semi - Public Spaces : 15

IN QUALITY

Change in Average Score for SVF : -0.28 Change in Average Score for Shading : -0.20 Change in Score for Cluster Quality : -1.4

PERCENTAGE CHANGE

- 14 % Affected Semi Public Spaces

Neighbourhood Level Design Development

143


7.1.3 Experiment III

Cluster Modification for Boundary Adaptation

Experiment set up Aim :

To

adapt

compact

aggregation

approach of clusters to site specific geographic constraints.

Inputs :

cluster type 1 cluster type 2 cluster type 3 geographic boundary

Variables :

Number of clusters Location of clusters Number of blocks to discard Boundary Condition

Criteria :

Adaptation

level

to

geographic

constraints Density Capacity

Restricted Flexibility to Clusters which affects tight packing

Flexibility induced by allowing block deductions

Example of Sequential Deduction of Blocks in the aggregation process

Position of Plots The experiment looks into modification of clusters by deletion of

related to the number of blocks in the clusters. However the effect

blocks. This would help the clusters to adapt to restricted geometric

on the geometries that define the characteristics of clusters: the

boundaries and create tighter and more efficient aggregations.

Chowk like central space and Hutong like street patterns get

This type of modification to clusters would allow higher flexibility

affected. Contrarily there is a positive rise in Sky-View factor as

to clusters in site specific scenarios. The experiment deals with

some of the neighbouring blocks are removed. However there is

removing various blocks and checking the effect on the parameters.

a drastic change in FAR value (density attribute) when peripheral

The diagram shows examples of various removed blocks, the

blocks are removed, this is because these are generally the largest

affected parameters and the percentage change in density and

blocks and hold the highest density value.

quality scores. Therefore, for the design process the modification allowed would

144

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Most of the quality attributes are not negatively affected by deletion

be deletion of only 1 or 2 peripheral blocks which would ensure

of blocks. This is because most of the attributes are not directly

flexibility as well as not affect optimised attributes drastically.


CLUSTER TYPOLOGY 1 : Q

C_Q_01

CLUSTER TYPOLOGY 2 : QD

C_QD_01

CLUSTER TYPOLOGY 3 : D

C_D_04

Affected Parameters

Affected Parameters

Affected Parameters

Sky View Factor (SVF) Floor Area Ratio (FAR)

Sky View Factor (SVF) Floor Area Ratio (FAR)

Sky View Factor (SVF) Floor Area Ratio (FAR)

Affected Parameters

Affected Parameters

Affected Parameters

Sky View Factor (SVF) Solar Shading Centralised network pattern

Sky View Factor (SVF) Solar Shading Centralised network pattern

Sky View Factor (SVF) Solar Shading Centralised network pattern

Floor Area Ratio (FAR) Continuous ring streets

Floor Area Ratio (FAR) Continuous ring streets

Floor Area Ratio (FAR) Continuous ring streets Neighbourhood Level Design Development

145


146

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7.2 SITE STUDY

The experiments are aimed at studying the attributes of density and quality which may get aďŹ&#x20AC;ected during the aggregation process. These experiments are intended to shape the design logic for the neighbourhood level generation. Aggregations that vary in terms of organisation of clusters, number of clusters and typology of clusters are tested for various conditions. 7.2.1 Beijing - Site Overview and Analysis

150

7.2.2 Mumbai - Site Overview and Analysis

154

7.2.3 Conclusions and Site SpeciďŹ c Ambitions

158

Neighbourhood Level Design Development

147


SITE CONTEXT

148

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The design so far has dealt with generating prototypes that are

The system would be applied to two distinct locations where the

suitable in terms of parameters which are common in both Mumbai

flexibility of the system to adapt to different conditions, design

and Beijing. To develop the system further the intention is to test

ambitions and site demands would be analysed and assessed. The

the relevance and suitability of the system for application in site

design would deal with a neighbourhood of approximately 0.8 to 1

specific and differentiated conditions. The prototypes need to adapt

Sq.Km Area. The sites would be located in Mumbai and Beijing to

to new locational circumstances arising during the application

deal with different urban scenarios and context. This level of design

at a larger level, which would incorporate aggregation of cluster

development would look into sites which are distinct in term of

on two different settings. The aim is to generate differentiated

Boundary Conditions, Density Requirements, Environmental

architectural geometries with the same application logic.

Factors and Existing Architectural Character around the site.


Boundary Conditions Boundary conditions refer to geometric and geographical constraints existing around the site. These would include elements like roads, streets and water-bodies that bound the site. It is an elementary requirement of the design system to adapt to different boundary conditions and still generate a relevant design proposal. Further the system needs to show flexibility in term of reacting to boundary conditions to alter accessibility and porosity of site depending on situations.

Density Requirements The design logic can be successful only if it can deal with multitude of density requirements. The challenge is not only to tackle extremely high density situations but also to tackle situations where density is not primary criteria where spatial quality is of significance and the system requires to generate spaces that can accommodate non-residential functions.

Environmental Factors Selecting environmentally different sites drives the blocks to react and modify differently to each local environmental conditions. This aspect would predominantly affect the block morphology and create differentiation.

Existing Architectural Character The architectural character of the surrounding spaces is one of the most significant contexts. The ability of the design system to adapt to different architectural characters in terms of existing urban fabric, incorporating the need of the location in terms of programmatic use and creating spatial order that reflects the local context would determine to a large extent the appositeness of the design.

Neighbourhood Level Design Development

149


7.2.1 Site Study Overview Beijing

Walled City (Urban Core) Site Fig 7.2.1 Urban Area Map of Beijing 150

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Urban Fabric

SITE

Indigenous Fabric

Fig 7.2.2 Juxtaposed Contexts Adjacent to the Site

Beijing Beijing, owing to the booming Chinese economy in the recent years is undergoing

Xuanwumen St.

rapid urban transformation and growth. The shift from the indigenous built forms to westernised / urban typologies is drastic and does not take into account the local context. The examples of these contrasting situations are seen throughout the city where the two

RONGSHU

Caishikou St.

types of built environments are always in conďŹ&#x201A;ict. The demographic and developmental SITE

Area : 0.83 km2

DASHILAR

pressure forces the indigenous character to cease and convert into multi-storey high rise forms that better suit the density requirements. To contain and reduce this phenomenon, there have been government initiatives to protect and conserve certain pockets of these local settings still existing in the city.

Luomashi St.

Therefore, the design experiment aims to create a synchronous between these two opposing built characters. The site is located within the urban core of the city; it presents a strong context of surroundings that represent the historical / local aspects as well as growing real estate sector. The juxtaposition provides interesting context for urban development which would need to stitch the urban divide posed by these diďŹ&#x20AC;erentiated

Site Location

fabrics. Neighbourhood Level Design Development

151


7.2.1 Site Study Analysis Beijing 1 . Existing Juxtaposition of Built Fabrics The site is surrounded by primarily two kinds of built architectural characteristics: on the western half are the recently developed high – rise high density typology of residential buildings and on the eastern side are the low-rise indigenous typologies having historical significance and are under conservation and redevelopment. There is stark contrast in block sizes, average heights, residential Fig 7.2.3 Highrise Residential Typologies

Fig 7.2.4 Traditional Courtyard Residential Typologies

DASHILAR

RONGSHU

density, network density and spatial aspects in both the fabrics. The embedded characteristics of indigenous settings provide higher significance to hierarchy of spaces and therefore there is prominent individuality seen in the architecture of their semipublic and public spaces that exists as Courtyards and Hutongs respectively. In contrast the urbanised residential parts are devoid of local characteristics and are designed with singular viewpoint of providing higher floor space for the increasing density. As a repercussion the buildings are bigger, higher and don’t respond to human scale and proportion, the difference in scale greatly affects streetscapes and the activities taking place in them. Table on the left shows the comparative analysis of the two situations existing around the site.

URBAN FABRIC

SITE

INDIGENOUS FABRIC

30 x 30 M to 80 x 80 M

6 X 6 M to 25 X 25 M

15,000 M2

5,000 M2

Fabric Type : Urban fabric is dominated by wide roads with lower network density whereas the indigenous fabric defined by small streets with higher network density.

Average Building / Block Sizes : There is significant difference in the sizes of building in the two condition. The urban block areas generally with large foot prints and segregated. In contrast the blocks in indigenous settings are small and intimately placed.

Plot Sizes : The plot sizes reflect the block sizes of respective types

however in Urban Fabric 1 to 4 buildings occupy the plot whereas in the indigenous one 20 -30 smaller blocks occupy the same plot

72 24 3 24 to 72 M

The Rongshu Location is dominated by Residential High Rise Typology common to new development in Beijing 152

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12

3 to 12 M

The Dashilar Area is under government preservation to protect the last remainders of indigenous spatial aspects.

Height Variation : High rise typologies are the norm for any new development so the urban fabric is dominated by theses in contrast to the other fabric.

The contrasting patterns in the two sites pose a design challenge where the intevention would have to take into account characteristic of both sites as an influence for the design.


2 . Urban Demographic Demands Population Density

15k 10k

typologies need to be explored.

2020

high and in this respect high quality - high density residential

2010

urban core demographic and developmental pressure would be

10k

1300

0

2000

5k

40k 30k 20k

1990

floor space needs to be explored. As the site is located in the

20k

1980

in population new opportunities to generate more usable

29550

25k

1970

persons / sq.km. To sustain and accommodate this increase

Built Area in sq.m

29000 persons / sq.km and is projected to increase to 42000

30k

90k 80k 70k 60k 50k

Projected For Walled City (2025)

walled city of Beijing where the density, on an average is about

35k

Walled City

residences and businesses. This aspect is prominent in the

42000

40k Population Density per km2

population and as a result higher demands for floor spaces for

Increase in Usable Built Space in Walled City

45k

escalation of urbanisation. This has resulted in higher urban

Beijing

The growing economy of China has resulted in multi-fold

Fig 7.2.5 Demographical Pressure in Beijing Numeric Data adapted from http://en.wikipedia.org/wiki/Beijing under topic of Government, Politic and Growth and from http://www.bjinvest.gov.cn/english/gn/ under topic of Beijing Real Estate Development,Investment Forum

3 . Environmental Aspects Beijing

has a rather dry,

monsoon-influenced

humid

continental climate characterized by hot, humid summers, and

Annual Temperature Variation 40

extremely cold, windy, dry winters. It is during this time that the minimum temperature falls below 0 degree Celsius. During this time the outdoor daily activities get restricted. The design would therefore need to target this time period so the semipublic spaces have a year round functionality.

31 30

32

31

22

21

27 21

19

20 8

6 2

0

20 15

14

13 10

26

7

1

10 4 -1

-5

-6

-9 - 10

Avera ge High T emp (°c)

4 . Programmatic aspects

Decem ber

Novem ber

O ctober

Septem ber

A ugus t

July

June

May

A pr il

Mar ch

Febr uar y

Januar y

- 20

Avera ge Low T emp (°c)

Fig 7.2.6 Annual Temperature in Beijing

The site surrounding has two contrasting characters even in terms of programmatic use. On the Rongshu side is a residential area but on the Dashilar side is mixed use setting having residential, commercial, and retail based programmes. The chief among them are the home based small scale industry units that attract a lot of tourism. These units focus on handicrafts and other household goods. The distribution of these units ensures constant pedestrian activity in the area keeping the area activated. This aspect will influence the programmes in the site.

Fig 7.2.7 Local Shop for Daily Items

Fig 7.2.8 Handicraftsman in Beijing Neighbourhood Level Design Development

153


7.2.2 Site Study Overview Mumbai

THANE

ISLAND CITY OF MUMBAI (URBAN CORE)

Island City (Urban Core) Site Fig 7.2.9 Urban Area Map of Mumbai 154

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Fig 7.2.10 Homogeneous Urban Scenario

Percentage Population Dependent for occupation on Mumbai

Proportion of Thane Population having economic dependence on Mumbai

37%

63% Population of Thane : 1.8 Million A large portion of the population is employed in Mumbai City

Persons commuting daily form Thane to Mumbai : 1.1 Million

Thane, Mumbai The site selected for Mumbai is located in Thane suburb on the outskirts of the city. Thane is one of the main suburbs which came into existence as â&#x20AC;&#x2DC;Bedroom Economiesâ&#x20AC;&#x2122; that had a symbiotic relationship with the main city of Mumbai. These suburbs developed due to the rising real estate and living costs within the main urban core. They provided alternate and cheaper residential options located close the city. Majority of the population travels to and fro from the main city on a daily basis for their livelihood. Therefore the urban scenario in these locations shows programmatic homogeneity of mid-rise residential typology. Due to the increased demographic pressure on the main city, government has taken initiatives to develop these peripheral suburbs, to reduce pressure on the city infrastructure. Thane, represents one such suburb where there has been a radical growth in the population. The Design would be tested in a site in Thane to respond to the context and generate not only typological but also programmatic variation in the Fig 7.2.11 Dependence on Urban Core : Daily Commuters to Mumbai City

chosen location. Neighbourhood Level Design Development

155


7.2.2 Site Study Analysis Mumbai 1 . Homogeneous Programmatic Use Thane shows an urban scenario that is dominated by mid-rise, low income group housing with very limited variation in occupational programmes. This location developed due to rising real estate and living costs within the main city. It provided alternate and cheaper residential options located in close proximity to the city. High percentage of the population depends of the Mumbai city and approximately 1million people travels to and from Mumbai on daily basis for their livelihood. This has led to development of only residential units which has resulted in an urban fabric that shows programmatic and typological homogeneity. Fig 7.2.12 Homogeneous Residential Typologies

Land-use Pattern in Thane City

03%

Open Space 0.8 Sq.M / Person

19%

78 %

Housing for Medium and High Income Groups Affordable housing

Housing Housing

71.0%

Offices Offices

10.3%

Retail Retail

4.0%

Institutional Institutional 5.7% Industrial Industrial

2.4%

Open Space Open Spaces 3.1% Parks Parks

3.5%

Existing Constraints

te

Sta

ay hw Hig

35

The site is strategically located at a junction of two roads leading into the city. This holds significance as this makes the site easily accessible. This would further help in introducing programmatically different function in the site which can be accessed by a large number of resident populations. This would be important consideration for developing this site, as this would not only create variation in terms of programmes but Nationa l

Highwa y3

The site is in proximity to two main roads leading into the city.

also decrease the dependence on the urban core of Mumbai. The site to hold significance in the location and to react to the homogeneous context would need to create differentiation and in the process generate opportunities for the residents to get

Main Roads 156

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employed in the same location and thereby reduce commuting.


2 . Urban Demographic Demands Population Growth between 2001 to 2011

have led to a phenomenal rise in the population of Thane. The

10

4.3%

0

The design would not only need to consider the rise in demands for residential floor spaces but also the demands to accommodate varied programmes.

15900

10k 5k 0 Projected Density

increase to equal that of Mumbai in the next 20 years.

15k

Thane

pressure on the main city. The projected average density is said to

17.6%

Mumbai

population and reduce the dependence and infrastructural

20

25000

24380

20k

Pop. Density / km2

businesses are moving into the fringe cities, closer to the working

25k

THANE

The city faces a shift in programmatic use as more and more

35.2% 30

Mumbai

of the Mumbai City and twice that of national average.

Density Comparison

40 Percentage Population Growth

growth rate in Thane is 35.2 which is approximately 9 times that

National Average

Government incentives and rising real estate cost in the main city

Fig 7.2.13 Demographical Pressure in Mumbai Numeric Data adapted from http://www.census2011.co.in/census/district/355-thane. html and http://www.census2011.co.in/census/metropolitan/305-mumbai.html under the topics of National Growth Rate

Annual Temperature Variation 36

33

Mumbai’s climate can be best described as moderately hot with 28

ensures temperatures won’t fluctuate much throughout the year, 25

24

26 25

25

25

24 21

18

Avera ge High T emp (°c)

Decem ber

Novem ber

O ctober

A ugus t

July

15 Septem ber

climatic aspects.

26

18

Febr uar y

need to reference the indigenous settings for their adaptability to

17

Januar y

which is not common in recent developments. The design would

32

21 20

however the hot summers affect any outdoor activity. The indigenous settings show consideration for this aspect something

26

24

about 36 °C in summer, while the mean average temperature during winter is 20.5 °C. This ensures comfortable winters

27

25

A pr il

the mean average of 27.2 °C. The mean average temperatures in

32

29

30

Mar ch

high level of humidity. Its coastal nature and tropical location

34

31

May

The Climate of Mumbai is a tropical wet and dry climate.

35

June

3 . Environmental Aspects

Avera ge Low T emp (°c)

Fig 7.2.14 Annual Temperature in Mumbai

4 . Programmatic aspects The city has seen radical growth of BPO (Business Process Outsourcing) sector. This sector is independent of any site dependences and therefore can be placed anywhere, where skilled work force is available. The government has encouraged growth of this sector as it can provide large scale employment to the local population. The corporate feature of this sector ensures low pressure on infrastructure as compared to any other industrial setup. This is new developing trend of the city and therefore would be an important consideration for creating programmatic variation in the site.

Fig 7.2.15 Local Shop for Pottery Supply

Fig 7.2.16 Modernised Working Space Neighbourhood Level Design Development

157


7.2.3 Site Study Conclusions and Site Specific Ambitions Beijing

Integrating the Contrasting Fabrics The site in Beijing is set in a juxtaposition of contrasting indigenous and urban fabric. There is stark contrast in block sizes, average heights, residential density, network density and spatial aspects. The urbanised part holds high-rise high density residential typologies and the indigenous settings are under preservation. The chief ambition for the Beijing site is to create a gradient configuration that would integrate the contrasting contexts. This would be established by creating a smooth transition of network, density and built forms. The site would need to incorporate aspects of both fabrics to generate a seamless transition. The network system would need to connect to the URBAN FABRIC

SITE

INDIGENOUS FABRIC

existing streets and roads.

Network and Density gradients

would be the primary aspects considered during the generation.

Density and Programmatic Use

50,000

Target Density : 50,000 persons/ sq.km

persons / sq.km

As the site is located in the urban core the target residential density would need to correspond to the projected value of the location. The density, the system would try to achieve is 50,000

Target Floor Area Ratio : 2.0

persons / sq.km. This would ensure that the proposal is at par with the new developments in the city. Therefore the corresponding

Number of Clusters : 26 - 28

FAR to achieve this density is 1.8. The possibility to achieve this value has been established in the initial experiments. The aggregation would require 28-30 clusters. To ensure variation in

Predominantly residential programmes with household retail

morphology the system would try to use equal number of each typology.

Environmental Responses k W h / m2

The system would recursively look into block levels to enforce

5 5 0 0 .0 5 0 0 0 .0

environmental compatibility. Parameters like Incident Solar

4 5 0 0 .0

Optimise aspects like Incident Solar Radiation on Open Spaces

4 0 0 0 .0 3 5 0 0 .0 3 0 0 0 .0 2 5 0 0 .0

Maximise South Facing Surfaces

2 0 0 0 .0 1 5 0 0 .0

5 0 0 .0

158

block modification in relation with the already optimised parameters. For Beijing the criteria would be to create comfortable environmental condition during the harsh winter season. Therefore the modification would tend to maximise solar access.

1 0 0 0 .0

0 .0

Radiation and South Facing surfaces would be used to enable

Ja n

Fe b

M ar

Apr

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M ay

Ju n

Ju l

Aug

Sep

Oc t

N ov

D ec


Mumbai

Generating fabric that optimises connectivity The site in Mumbai shows a programmatic homogeneity of residential which depends on the daily commuting with Mumbai city. The Chief aim for this site would be to create a design which would be differentiated from the homogeneous residential typologies. The ambition would be to create a centralised pattern that would link the periphery to the site centre. And at the centre the proposal would be to create a programmatically different use in the form of commercial or business centre. This would be embodied in an imposing building created by using tools of the same system. The site would be generated with a density gradient that relates the surrounds to the site. Network would be leading to the central core with increasing network density towards it to ensure maximum accessibility.

Density and Programmatic Use The site would target a residential density of 30,000 persons/

30,000

Target Density : 30,000 persons/ sq.km

persons / sq.km

sq.km. This is marginally higher than the projected density for the area. In this site quality attributes would be weighed

Target Floor Area Ratio : 1.5

higher as the pressure on density attributes are lower than that of Beijing. The target FAR for residential development would be around 1.2. This value for the site will increase when new programmes would be added. The development is aimed to be mixed use development with higher weightage on differentiated programs that would not only generate employment for 70% of the residing population to decrease pressure on commuting but also add morphological variation.

Number of Clusters : 21 - 23 Mixed Use Development with high percentage of commercial functions Occupational Opportunity for 70% of the population

Environmental Responses k W h / m2

The blocks for Mumbai, like Beijing, would recursively optimise

5 5 0 0 .0

environmental parameters of incident solar radiation. Due

5 0 0 0 .0

to the hot climatic conditions the aim would be to create comfortable conditions during the overheated period of the summer. Therefore the modifications of the block would look into decreasing solar access. The blocks during the aggregation would modify to optimise south facing surfaces which in the case of Mumbai need to be minimised.

4 5 0 0 .0 0

Optimise aspects like Incident Solar Radiation on Open Spaces

4 0 0 0 .0 0 3 5 0 0 .0 0 3 0 0 0 .0 0 2 5 0 0 .0

Minimise South Facing Surfaces

2 0 0 0 .0 1 5 0 0 .0 1 0 0 0 .0 5 0 0 .0 0 .0

Ja n

Fe b

M ar

Apr

M ay

Ju n

Ju l

Aug

Sep

Oc t

N ov

Neighbourhood Level Design Development

D ec

159


160

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7.3 AGGREGATION

This level deals with applying clusters into the constraints posed by site speciďŹ c neighbourhoods. Aggregation method uses peripheral packing algorithm to generate various iterations for each site by taking into consideration density requirements, density gradients and emergent spaces. The intelligence of the system is derived from logics developed from the experiments as well as site speciďŹ c experiments that would be conducted in the following chapter. The iterations would be evaluated and the desired options would be selected for further working. 7.3.1 Experiment IV : Cluster Organisation

162

7.3.2 Mumbai Aggregation

164

7.3.3 Beijing Aggregation

170

Neighbourhood Level Design Development

161


7.3.1 Aggregation Experiment

Cluster Organisation Experiment set up Aim :

To produce a centralised network /

gradient

aggregation

pattern of

by

cluster

different typologies

responding to the site requirements.

Inputs :

Clusters of Typology 1, 2 and 3

C_Q_02

Variables : Position and typology of Clusters.

Criteria :

C_QD_01

C_D_04

Connectivity Analysis showing the desired patterns in terms of higher connectivity in the centre in the Mumbai Experiments and a gradient from high to low network density in Beijing from east to west

Comparative Graph of Number of Streets

Comparative Graph of Number of Streets

25

Typology 1

Typology 2

Typology 3

Clusters

Clusters

Clusters

Q

QD

D

Formal Streets

12

11

11

Informal Paths

11

07

05

Total

23

18

16

Number of Streets

20 Informal Paths

15

Formal Streets

10 5 0

Q

QD

D

Fig 7.3.1 Network Comparison of Q/QD/D Clusters

Experiment IV Cluster Organisation The experiment investigates organisational logics of different

Mumbai site the aim is to generate a centralised pattern with higher

typologies of clusters to generate the desired network patterns.

network density for the central core. For Beijing a linear gradation

The experiment takes into account not only the peripheral streets

is expected where the network patterns relate to the neighbouring

formed around the blocks but also the informal paths formed

context.

within them. As the cluster typologies constitute the same number

162

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of blocks, the number of peripheral streets formed is roughly the

CONCLUSION: From experiments that evaluate different

same. However, as the Quality preferred clusters accommodate

arrangements with connectivity ratios, we conclude that for

blocks with lower densities that have higher informal pathways

Mumbai the well-connected Q clusters should be in the centre

at ground level the overall connectivity of these clusters is higher

while the less-fragmental on the periphery. For Beijing the

than that of Density preferred clusters.

optimum arrangement is found to be linear gradation from D to Q

Different network patterns are desired from each of the site. For

from West to East.


MUMBAI

Q - D pattern

D - Q pattern

D

Q

QD

QD

Q

D

42 36 30 24 18 12 6 0

42 36 30 24 18 12 6 0

Connectivity Value

BEIJING

Fig 7.3.2 Decentralised Network Pattern

Fig 7.3.3 Centralised Network Pattern

Random pattern

QD

D

Q QD

Connectivity Value

D - Q pattern

Q D

D QD

Fig 7.3.4 Random Network Pattern

42 36 30 24 18 12 6 0

Connectivity Value

Q

Fig 7.3.5 Network Pattern featured with linear Gradient

42 36 30 24 18 12 6 0

Connectivity Value

Neighbourhood Level Design Development

163


7.3.2 Aggregation : Mumbai Generation Criteria

INPUTS Boundary Conditions

Clusters

Mumbai Site

Typology 1 Clusters

Typology 2 Clusters

Typology 3 Clusters

Total Area : 790000 sq.m (0.79 sq.km) Boundaries: 2 Main Roads and Waterfront

Plot Area : 21253 sq.m Avg. Population : 1280 persons Avg. FAR : 1.20

Plot Area : 20148 sq.m Avg. Population : 1528 persons Avg. FAR : 1.52

Plot Area : 19401 sq.m Avg. Population : 1937 persons Avg. FAR :

Q

QD

D

Experiments Adjacent Cluster Condition

Experiment II informs this criteria where the generation will avoid adjacent placement of clusters of typology 3 (D)

164

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Cluster Organisation

Experiment IV informs this criteria where Typology Q are placed near the centre to generate a centralised pattern.

The aggregation process aims at using the different the site

The criteria for generation in Mumbai is based on aspects like

boundaries as constraint and cluster catalogue as inputs. The

packing efficiency which is the ratio of Cluster Footprints to Total

arrangement of these clusters within the site is informed by

Site area, minimum neighbourhood FAR (Floor Area Ratio) of

3 factors, the design ambition, the logics derived from initial

1.2 which would accommodate a minimum residential density

experiments and the logics derived from site specific experiments.

of 30,000 persons / sq.km, density gradient that relates to the

These aspects are common for Mumbai and Beijing.

surrounding context of mid- rise houses and a centralised network


CRITERIA Packing EďŹ&#x192;ciency

FAR & Density

30,000

persons / sq.km

1.5

Floor Area Ratio

Total Site Area

According to ambitions for the site a residential density of 30,000 persons / sq.km is desired with total FAR of 1.5

Density Gradient

Centralised Pattern

A density gradient that relates the surrounding areas to the site. Having the highest density in the centre

A centralised pattern with a central space that allows adding new programmes to the site.

pattern that provides higher connectivity to the central core which

score. The aim is to generate a number of iterations by using the

to suit the ambition would serve as an attractor for the masses. As

peripheral packing algorithm and select the ďŹ ttest individuals for

a higher quality score is desired the weightage for adjacent cluster

evaluation and further working.

Total Cluster Footprint Area

condition (Experiment II) is set higher as this would guarantee that number of adjacent D blocks in minimum. This would however aďŹ&#x20AC;ect the overall density capacity but help the overall quality Neighbourhood Level Design Development

165


7.3.2 Aggregation : Mumbai Generation Process

Cluster Catalogue Typology 1 Clusters

Typology 2 Clusters

Typology 3 Clusters

4 Clusters

4 Clusters

4 Clusters

Q

QD

D

Design Input Boundary Conditions Mumbai Site (Thane)

Experiment 1 Adjacent Cluster Logic

Experiment 2 Cluster Organisation

Generation Criteria Packing EďŹ&#x192;ciency

Population Density / FAR / Number of Clusters

Centralised Pattern / Central Emergent Space

Generation

12 Fittest Aggregations

166

M_G_01

M_G_02

M_G_03

M_G_04

M_G_05

M_G_06

M_G_07

M_G_08

M_G_09

M_G_10

M_G_11

M_G_12

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Analysis Chart

Packing Efficiency

Population ppl / sq.km

FAR

Total Clusters

Cluster Adjacency (Score)

Void Area (Sq.M)

M_G_01

0.50

27881

1.58

21

0.7

432123

M_G_02

0.52

29154

1.57

22

1

411975

M_G_03

0.50

26579

1.49

21

0.8

427314

M_G_04

0.50

26507

1.48

21

0.8

425851

M_G_05

0.52

29154

1.57

22

0.8

411975

M_G_06

0.49

28976

1.65

21

0.2

435827

M_G_07

0.52

28947

1.56

22

1

410870

M_G_08

0.52

29082

1.56

22

0.6

410512

M_G_09

0.49

28088

1.59

21

0.8

433228

M_G_10

0.52

29154

1.57

22

0.8

411975

M_G_11

0.47

26060

1.54

20

0.9

450419

M_G_12

0.52

29154

1.57

22

1

411975

Central Space

-

-

-

-

-

Best 3 iterations are selected for the next stage Presence of a central space

M_G_xx

Unsatisfactory Values

Selected Iterations M_G_02

M_G_07

M_G_12

Evaluation

With the discussed criteria a number of iterations are generated

We also checked the proportion of the different typologies used

using peripheral packing algorithm. We realised that to achieve the

within the site, to ensure variation in morphology; attempt was to

desired density the entire site does not need to be fully occupied.

use equal number of clusters from each typology.

Emergent void spaces made up sizable portion of the land use. This aspect works in favour of the ambition as the interstitial spaces

The iterations are tabulated and scored. The score represents the

could be used to introduce new programmatic functions. The

summation of numeric data acquired for each criterion. 3 fittest

criteria to check the existence of a central emergent space also works

individuals are selected to be evaluated for their size, distribution

in tandem with the design objective to create programmatically

and configuration of emergent spaces.

different central core. Neighbourhood Level Design Development

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7.3.2 Aggregation : Mumbai Evaluation

EVALUATION CRITERIA

Distribution of different types of emergent spaces

EVALUATION 1

Consolidated and Large Central Space (larger than 30000 Sq.M) to accomodate groups of buildings for commercial programmes like offices

EVALUATION 2

Evenly distributed Medium size spaces (5000 - 30000 Sq.M) to accommodate entertainment and smaller commercial functions like malls and shopping centres

EVALUATION 3

Minimum number of emergent spaces smaller than 5000 Sq.M.

SELECTED ITERATIONS FOR MUMBAI SITE

M_G_02

M_G_07

Total Clusters Accommodated : 22

M_G_12

Total Clusters Accommodated : 22

Total Clusters Accommodated : 22

No. of Clusters of Typology 1 (Q) : 6

No. of Clusters of Typology 1 (Q) : 7

No. of Clusters of Typology 1 (Q) : 6

No. of Clusters of Typology 2 (QD) : 9

No. of Clusters of Typology 2 (QD) : 8

No. of Clusters of Typology 2 (QD) : 9

No. of Clusters of Typology 3 (D) : 7

No. of Clusters of Typology 3 (D) : 7

No. of Clusters of Typology 3 (D) : 7

Neighbourhood FAR generated : 1.57

Neighbourhood FAR generated : 1.56

Area of Emergent Spaces : 214,658

Neighbourhood FAR generated : 1.57

Area of Emergent Spaces : 215,137

Area of Emergent Spaces : 215,287

The selected aggregations for Mumbai are evaluated for the

The medium size emergent spaces (5000-30000 Sq.M) should be

feasibility of emergent spaces to accommodate new functions. The

evenly distributed through the site as these would accommodate

primary evaluation criterion is based on the central space. This

smaller functions like malls, shopping centres, theatres, schools

space needs to be consolidated, convex and large ie. greater than

etc.

30000 Sq.M . as this would need to house a group of buildings

number of emergent spaces less than 5000 Sq.M as this level of

with larger floor spaces that would serve for a business centre.

open spaces are already existing as public spaces in clusters.

The aggregation are also evaluated for having minimum

Therefore it is imperative that the space is a whole consolidated

168

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mass and not broken into branches which may limit the possibility

On this basis Aggregation M_G_12 is selected for further working

of introducing new built forms.

for Mumbai Site.


Selected Aggregation

12,411 sq.m.

5546 sq.m.

6274 sq.m.

D

QD

QD 5021 sq.m.

3162 sq.m.

Q

17,079 sq.m.

4702 sq.m.

QD

D

D 13,946 sq.m.

Q

QD

Q

D

6851 sq.m.

Q

7849 sq.m.

9076 sq.m.

70217 sq.m.

Q

D

QD

QD QD 8,982 sq.m.

8084 sq.m. 14,033 sq.m.

QD

Q 8,112 sq.m.

D QD

D

3053 sq.m. 7207 sq.m.

Fig 7.3.6 Neighbourhood Aggregation Pattern in Mumbai

Total Clusters Accommodated :

The option selected for the Mumbai site generated a residential FAR of 1.57 .The FAR value would signiďŹ cantly increase when the new programmes would be added to the emergent spaces. The aggregation obtained as the result still appears fragmented into divisible clusters. This aspect would need to be addressed at the next level of generation. Most of the criteria aimed for in this stage of generation in terms of packing, cluster organisation and density were accomplished and the emergent aspects provide opportunity to further our design ambitions.

22

No. of Clusters of Typology 1 (Q) :

6

No. of Clusters of Typology 2 (QD) :

9

No. of Clusters of Typology 3 (D) :

7

Neighbourhood FAR generated : Population Supported : Built up Area : Area of Emergent Spaces :

1.57 29,154 ppl/sq.km 699,696 Sq.M 215,287 Sq.M

Additional Area for Large Commercial Functions:

70,217 Sq.M

Additional Area for Entertainment and Small Commercial functions:

21,795 Sq.M

Neighbourhood Level Design Development

169


7.3.3 Aggregation : Beijing Generation Criteria

INPUTS Boundary Conditions

Clusters

Beijing Site

Typology 1 Clusters

Typology 2 Clusters

Typology 3 Clusters

Plot Area : 21253 sq.m Avg. Population : 1280 persons Avg. FAR : 1.20

Plot Area : 20148 sq.m Avg. Population : 1528 persons Avg. FAR : 1.52

Plot Area : 19401 sq.m Avg. Population : 1937 persons Avg. FAR :

Q

Total Area : 830000 sq.m (0.83 sq.km) Boundaries: 3 Main Roads

QD

D

Experiments Adjacent Cluster Condition

Experiment II informs this criteria where the generation will avoid adjacent placement of clusters of typology 3 (D)

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Cluster Organisation

Experiment IV informs this criteria where Typology Q are placed on the east side and Typology on the opposite side

A similar process for generation is followed for Beijing where

accommodate a population density of around 60,000 persons /

Clusters and Boundary Conditions would be part of the inputs

sq.km. This would appease the demographic pressure the urban

and the generation would be guided by logics and criteria of

scenario faces. To achieve this, a higher number of Density-

the developed in the experiments. The generation in Beijing is

preferred clusters would be used. The generation logic also reduces

expected to a achieve the maximum possible density. The process

the weightage on Adjacent Cluster Conditions so as accommodate

is approximately looking at an FAR of 2.1 to 2.5 which would

higher density which is the primary criteria for aggregation on


CRITERIA Packing EďŹ&#x192;ciency

FAR & Density

50,000

persons / sq.km

2.0

Floor Area Ratio

Total Site Area

According to amibitions for the site a residential density of 50,000 persons / sq.km is desired with total FAR of 2.0

Adjacent Cluster Condition

Gradient Pattern

Total Cluster Footprint Area

A density gradient that relates the surrounding areas to the site. Highest density towards west decreasing toward East

Gradation of fabric in terms of Network from East to West

this site. Apart from factors of packing eďŹ&#x192;ciency the criteria for generation also looks at creating a density and network gradient ascending from east to west to match the context by appropriate cluster arrangements. Similar to the process applied for Mumbai, a number of iterations would be generated.

Neighbourhood Level Design Development

171


7.3.3 Aggregation : Beijing Generation Process

Cluster Catalogue Typology 1 Clusters

Typology 2 Clusters

Typology 3 Clusters

4 Clusters

4 Clusters

4 Clusters

Q

QD

D

Design Input Boundary Conditions Beijing Site (Dashilar)

Experiment 1 Adjacent Cluster Logic

Experiment 2 Cluster Organisation

Generation Criteria Packing EďŹ&#x192;ciency

Population Density / FAR / Number of Clusters

Density / Network Gradient

Generation

12 Fittest Aggregations

172

B_G_01

B_G_02

B_G_03

B_G_04

B_G_05

B_G_06

B_G_07

B_G_08

B_G_09

B_G_10

B_G_11

B_G_12

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Analysis Chart

Packing EďŹ&#x192;ciency

Population ppl / sq.km

FAR

Total Clusters

Cluster Adjacency (Score)

Void Area (Sq.M)

B_G_01

0.64

43194

2.03

27

0.9

304992

B_G_02

0.64

43603

1.96

27

0.6

305739

B_G_03

0.64

42375

1.97

27

0.9

303498

B_G_04

0.64

43194

2.01

27

0.8

304992

B_G_05

0.63

43689

1.95

27

0.8

307202

B_G_06

0.64

42375

2.01

27

0.8

303498

B_G_07

0.61

42323

1.90

26

0.6

326992

B_G_08

0.61

42323

1.90

26

0.8

326992

B_G_09

0.60

44064

1.92

28

0.9

282992

B_G_10

0.64

43194

1.94

27

0.8

304992

B_G_11

0.64

43194

2.13

27

0.8

309992

B_G_12

0.63

43441

1.97

27

0.8

300097

Best 3 iterations are selected for the next stage

B_G_xx

Unsatisfactory Values

Selected Iterations B_G_05

B_G_11

B_G_12

Evaluation

The iterations formed closely packed aggregations that were able to achieve FAR of up to 1.95. The percentage of Density preferred blocks was understandably higher and the interstitial spaces were visibly lower. To achieve higher packing eďŹ&#x192;ciency the density clusters on certain instances strayed from the desired cluster organisation patterns. A scoring system similar to the Mumbai option was applied where the aggregations and their attributes were tabulated and scored and 3 ďŹ ttest individuals are selected for the evaluation process. Neighbourhood Level Design Development

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7.3.3 Aggregation : Beijing Evaluation

EVALUATION CRITERIA

Distribution of different types of emergent spaces

EVALUATION 1

Minimum Void or Emergent Spaces in the Site

EVALUATION 2

Void spaces located on the periphery to be used as public green pockets spaces.

EVALUATION 3

Larger emergent spaces located near the indigenous setting so as to have higher porosity on that side

SELECTED ITERATIONS FOR BEIJING SITE

B_G_05

B_G_11

Total Clusters Accommodated : 27

B_G_12

Total Clusters Accommodated : 27

No. of Clusters of Typology 1 (Q) : 5

No. of Clusters of Typology 1 (Q) : 7

No. of Clusters of Typology 1 (Q) : 6

No. of Clusters of Typology 2 (QD) : 13

No. of Clusters of Typology 2 (QD) : 11

No. of Clusters of Typology 2 (QD) : 12

No. of Clusters of Typology 3 (D) : 9

No. of Clusters of Typology 3 (D) : 9

No. of Clusters of Typology 3 (D) : 9

Neighbourhood FAR generated : 1.95 Area of Emergent Spaces : 307202

Neighbourhood FAR generated : 2.13 Area of Emergent Spaces : 309992

Area of Emergent Spaces : 300097

The configurations of these spaces that would be best suited for

minimum percentage of emergent spaces as this would ensure

this site would be linear, as these can be converted into avenue

higher built up area. Avoiding emergent spaces all together in the

like spaces similar to Hutongs and become part of the network.

aggregation was not possible due to predefined cluster geometries.

Therefore, this is another criteria for selection. In terms of

However these spaces could be used for alternate functions. As the

distribution, majority of the emergent spaces should be located

morphology in Beijing is extremely dense due to higher density

near the indigenous setting to match the fabric in terms of porosity.

goals these emergent void spaces could be used for public open

On these basis B_G_12 iteration is selected for further working.

for home based industries existing in the neighbouring site. SynchroniCity

Neighbourhood FAR generated : 1.97

The selected aggregations for Beijing are evaluated for the

spaces like pocket parks, gardens, markets or small exhibition areas

174

Total Clusters Accommodated : 27


Selected Aggregation

4278 sq.m.

909 sq.m.

6851 sq.m.

Q

D

QD

QD

4278 sq.m.

D QD

QD

D

Q D

458 sq.m.

5389 sq.m.

QD D

QD QD

Q

QD

QD

5389 sq.m.

D

436 sq.m.

Q Q

D QD 2173 sq.m.

Q

907 sq.m.

1359 sq.m.

QD D

D

Q

2091 sq.m.

Fig 7.3.7 Neighbourhood Aggregation Pattern in Beijing

Total Clusters Accommodated :

The option selected for Beijing site visually shows higher porosity on the Eastern site and a considerably higher density on the western side. This was one of the objectives of the aggregation process. The western side also shows tightly packed aggregation making the clusters continuous and less distinguishable as compared to Mumbai aggregation. The aggregation achieves an overall FAR of 1.97 which is on the lower side of the target value, however this was expected as the eastern side of the site required to be much lower in terms of density to match the indigenous settings.

27

No. of Clusters of Typology 1 (Q) :

6

No. of Clusters of Typology 2 (QD) :

12

No. of Clusters of Typology 3 (D) :

9

Neighbourhood FAR generated : Population Supported :

1.97 43441 ppl/sq.km

Built up Area :

868827 Sq.M

Area of Emergent Spaces :

300097 Sq.M

Neighbourhood Level Design Development

175


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7.4 NETWORK GENERATION

This stage of design development investigates logic of network generation that would relate to the existing context. The logic therefore approaches the subject at two levels: at an overall level the network pattern aims to create a fabric that integrates the site with the surroundings and at a local level the logic refers back to features that deďŹ ne networks in local indigenous settings. The block organisation is negotiated within limits to accomplish these goals. 7.4.1 Network Generation Principal Criteria

178

7.4.2 Network Generation : Mumbai

180

7.4.3 Network Generation : Beijing

184

Neighbourhood Level Design Development

177


7.4.1 Network Generation Criteria Principal Aspects Network Criteria

The network generation is based on 4 basic aspects. These would be common for both the sites and would define the general properties and principles for the process.

Hybrid System For the network generation a hybrid method, which is combination of top-down imposed system and an emergent network system (Fig 7.4.1), is followed. This method allows incorporating architectural ambitions within an otherwise bottom up approach;

Top Down Imposed Network

secondly, it avoids randomness and inefficiency existing in a completely emergent method. The system provides better control of emergent spaces and still maintain characteristic and properties of morphologies defined in the process.

Hybrid Network

Bottom Up Emergent Network Fig 7.4.1 A Hybrid System Between Imposed and Emergent Networks

Minimum Spanning and Hierarchical The network system would be regarded as an analogue system of Block Re-Organisation

woolly path experiment to generate a highly connected, effective and minimum spanning system. This would ensure that the network length is minimized without compromising connectivity (Fig 7.4.2). The system would also try to establish a defined hierarchical order for the network system, this would be defined by the connectivity values found on each segment of the network which would in turn define the widths of these routes.

Fig 7.4.2 Before/ After Network Self-Organisation

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Organisational Flexibility to Blocks To incorporate the design ambitions block organisation would need to be negotiated. To minimise distortion to the clusters, limitations are set on the extent to which a block is allowed to shift. The limit is set according to the location of the block with in the cluster. The peripheral blocks are allowed to move only within 30 M radius as this ensures the density values are not affected and to some extent cluster morphology is preserved. The inner blocks are allowed to move up to 60 M as this ensures higher flexibility for smaller blocks to help create tighter packing as well as provide flexibility to change the morphology of the central space.

Fig 7.4.3 Displacement Ranges for Small/ Medium Blocks

Access Routes integrated with Buildings The network system attempts to break away from conventional network sub-division that divides the fabric into plots with segregated built form. The logic attempts to use the emergent paths within the built forms as part of the main network for better integration.

Fig 7.4.4 Informal Pass-way that Across the Building Morphology Neighbourhood Level Design Development

179


7.4.2 Network Generation : Mumbai Site SpeciďŹ c Criteria

Fig 7.4.5 Proposed Centralised Network Pattern in Mumbai

Primary Routes leaded to the centre

Branched layout of network ending in Cul-de- sacs

Public Spaces located on secondary / tertiary branches

Network Concept Diagrams

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The aim for the network generation in Mumbai is to create a

the characteristics of existing indigenous patterns. The network

centralised pattern that extends from the existing fabric around

in Mumbai would follow branching patterns to accommodate

the site to the central core within the site. This is proposed so as to

the local level public squares on secondary paths to create visual

create better connectivity for the programmatically diďŹ&#x20AC;erent core

segregation for these spaces.

and increase porosity to the central business district. The primary

To bring in the properties of featured cul-de sacs and oďŹ&#x20AC;set paths,

routes would connect the site periphery to the centre and the

which create intimate gathering spaces, we device a logic to control

secondary paths emerge from these main routes.

network density, segment detour and cul-de-sac proportions.

Another criterion that governs the network pattern is that it follows

Network density indicates the road length per square kilometre


Fig 7.4.6 ConďŹ guration of Public Space in Mumbai

Fig 7.4.7 Proposed Aggregation Pattern in Mumbai Site

which is found to be lower in Mumbai. Segment Detour is the ratio of total length to distance, this indicates the ratio of road length required to cover a certain distance, and therefore higher the road length higher is the detouring. Cul-de-sac proportions indicate the proportion of discontinuous routes. Both these aspects hold higher numeric values in the case of Mumbai and therefore would be the target for the design. Fig 7.4.7 shows the modiďŹ cations occurring in the clusters.

Network Density :

171km / 100sq.km

Segment Detour :

1.47

(Total length / distance) Cule-de-Sac Proportions :

8/10

Neighbourhood Level Design Development

181


7.4.2 Network Generation : Mumbai Generation Process

Fig 7.4.8 Generated Neighbourhood Morphology in Mumbai Site

Fig 7.4.9 Network Pattern after Re-organisation of Geometries

1.26

Fig 7.4.10 Global Integration [HH] Value before Geometries Re-organisation

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0.25

1.26

Fig 7.4.11 Global Integration [HH] Value after Geometries Re-organisation

0.25


Fig 7.4.12 Network Patterns that Integrating Site with Context

The block reorganisation takes into account the generic aspects as

ambition. The primary routes form a direct connectivity to the

well as those speciďŹ c to Mumbai. To aid the process connectivity

existing road systems and lead into the central core. The local

analysis is carried out in the aggregated geometry. The possible

level public spaces connect to primary or secondary routes through

route systems are analysed on these patterns using the logics

tertiary routes and the informal ground network system is made

mentioned above. The blocks are consequently rearranged to

part of the main network.

derive a simpliďŹ ed and minimalistic version of the network system which encompasses the properties mentioned as the design Neighbourhood Level Design Development

183


7.4.3 Network Generation : Beijing Site SpeciďŹ c Criteria

Fig 7.4.13 Proposed Gradient Network Pattern in Beijing

Primary Routes Connect to the Existing Network

Network Density Gradient from West to East

Public Spaces convert into linear organisation

Network Concept Diagrams

184

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The network generation in the Beijing patch is governed by the

of roads is aimed for as seen in the Mumbai patch. The network

contrasting context existing around the site. The network needs

patterns to match the context again refers to the three aspects of

to form a gradient from high network density on the east to a

network density, segment detour and cul-de-sac proportions. The

lower network density on the west. To enable smooth transition

network density on an average is aimed to be much higher than

from west to east the existing routes needed to be linked to the

that of Mumbai however the segment detouring and cul-de â&#x20AC;&#x201C;sac

newly generated ones. The primary routes would be extensions of

proportions to be achieved would be lower. The goal is to create

existing network and continue into the site. A similar hierarchy

linear orthogonal network routes similar to existing morphologies


Fig 7.4.14 ConďŹ guration of Public Space in Beijing

Fig 7.4.15 Proposed Aggregation Pattern in Beijing Site

to induce the possibility to create Hutong like spaces. The blocks are reorganised to convert from central public space formed in clusters to linear forms more suited to the indigenous morphologies. This is shown in the diagram that indicates the change in morphologies of the cluster.

Network Density :

324 km / 100sq.km

Segment Detour :

1.08

(Total length / distance) Cule-de-Sac Proportions :

4/16

Neighbourhood Level Design Development

185


7.4.3 Network Generation : Beijing Generation Process

Fig 7.4.16 Generated Neighbourhood Morphology in Beijing Site

Fig 7.4.17 Network Pattern after Re-organisation of Geometries

1.35

Fig 7.4.18 Global Integration [HH] Value before Geometries Re-organisation 186

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0.31

1.35

Fig 7.4.19 Global Integration [HH] Value after Geometries Re-organisation

0.31


Fig 7.4.20 Network Patterns that Integrating Site with Context

A similar process as Mumbai is followed in Beijing site where

The routes connect to the existing peripheral roads which creates

the block reorganisation is informed by the generic principles

higher network density on the east as compared to the west. The

of the network as well as speciďŹ c criteria of the site. From the

central public spaces morph into linear forms more suited to the

connectivity analysis the possible route systems are analysed and

Hutong typologies.

the blocks re-arranged to derive the desired network pattern. The network pattern in contrast to Mumbai is linear and continuous. Neighbourhood Level Design Development

187


188

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7.5 BLOCK DIFFERENTIATION

This is a recursive step within the process that looks at block level to introduce aspects specific to the site location. The aspects are based on two factors: the new site specific parameters which are distinct for each site and were not considered in the catalogue generation, secondly, the architectural elements which are familiar to the context and suit the local socio-cultural and climatic aspects. Subtle modifications that don’t affect the optimised parameters would be introduced in this stage. 7.5.1 Block Differentiation Criteria

190

7.5.2 Block Differentiation : Mumbai

192

7.5.3 Block Differentiation : Beijing

196

Neighbourhood Level Design Development

189


7.5.1 Block Differentiation Criteria

k W h / m2

Mumbai

5 5 0 0 .0 5 0 0 0 .0 4 5 0 0 .0 4 0 0 0 .0 3 5 0 0 .0 3 0 0 0 .0 2 5 0 0 .0

Total Annual Collection: 524.33 kWh/m2

2 0 0 0 .0

[12] EERE/ US Department of Energy (2013), Weather Data Source, Available from: http://apps1.eere.energy.gov/buildings/ energyplus/weatherdata/2_asia_wmo_ region_2/IND_Mumbai.430030_ISHRAE. zip (Accessed in 20th, Dec, 2013 )

Underheated Period: 58.31 kWh/m2 Overheated Period: 190.98 kWh/m2

1 5 0 0 .0 1 0 0 0 .0 5 0 0 .0 0 .0

The graph shows that the comfortable value of incident Ja n

Fe b

M ar

Apr

M ay

Ju n

Ju l

Aug

Sep

Oc t

N ov

D ec

Fig 7.5.1 Accumulative Incident Solar Radiation in Mumbai (18.9750° N, 72.8258° E)[12]

k W h / m2

solar radiation for Mumbai in Summer is : 1300 kWh/m2

Beijing

5 5 0 0 .0 5 0 0 0 .0 4 5 0 0 .0 0 4 0 0 0 .0 0 3 5 0 0 .0 0 3 0 0 0 .0 0 2 5 0 0 .0

[13] EERE/ US Department of Energy (2013), Weather Data Source, Available from: http://apps1.eere.energy.gov/ buildings/energyplus/weatherdata/2_ asia_wmo_region_2/CHN_Beijing. Beijing.545110_CSWD.zip (Accessed in 20th, Dec, 2013 )

Total Annual Collection: 420.20 kWh/m2

2 0 0 0 .0

Underheated Period: 47.68 kWh/m2 Overheated Period: 166.25 kWh/m2

1 5 0 0 .0 1 0 0 0 .0 5 0 0 .0 0 .0

The graph shows that the comfortable value of incident Ja n

Fe b

M ar

Apr

M ay

Ju n

Ju l

Aug

Sep

Oc t

N ov

D ec

solar radiation for Beijing in Winter is : 4200 kWh/m2

Fig 7.5.2 Accumulative Incident Solar Radiation in Beijing (39.9139° N, 116.3917° E) [13]

Incident solar radiation is the index that represents the exterior

block morphologies in the two sites. The modification would be

thermal comfort.

Incident solar radiation or Insolation is a

informed by the incident solar radiation on the semi-public spaces

measure of solar radiation energy received on a given surface area

occurring in the blocks. The block and the semi-public space

and recorded during a given time. It is also called solar irradiation

morphology would be simultaneously modified till a suitable value

and expressed as “hourly irradiation”. The unit of measure is

for the incident radiation at the semi-public spaces is reached.

kilowatt-hours per square metre (kWh/m2).

This modification would simultaneously consider other optimised parameters so as to minimise impact on them and achieve an

The parameter would be used to create differentiation in the 190

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overall high fitness in terms of quality.


Fig 7.5.14 Informal Pass-way as Local Architectural Features

Fig 7.5.6 Arcade Features of Local Architectures for Shading

To create higher relevance of design to the location, architectural

feature element is each site are selected which can be embedded in

features representative of the local settings are selected. Aspects

the design logic by simple modifications so as to not sacrifice the

would be chosen on the basis of their socio-cultural significance,

parameters optimised in the initial process.

functionality and suitability to local environment. This would help relate the design to the surrounding features and still maintain independent identity. Incorporating such features provides another level of differentiation which is highly site specific and does not depend on direct computational results. Therefore different Neighbourhood Level Design Development

191


7.5.2 Block Differentiation : Mumbai Incident Solar Radiation

k W h / m2 5 5 0 0 .0 5 0 0 0 .0 4 5 0 0 .0 4 0 0 0 .0 3 5 0 0 .0 3 0 0 0 .0 2 5 0 0 .0 2 0 0 0 .0 1 5 0 0 .0 1 0 0 0 .0 0 [12] EERE/ US Department of Energy (2013), Weather Data Source, Available from: http://apps1.eere.energy.gov/buildings/ energyplus/weatherdata/2_asia_wmo_ region_2/IND_Mumbai.430030_ISHRAE. zip (Accessed in 20th, Dec, 2013 )

5 0 0 .0 0 .0

Ja n Fe b M ar Apr M ay Ju n Ju l Fig 7.5.3 Accumulative Incident Solar Radiation in Mumbai (18.9750° N, 72.8258° E) [12]

Oc t

N ov

D ec

This type of modification is simultaneously carried out in the

context of Mumbai. The blocks in Mumbai need to adapt for

whole site. The resulting morphologies are analysed for the kind

overheated seasons to provide spaces of lowered temperatures

of semi-public spaces generated. The most common change

where outdoor activities can take place. The ideal value which

noticed was that the semi-public spaces converted into semi-

for Mumbai is 1310 kWh/m is set as the target value to achieve.

enclosed forms that shaded the space and reduced the incident

Fig 7.5.4 shows the simultaneous optimisation of two opposing

solar radiation. These features when compared with those found in

parameters: incident solar radiation and sky view factor. The

the local settings are similar in character to semi-enclosed, semi-

iteration is rejected if either parameter falls below the threshold

public spaces.

limit. Threshold for incident solar radiation is set as 1310 kWh/m

2

and for sky view threshold remains at 0.53. SynchroniCity

Sep

The graph shows the ideal solar radiation value desired in the

2

192

Aug


radiation value

SVF : 0.72

target value

SVF : 0.36 target value radiation value

SVF : 0.51 target value radiation value

Fig 7.5.4 Block Re-optimisation for Incident Solar Radiation and Skyview Factor

Fig 7.5.5 Blocks Before/ After Optimisation Neighbourhood Level Design Development

193


7.5.2 Block Differentiation : Mumbai Architectural Aspects

Fig 7.5.6 Arcade Features of Local Architectures for Shading

The architectural feature selected for Mumbai is the shaded

is to create continuous pathways along the streets leading into the

pathways that line the main streets and roads. They provide respite

business centre within the site.

from the elements during the hot summer season as well as wet monsoon season. These pathways also convert onto spots for

The operation depends on the overall Floor Area Ratio and

informal small shops or hawkers who capitalise on the pedestrian

therefore the change in the floor space after the addition and

traffic. A number of retail units are located along these pathways.

deletion operations must remain minimal. These modifications are generated on the primary routes and some of the secondary

To create these shaded corridor spaces, the modification takes place by simple extrusion or deletion of building mass. The aim 194

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routes of the site.


Fig 7.5.7 Typical Section of Arcade Architectures

Block Geometry Before Modification

Block Geometry After Volume Addition

Block Geometry After Volume Deletion

Fig 7.5.8 Modification on Block Geometries

Fig 7.5.9 Rendering of Street-view with Mumbai Local Architectural Features Neighbourhood Level Design Development

195


7.5.3 Block Differentiation : Beijing Incident Solar Radiation

k W h / m2 5 5 0 0 .0 5 0 0 0 .0 4 5 0 0 .0 0 4 0 0 0 .0 0 3 5 0 0 .0 3 0 0 0 .0 0 2 5 0 0 .0 2 0 0 0 .0 1 5 0 0 .0 1 0 0 0 .0 [13] EERE/ US Department of Energy (2013), Weather Data Source, Available from: http://apps1.eere.energy.gov/ buildings/energyplus/weatherdata/2_ asia_wmo_region_2/CHN_Beijing. Beijing.545110_CSWD.zip (Accessed in 20th, Dec, 2013 )

5 0 0 .0 0 0 .0

Ja n

Fe b

M ar

Apr

M ay

Ju n

Ju l

Aug

Sep

Oc t

N ov

D ec

Fig 7.5.10 Accumulative Incident Solar Radiation in Beijing (39.9139° N, 116.3917° E) [13]

The blocks in Beijing need to be optimised for winter months

threshold sky-view factor of 0.5. The diagrams show the trading off

when the temperatures drop below 10 degree Celsius. The aim for

between the sky-view factor and incident solar radiation.

Beijing would be to target a higher value of incident solar radiation which would ensure that in winters these spaces get adequate solar

The resulting morphologies show a common trend of generating

exposure and therefore remain warm for outdoor activities.

larger semi-public spaces formed by connecting two or more spaces separate ones, the spaces get elongated in east west direction to

Similar to the process in Mumbai the semi-public spaces and their

increase the solar factors. Even in the elevated forms the spaces

corresponding blocks are modified to create spaces that receive

resemble the typical courtyard morphologies.

4250 kWh/m of incident solar radiation as well as maintain 2

196

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target value radiation value

SVF : 0.72

target value radiation value

SVF : 0.41

target value radiation value

SVF : 0.94

Fig 7.5.11 Block Re-optimisation for Incident Solar Radiation and Skyview Factor

Fig 7.5.13 Blocks Before/ After Optimisation Neighbourhood Level Design Development

197


7.5.3 Block Differentiation : Beijing Architectural Aspects

Fig 7.5.14 Informal Pass-way as Local Architectural Features

The architectural features selected for Beijing are the elevated

by a pathway or a built volume depending on the situation. The aim

pathways commonly seen as features in precedents such as the Ju’er

is to generate a well-connected and minimum spanning elevated

Hotong Project. These have been adopted as they suit the cultural

route as shown in the figure Fig 7.5.14. These connections are

aspects as well as the design needs of better connectivity in higher

employed in parts of the site where the average building height

density locations. These elevated paths in the original settings are

exceeds 20 m. This is done to reduce vertical commute as well as

used as informal pathways to connect residential units and create

provide alternate paths.

a better community links. The blocks within the site are connected 198

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Fig 7.5.15 Typical Section of Architectures with Elevated Pass-ways

Block Geometry After Volume Addition

Block Geometry After Pass-way Addition

Fig 7.5.16 ModiďŹ cation on Block Geometries

Fig 7.5.17 Rendering of Street-view with Beijing Architectural Features Neighbourhood Level Design Development

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7.6 PROGRAMMATIC VARIATION

The aim of this design stage is to generate programmatic as well as morphological variation by using tools within the design system without sacrificing the optimized spatial quality. These new additions and modifications take into account the programmatic requirements of the site. It tests the ability of the system to generate new site specific morphologies to suit specific building requirements. Typological variation is pursued on two levels:

the local level / home based retail establishments that closely link to

residential use and serve daily requirements and the large scale establishments prevalent in urban scenarios. 7.6.1 Local Level Retail Units

202

7.6.2 Corporate Large Scale Establishments

208

Neighbourhood Level Design Development

201


7.6.1 Local Retail Supply Units Overview

Fig 7.6.1 Local Retail Unit for Daily Items in Beijing

Morphological Variation to suit Local Residential Requirments Rather than conventional planning practice of zoning that is devised to separate incompatible land uses, the indigenous economy in settlements of Beijing and Mumbai are largely based on the mixed-use mode in which small retails are closely linked to residential units. We observe that, in both cultures, there are typical mixed-use built forms with distinct typological features. These autonomous morphologies have emerged and evolved with simplicity and informality. In Beijing these units show higher significance as they are essential for the local economy. More specifically in the indigenous settlements of Beijing there are typical spatial layouts of mixed-use building typologies such as ‘shang dian xia zhai’ which means ‘store on ground floor and living space above it’, or ‘qian dian hou zhai’ that for ‘store in the front and living space at back’. These produce handicraft goods which attract the user groups such as tourists. From a programmatic perspective, these small retail units work in close association with residential units. They are located in close proximity to residents and provide for their daily household requirements. In both the sites this is a common aspect to be considered. Additionally these small units are closely linked to social interaction spaces. They propagate informal gatherings which are commonly seen in both the indigenous case studies. The essential factor to consider for this programmatic use, is its distribution

Fig 7.6.2 Local Shop for Pottery Supply in Mumbai 202

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as they are localised and essential to the community.


Morphological Adaptation

Fig 7.6.3 Volume Insertion for Local Retailing Usages

To incorporate the local retail units in the built form, volume additions in various possible locations is proposed. The volumes utilize voids and niches produced during the generation process. By operating the modifications shown above, we are enabled with the compatibility of resolving different programmatic uses within singular blocks. The blocks are accommodated with the criteria that they have the least effect on optimised parameters. The extra volumes for programmatic differentiation will be inserted either on the ground floor or at-least partly on the ground level in order to be accessible for the maximum number of costumers. The inserted volumes vary from T - shaped space with elevated part accessed from foyer on ground level, linear space to singular space. The addition of these volumes would add further differentiation in the blocks. Neighbourhood Level Design Development

203


7.6.1 Local Retail Supply Units Criteria for Distribution

The distribution of local level retail units are informed by three aspects: Tendency to locate near main street, location to serve maximum number of households and location to provide sufficient visual surveillance.

Programme Accessibility Streets in old Beijing used to be the public space between residential spaces and administrative-commercial spaces. And according to this strategy, street hierarchy addresses not only the density of pedestrian flow but also informs the programmatic allocation within urban scenarios. Primary hierarchies of routes are preferred locations for stores as they are most accessible for large population. This large population is essential for department stores, which require a considerable turnover. Examples such as the Bell & Drum Commercial Street and the Qian Men Commercial Street are proved to follow this pattern.

Fig 7.6.4 Typical Street Hierarchies

Network Hierarchy Programme Location

Connectivity These stores are the closest source for retail of raw & cooked food for daily use, it is an inevitable element to achieve a self-sufficient neighbourhood. According to this, it is optimal for the stores to be located in the spot that all the households are within a certain distance. Taking this criterion into account stores must be placed at strategic locations that show connectivity to maximum number of houses.

Fig 7.6.5 Accessibility of Retail Unit

Programme Accessibility Programme Location

Visual Controllability In describing the configuration of street, Jane Jacobs’ put forward the concept of casual surveillance by local shop owners. This theory on visual surveillance, also known as “eyes on the street”

[14]

became Jacobs’ best known concept and indicates that dwell-able streets

with small-scale commerce and a high density of residents are the best means to ensure street safety, since the informal surveillance by the street’s denizens and merchants creates the sense of well-being and safety. The inverse of street eyes, Jacobs called them “blind” places, characterized many recent urban renewal projects. The local stores will prefer to be located where maximum area of street spaces can be visually surveillanced and minimum area is in ‘blind’ place within the site. [14]. Jacobs, J. (1961). The Death and Life of Great American Cities, Vintage Books-Random House, New York

Fig 7.6.6 Areas Under Visual Surveillance

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Isovist Area Programme Location


Distribution Analysis Maps BEIJING

MUMBAI Pedestrian Preference Simulation is performed in order to simulate the pedestrian movement density in the site. This is one of the possible circumstances indicating the spots where local pedestrians tend to influx or gather.

Fig 7.6.11 Pedestrian Preference Simulation of Beijing Site Low

Pedestrian Density

Fig 7.6.12 Pedestrian Preference Simulation of Mumbai Site

High

In order to cover the maximum number of local households with minimum number of units. A Genetic Algorithm is applied to search for the optimal locations. The algorithm aims to allocate stores where local households are within 100m radius which is a 2 minute walk according to pedestrian speed of 4.75 kilometres per hour (2.95 mph).

Fig 7.6.9 Accessibility Analysis of Beijing Site

Fig 7.6.10 Accessibility Analysis of Mumbai Site Area accessible within 100m

Depthmap controllability analysis was introduced to ascertain where people might or might not feel exposed or secluded, safe or vulnerable. Also, it indicates the visual-dominant areas that guarantee the observers to easily see people’s activities without difficulty being seen by observed people. The local stores will prefer to be located in spots with high controllability values.

Fig 7.6.7 Visual Controllability Analysis of Beijing Site Low

Visual Controlability

High

Fig 7.6.8 Visual Controllability Analysis of Mumbai Site Neighbourhood Level Design Development

205


7.6.1 Local Retail Supply Units Beijing

The mapped information from the three criteria connectivity,

modified. Understandably most of the blocks to be modified are

programme accessibility and visual controllability inform the

located on main streets, primarily at junctions. Smaller blocks

location of the retail units. The three layers of information are

are evenly distributed through the site. However, there is a higher

overlapped and the locations adding up to the highest probability

concentration of these blocks in the North-western part where

are identified to create programmatic differentiation.

there is a higher population density rather than the opposite side

The map below shows the location of blocks which would be

where the density is comparatively low.

Location Mapping

Fig 7.6.13 Distribution of Local Retail Programmes in Beijing Site 206

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0

250 m


Mumbai

The distribution pattern for retail units in Mumbai also follows

where most of the units are located in close proximity or adjacent to

a similar approach of overlaying the three layers of information:

public squares which shows some reminisce of indigenous settings.

criteria

connectivity,

programme

accessibility

and

visual

controllability to generate the location of blocks to be modiďŹ ed. The pattern in Mumbai site turned out to be an even distribution,

Fig 7.6.14 Distribution of Local Retail Programmes in Mumbai Site

0 Neighbourhood Level Design Development

250 m 207


7.6.2 Corporate Large-scale Establishments Overview

Morphological Variation to suit Urban Programmatic Requirements Fig 7.6.15 Modernised Working Space in Mumbai

This part of the design exercise looks at programmatic and typological variation by addition of new block morphologies. Novel geometries will be generated with distinct floor areas and volume heights by varying parameters within the same system in order to meet the urban programmatic requirements. This is done so as to generate morphologies which are more specific to the programmatic and site requirements. This stage also tests the compatibility of the system to accommodate new functions which form sync with the designed residential fabric. The new morphologies take into account the spatial requirements of urban scenarios where different kinds of functions would require different kinds of floor depth, volumes and heights. Taking this into account a number of differentiated morphologies are generated. The following segment shows a few examples of these morphologies. These volumes could be located in emergent spaces generated during aggregations. The generation logic for this stage would need to take into account the developable areas available in terms of boundaries, the existing quality attributes of existing blocks and the programme that needs to be accommodated. This aspect is explored in detail in the Mumbai site where one of the chief ambitions was to create a central space which was programmatically and typologically different and

Fig 7.6.16 Modernised Working Space in Mumbai 208

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would work like a landmark for the location.


Block Morphologies for Differentiated Programmes

40m

20-

40m

20-

12-24m

4m

8-2

4-8m 4-8m

MORPHOLOGY TYPE I

MORPHOLOGY TYPE II

Office/ Business Cubicles / Commercial and Instituitional Functions

Markets / Exhibitions / Workshops that Requires Larger Floor Spans

MORPHOLOGY TYPE II Malls / Multiplexes / Auditoriums that Requires Multi-level Volumes

Programmatic/ Morphological Differentiation

According to the programmatic usages, we proposed

Also, we are able to produce blocks suitable for

The system is also capable of generating block

block with differentiated morphologies.This is one

exhibition spaces/ workshops which is featured by large

geometries featured by singular multi-floor space

example which is from the aggregation of small office/

spanning spaces and have flexibility in terms of interior

which is ideal for malls/ auditorium or other

business cubicles. it is generated with floor spaces that

layout. These spaces have porous ground floor which

commercial/ performance related functions that

relate to offcice cubicles. Porosity is of significnce here

generates shaded area for outdoor markets or other

does not require natural lighting or ventilation.

as it would affect ventilation and lighting factors.

informal activities

Neighbourhood Level Design Development

209


7.6.2 Corporate Large-scale Establishments Potential Intervention for Mumbai

A high rise typology that accomodates office / commercial functions and serves to provide a landmark type identity to the site with its typologically distinct volume

High porosity on the ground level. Ideal location for high street retail units as the space can accomodate high pedestrian flows associated with such programmes.

The morphology reacts to the quality attributes of neighbouring blocks. It generates in a manner that the optimised quality of residential blocks is not sacrificed.

Fig 7.6.16 Programmatic/ Morphological Differentiation 210

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Fig 7.6.16 Programmatic/ Morphological Differentiation: Example of Mumbai Central Core

Business Centre for Mumbai As an example for programmatic differentiation, to accommodate non-residential function, the central emergent space in the Mumbai site is chosen. Generically the centre of settlement is usually typified by a concentration of retail, office buildings and other public activities. More specifically, in Mumbai there are several Business Districts located in the centre of neighbourhood such as Belapur, Nariman Point, Ballard Estate, Bandra Kurla Complex and Andheri. Similarly the design ambition could be the establishment of a programmatically different

Proposed Plots for Differentiated Programmatic Usages

sector located in the geographical centre of the site with a vibrant business environment that aims at shifting a portion of the economic dependence of Thane out of the Mumbai core and into the hinterland of the site. The example shown here is an establishment that could house offices as well as technology centres, being designed and developed in order to promote local business. The growing BPO (Business process outsourcing) sector could be also be part of the centre. The morphology takes into account the surrounding built forms are their quality attributes, the geometry of the emergent space, high porosity at the ground level for pedestrian activities to support large retail shops and the ambition to generate a high rise typology that gives an identity to the space. Neighbourhood Level Design Development

211


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8

CRITICAL ANALYSIS The last chapter aims at evaluating and analysing the results of our design research. For the design conclusion we would critically revisit three aspects that focuses over the established evolutionary system, the fulfilment of design objectives, as well as the emergence of architectural properties during the bottom-up aggregation. Potential further refinements will be discussed in terms of system adaptation and materialisation. 8.1 Conclusions

213

8.1.1 System Accomplishments

214

8.1.2 Design Objectives Fulfilment

216

8.1.3 Emergent Attributes Observations

218

8.2 System Evaluation and Future Prospects

233

Critical Analysis

213


8.1.1 System Accomplishments

Quantifiable Spatial Parameters

Semi-public Space Height

2 +3 m 4 +9 m 3 +6 m

1 +0 m

Cluster_Q_02

Incident Solar Radiation

Shaded Area Proportion

Fig 8.1.1 Exploded Section View of Sample Block

Sky View Factor

The evolutionary system that we established is an attempt to reinterpret the indigenous attributes in urbanised scenario. The system adopts to the localised socio-cultural aspects as well as to satisfy the high urban population density requirement via midrise residential typologies which are embodiments of quantifiable spatial parameters. Generic block geometries are generated as the output of the system which featured by elevated semi-public spaces, and can be developed into urban fabrics during the bottom-up aggregation. According to the evaluation of sample blocks we realised that although all the quantified parameters that describe the spatial value may not be able to achieve a high score at the same time, an overall high

284

0.41

0.56

fitness value is still guaranteed.

1 +0 m 1 Sky View

339

0.45

0.76

3 +6 m

Factor Enclosure Value

369

0.45

0.82

4 +9 m

+0 m

2

+3 m

3

+6 m

4

+9 m

0.56

0.57

0.76

0.82

1.77

0.69

1.02

1.44

287

284

369

339

23%

23%

25%

21%

Incident Solar Radiation (Wh/m2) Shaded

287

0.38

0.57

2 +3 m

Fig 8.1.2 Incident Solar Radiation/ Shaded Area Proportion/ Sky View Factor of Semi-public Spaces 214

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Area Proportion

Fig 8.1.3 Table Showing the Values of Evaluation


Differentiated Morphogenesis The generation created differentiation at all three levels of design

Block Level Differentiation looked at sitespecific requirements in terms of solar factors as well as local architectural aspects. Different morphological modifications were based on these aspects

Cluster Level Differentiation looked at configuration of public spaces. In Beijing the spaces converted into linear organisation and in Mumbai these became large centralised spaces.

10 F 8F 6F 4F

Neighbourhood level accomplished site specific requirements of network connectivity and network patterns, density requirements and density gradients. The system was used to generate two very different urban scenarios that relate to the site context.

2F 0F

10 F 8F 6F

Density Gradients were referenced from the site surroundings and were part of the architectural ambition to create a smooth transition of height and density into the site.

12 F 10 F 8F 6F

4F

4F

2F

2F

0F

0F

12 F 10 F Fig 8.1.4 Comparison between Differentiated Geometries in Block/ Cluster/ Neighbourhood Scale 8F 6F 4F 2F 0F

Critical Analysis

215


Density Score (FAR) 3.50

8.1.2 Design Objectives Fulfilment

3.00

Quality Score 2.50

Comparative Analysis of Parameters and Design Ambitions

2.50 Enclosure Value

2.00 1.50 1.00

Quality Score

0.50

2.50

0

Density Score (FAR)

Shaded Area Proportion 3.50

1.50

Incident Solar Radiation

1.00

Sky View Factor x 2

0.50

3.00 2.50

2.00

Enclosure Value

1.50

Samples Shaded Area Proportion

1.50

1.00

Incident Solar Radiation

1.00

0.50

Sky View Factor x 2

0.50

0

2.00

0

2.00

Samples Ranwar Village_ Mumbai Mumbai Sample

Urban Cluster

0

Fig 8.1.5 Comparison between Indigenous/ Urban Scenarios and Proposed Interventions Samples

Office Buildings

Beijing Sample

Samples Ranwar Village_ Mumbai

Ranwar Village, Mumbai

Typical Urban Cluster Mumbai Sample

Office Buildings

Beijing Sample Urban Cluster

r = 175 m

r = 90 m

Plot Area: 22513 m2 Coverage Radius: 90 m

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Plot Area: 54179 m2 Coverage Radius: 150 m


The generated spatial attributes show characteristics that are intermediate between the typical urban and typical indigenous settings (Ranwar Village has been taken as the

Site Name

Rongshu, Beijing

Thane, Mumbai

representative standard for indigenous settings to compare parameters). The values for

Site Area (m2)

1,287,969

1,381,265

the generation in Mumbai tend to be similar to the indigenous settings whereas the

Total Built Up Area (m2)

2,685,646

2,024,186

59,681

44,982

ones in Beijing are more similar to the urban patch. They may possibly be due to the different density requirement of each site. In most respect the density attributes were

Population

achieved as per the design ambitions and goals. Apart from this the quality attributes

Population Density (p/ha)

463

326

would have been affected in the process, however with the site specific differentiation

FAR value

2.09

1.47

each site possess its own differentiable attributes.

Fig 8.1.6 Statistics Obtained in the Design generated on each site

Proposed Design in Thane, Mumbai

r = 95 m

Plot Area: 19401-22513 m2 Coverage Radius: 95 m

Proposed Design in Rongshu, Beijing

r = 95 m

Plot Area: 19401-22513 m2 Coverage Radius: 95 m

Critical Analysis

217


8.1.3 Emergent Attributes Observations

Comparative Analysis of Network Patterns BALLWIN

MUMBAI SITE

BRANCHED HIERARCHICAL

0.22

Fig 8.1.7 Streets Patterns and Global Integration [HH] Value of Ballwin

0.06

0.31

Fig 8.1.8 Proposed Streets Patterns and Global Integration [HH] Value of Thane, Mumbai

0.11

Apart from these, we also evaluate the emergent properties that the generated network

development of low-density urbanism. As an analogue structure to leaf veins, there

patterns embed. Samples of Ballwin and San Francisco are introduced for their distinct

are multiple hierarchies from primary branches to minor ones. And the result from

network conďŹ gurations and will be compared with the street patterns generated in both

Depthmap Global Integration [HH] Value analysis shows a clear integration at the main

sites. Ballwin represents an ideal and uncompromising example for branching network

streets.

system which is partly adopted in generation of Mumbai network. And San Francisco is an example of planning with strict grid-iron network system partly reďŹ&#x201A;ected in the

Grid patterns (Fig 8.1.10), on the other hand, are favoured by urban planners in dense

planning of Beijing.

scenarios currently. Opposite to the hierarchical cul-de-sac patterns, gridiron systems usually simplify it into a two-level hierarchy from street to the destination as Albert Pope

Cul-de-sac patterns(Fig 8.1.7) are a recognisable feature that characterise the 218

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[1]

described . And the Global Integration [HH] Value analysis shows streets patterns of


BEIJING SITE

SAN FRANCISCO

GRID HOMOGENEOUS

0.64

0.43

Fig 8.1.10 Proposed Streets Patterns and Global Integration [HH] Value of Rongshu, Beijing

0.13

Fig 8.1.11 Streets Patterns and Global Integration [HH] Value of San Francisco

0.19

homogeneity. The network patterns adopted in the design are produced by observing and manipulating the spatial elements of the two existing network systems, we are able to generate network patterns for both Mumbai and Beijing sites which are intermediaries to both types of systems. We manipulate spatial factors such as network density, segment detouring and cul-de-sac proportions. And the results of Global Integration [HH] Analysis illustrate that the generated patterns are hybrid that not only spans between but also cooperates typical branching and grid system.

[1]. Pope, A. (2008), Terminal Distribution, Architectural Design, 78: 16â&#x20AC;&#x201C;21. doi: 10.1002/ ad.603, John Wiley & Sons, Ltd

Critical Analysis

219


8.1.3 Emergent Attributes Observations Informal Network Evaluation

Ground Level Plan Showing Emergent Network Other Routes

Primary Route

Emergent Ground Network

Key Plan

Emergent Ground Network The inherent property of the individual blocks constitute void spaces on the ground. Following the aggregation process, it is observed that these small passages become part of a larger network system. This emergent phenomena gives rise to shaded, informal corridor spaces that can be used as alternate network paths. These hold very diďŹ&#x20AC;erent characteristic to other network systems. These informal network systems not only improve integration but also provide shorter routes to the destinations. Some of these routes Location Under Reference

220

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Beijing Site Example

run continuously upto 160 M.


Fig 8.1.12 Perspective View of Informal Networks

Fig 8.1.13 Exploded View of Informal Networks

Elevated Network Elevated passages are proved to be a means that can reduce vertical transport as well as connect the semi-public spaces with the informal network analogue to indigenous precedents. Incorporating these in

2 Average Radiance Value: 268 Wh/m18 m

high density situations provides an alternate connectivity and better integration.

10 m

Shading Quality on Streets

10 m

The informal networks are evaluated for their spatial quality. The

21 m

street conďŹ guration in Mumbai shows an alternate between shaded / unshaded areas with solar radiance level varying from around 50Wh/ m2 to 450 Wh/m2. While on the other hand, networks in Beijing show a

40 m

preference of continuous solar accessibility. Average Radiance Value: 318 Wh/m2

31 m

36 m

18 m

12 m 10 m 16 m

Radiance Wh / m2 450 405 350 300 250 200 150 100 50 0

Fig 8.1.14 Incident Solar Radiation of Informal Networks in Mumbai

Radiance Wh / m2 450 405 350 300 250 200 150 100 50 0 Fig 8.1.15 Incident Solar Radiation of Informal Networks in Beijing Critical Analysis

221


8.1.3 Emergent Attributes Observations Morphological Versatility Evaluation

Analogue to traditional courtyard houses, the generated morphology organises the living units around common spaces shared by a few households. Also, the open spaces functions as the element that adds influx to the circulation system.

Minimum spanning floor depths is one of the features of multi-storey slab buildings. Thus aid in improving natural lighting as well as ventilation flows.

Floor set-backs prevailing in mass/ collective housing projects. It increases valid daylighting area and at the same time provide better view and private terraces for each residential unit.

Elevated passages can be found in precedent projects as an approach to achieve self-sufficient neighbourhoods by reinforce the interlink between adjacent residential buildings. Such informal passages also remit the pedestrian density on the ground.

222

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Photographs of 3D-printed Sectioning Models

Critical Analysis

223


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Critical Analysis

225


Visualisations Mumbai : Street Views

1

2

3

4

5

6

9

8 7

7

6 5 4

8

3 2 1 9 226

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+0

+0

+0

+0

Section CC’

+6

+12

+18

+24

+30

+36

+42

Section BB’

Section AA’

+6

+12

+6

+6

+18 +12

+0

+0

+12

+6

+6

+12

Mumbai : Sections

Critical Analysis

227


Visualisations Beijing : Street Views

1

2

3

4

5

6

1

7 2 3 4

9 5 8 7

8

6

9 228

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+0

+0

Section CC’

+6

+6

+0

+0

+12

+6

+6

+12

+12

+12

Section BB’

+18

+0

+0

+18

+6

+6

Section AA’

+12

+12

+18

Beijing : Sections

Critical Analysis

229


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Critical Analysis

231


232

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8.2 System Evaluation and Future Development

Fig 8.2.1 Typical Windcather Morphologies in Yazd, Iran

Fig 8.2.1 Typical Stepwell Morphology in Hampi

The System The system developed in the this process comprised of a logic which was a combination of

cultures and regions. Morphological elements of local architectures are to be critically

bottom-up and top-down approaches. This aspect allowed a synchronous combination

reinterpreted in order to achieve socio-cultural and native climatic identity. Apart from

of emergent characteristics and architectural ambitions. The stage based approach of

the calibrating the system to new parameters, scenarios and context, the system would

design from catalogue to site specific scenarios is one of the critical aspect that has been

need to accommodate local architectural features which play a significant role in daily

raised a number of times. An alternate approach could use the same logic of generation

life.

without involving the cataloguing process. This would involve generating each block independently within the context of a site. This approach would produce highly specific

One potential practice of adapting the system into arid regions is to imitate the

block morphologies that appease all aspects of the site, but the process would be

morphological features that make use of the towering lofts as ventilation tunnels. The

computationally extremely heavy as a large number of parameters and a large number

loft known as windcatcher is a traditional Persian architectural element to create natural

of blocks would need simultaneous optimization. The stage based approach provides

ventilation in buildings. It is a low cost and efficient strategy that is disappearing due

this advantage that the parameters are separated into different levels, so at every level

to urbanisation. By adding in socio-cultural and climatic specific criterion, geometries

certain base characteristic of morphology is always maintained. A number of additions

generated from the system should be able to differentiate with chimney-featured

and modifications can be investigated for the process to make it tackle more diverse

morphologies in order to create a pressure gradient which allows hot air to travel upwards

parameters and attributes.

and escape out the top. Another morphological adaptation that could take place in

System Adaptation for Other Context

hydroponic regions is to learn from localised step wells in Hampi. It is a typical example of how the socio-cultural as well as climatic requirements are embedded within building

There are several aspects that could be further revisited or refined. One of them is to

typology. Analogously, generated block geometries could be specifically developed in

discuss the potential ways how the evolutionary system could be adapted into other

order to incorporate water storage system in design. Critical Analysis

233


8.2 System Evaluation and Future Development

Aggregation Strategies Further research could be into revisiting the local geometries and derive other possible aggregation strategies. As the embodiment of quantified spatial parameters, the generated blocks are aggregated with elaboration to avoid deformation that may affect the parameters. However, such distortion-free aggregation logic always produces urban fabrics composed by orthogonal oriented geometries. A possible discussion could be about the trading off between achieving the spatial parameters and the attaining of greater flexibility of geometric formation. Subdivision could be operated on generated block morphologies that allows each divided part to have the capacity of individual adjustment. The blocks will be controlled by bounding geometries that exert moderate deformation for each part of the block. And in this way the geometries are enabled to attain greater flexibility in aggregation patterns as well as to fit in irregular plot footprints.

234

SynchroniCity


Material Differentiation Differentiation on the basis of material application would be another study that can be pursued. Using local low cost material would not only and economic sustainability but also create an identity that is representative of that location. The images show some examples where the local material combined with recycled material can used. And usage of such location specific materials become part of the process.

Fig 8.2.3 Liu Jiakun’s Rebirth Brick Project in Venice Architecture Biennale 08 As is also an inevitable element of indigenous architectures, localised material could be further discussed at the next stage. Liu Jiakun’s Rebirth Brick Project shows the potential of using recycled local architectural material. The bricks are recycled and sterilized from demolished old buildings. And the fractured material segments are mixed with wheat straws that act as reinforcing fibres.

As the hollow brick is a composite of used material debris, added cement and wheat straws, it is light-weighted and can be produced easily with semi-manual leveraging tools that are widely used in China by the local crafting industry. The composite brick could be an appropriate choice for 3-5 storey residences for lower income communities.

Fig 8.2.4 Details of Wang Shu’s Ningbo History Museum Bamboo mould concrete is the material produced by novel concrete forming techniques devised by Wang Shu in several of his recent projects. Strips of bamboo are applied to form the mould rather than the conventional planks of wood. The thin strips of bamboo can benefit in terms of low-tech fabrication

as well as in creating context-rooted facade textures. Different from the hollow bricks, the bamboo mould concrete could be the ideal material for 5-8 storey residences for higher income communities.

Critical Analysis

235


8.2 System Evaluation and Future Development Type 1 16 m2

Type 2 32 m2

Type 5 48 m2

Type 3 48 m2

Type 6 64 m2

Type 4 64 m2

Type 7 64 m2

Architectural Detail Possibility The process in the future would need to establish detail

plans were made taking into reference of the planning aspects

architectural requirements in terms of floor layouts and services.

followed in Mumbai and Beijing. Different kinds of housing units

A small exercise was conducted to attempt to accommodate

can be generated for different requirements. The houses can be set

different arrangements of housing units according to the urban

within the block in a tetris interlocking arrangements. The semi-

requirements. Varying from one room studio apartments to up

public spaces would be located at the entrance of each house unit.

three bedroom apartments with single or doubled floored. The 236

SynchroniCity


Type 12 90 m2

Type 13 90 m2

Type 14 90 m2

Critical Analysis

237


238

SynchroniCity


APPENDIX

Appendix

239


Block Generation Evaluation 1- Sky View Factor Large Plot Typology 1 (Q)

240

Q

D

Large Plot Typology 2 (QD)

80% 20%

Q

D

50% 50%

Large Plot Typology 3 (D)

Q

D

20% 80%

L_Q_08

SVF: 0.60

L_QD_08

SVF: 0.70

L_D_08

SVF: 0.51

L_Q_07

SVF: 0.59

L_QD_07

SVF: 0.68

L_D_07

SVF: 0.42

L_Q_06

SVF: 0.72

L_QD_06

SVF: 0.65

L_D_06

SVF: 0.42

L_Q_05

SVF: 0.59

L_QD_05

SVF: 0.48

L_D_05

SVF: 0.38

L_Q_04

SVF: 0.61

L_QD_04

SVF: 0.62

L_D_04

SVF: 0.50

L_Q_03

SVF: 0.53

L_QD_03

SVF: 0.52

L_D_03

SVF:0.53

L_Q_02

SVF: 0.53

L_QD_02

SVF: 0.65

L_D_02

SVF:0.48

L_Q_01

SVF: 0.59

L_QD_01

SVF: 0.69

L_D_01

SVF: 0.54

SynchroniCity


Medium Plot Typology 1 (Q)

Q

D

80% 20%

Medium Plot Typology 2 (QD)

Q

D

50% 50%

Medium Plot Typology 3 (D)

Q

D

20% 80%

M_Q_08

SVF: 0.74

M_QD_08

SVF: 0.66

M_D_08

SVF: 0.66

M_Q_07

SVF: 0.74

M_QD_07

SVF: 0.67

M_D_07

SVF: 0.71

M_Q_06

SVF: 0.67

M_QD_06

M_D_06

SVF: 0.66

M_Q_05

SVF: 0.63

M_QD_05

M_D_05

SVF: 0.74

M_Q_04

SVF: 0.61

M_QD_04

M_D_04

SVF: 0.47

M_Q_03

SVF: 0.72

M_QD_03

SVF: 0.71

M_D_03

SVF: 0.43

M_Q_02

SVF: 0.63

M_QD_02

SVF: 0.74

M_D_02

SVF: 0.43

M_Q_01

SVF: 0.76

M_QD_01

SVF: 0.64

M_D_01

SVF: 0.45

SVF: 0.74

SVF: 0.68

SVF: 0.63

Appendix

241


Block Generation Evaluation 1- Sky View Factor Small Plot Typology 1 (Q)

242

Q

D

Small Plot Typology 2 (QD)

80% 20%

Q

D

50% 50%

Small Plot Typology 3 (D)

Q

D

20% 80%

S_Q_08

SVF: 0.81

S_QD_08

SVF: 0.63

S_D_08

SVF: 0.84

S_Q_07

SVF: 0.83

S_QD_07

SVF: 0.77

S_D_07

SVF: 0.80

S_Q_06

SVF: 0.62

S_QD_06

SVF: 0.81

S_D_06

SVF: 0.76

S_Q_05

SVF: 0.81

S_QD_05

SVF: 0.85

S_D_05

SVF: 0.86

S_Q_04

SVF: 0.79

S_QD_04

SVF: 0.72

S_D_04

SVF: 0.80

S_Q_03

SVF: 0.87

S_QD_03

SVF: 0.81

S_D_03

SVF: 0.75

S_Q_02

SVF: 0.82

S_QD_02

SVF: 0.79

S_D_02

SVF: 0.71

S_Q_01

SVF: 0.83

S_QD_01

SVF: 0.73

S_D_01

SVF: 0.84

SynchroniCity


Evaluation Criteria

Large Plot

Typology 1 (Q) Typology 2 (QD)

Sky View Factor

Typology 3 (D)

Medium Plot

Sky View Factor

Sky View Factor

Typology 1 (Q)

Sky View Factor

Typology 2 (QD)

Sky View Factor

Typology 3 (D)

Sky View Factor

Small Plot

Typology 1 (Q)

Sky View Factor

Typology 2 (QD)

Sky View Factor

Typology 3 (D)

Sky View Factor

Generated Block Individuals

Average

L_Q_01

L_Q_02

L_Q_03

L_Q_04

L_Q_05

L_Q_06

L_Q_07

L_Q_08

0.59

0.53

0.53

0.61

0.59

0.72

0.59

0.60

L_QD_01

L_QD_02

L_QD_03

L_QD_04

L_QD_05

L_QD_06

L_QD_07

L_QD_08

0.69

0.65

0.52

0.62

0.48

0.65

0.68

0.70

L_D_01

L_D_02

L_D_03

L_D_04

L_D_05

L_D_06

L_D_07

L_D_08

0.54

0.48

0.53

0.50

0.38

0.42

0.42

0.51

M_Q_01

M_Q_02

M_Q_03

M_Q_04

M_Q_05

M_Q_06

M_Q_07

M_Q_08

0.76

0.63

0.72

0.61

0.63

0.67

0.74

0.74

M_QD_01

M_QD_02

M_QD_03

M_QD_04

M_QD_05

M_QD_06

M_QD_07

M_QD_08

0.64

0.74

0.71

0.63

0.68

0.74

0.67

0.66

M_D_01

M_D_02

M_D_03

M_D_04

M_D_05

M_D_06

M_D_07

M_D_08

0.45

0.43

0.43

0.47

0.74

0.66

0.71

0.66

S_Q_01

S_Q_02

S_Q_03

S_Q_04

S_Q_05

S_Q_06

S_Q_07

S_Q_08

0.83

0.82

0.87

0.79

0.81

0.62

0.83

0.81

S_QD_01

S_QD_02

S_QD_03

S_QD_04

S_QD_05

S_QD_06

S_QD_07

S_QD_08

0.73

0.79

0.81

0.72

0.85

0.81

0.77

0.63

S_D_01

S_D_02

S_D_03

S_D_04

S_D_05

S_D_06

S_D_07

S_D_08

0.84

0.71

0.75

0.80

0.86

0.76

0.80

0.84

0.60 0.62 0.47

0.69 0.68 0.57

0.80 0.76 0.80

Sky View Factor < 0.5

Appendix

243


Typology 3

Typology 2

Typology 1

Block Generation Catalogue- Quality and Density Parameter Values

Q

80% D

20% L_Q_01

L_Q_03

L_Q_05

L_Q_07

L_QD_03

L_QD_04

L_QD_06

L_QD_07

L_D_01

L_D_03

L_D_04

Q

50% D

50%

Q

20% D

80%

Large Plot

FAR TBA (m )

Typology 3

Typology 2

Typology 1

2

244

Q

80% D

20%

Q Quality

D Density

Size: 1600 m2

Value

L_D_08

OSR

Score

Value

CL

Score

SFR

Value

Score

Value

EV Score

Value

Score

Quality Score

Density Score

Final Score

L_Q_01

8691

1.81

0.55

20%

1.00

42

0.64

11%

0.52

1.30

0.62

5.29

5.43

10.72

L_Q_02

6800

1.42

0.28

14%

0.63

28

1.00

10%

0.67

1.00

0.48

4.77

4.25

9.02

L_Q_03

6324

1.32

0.22

14%

0.63

32

0.94

6%

1.00

1.20

0.57

5.28

3.95

9.23

L_Q_04

8006

1.67

0.45

17%

0.84

40

0.70

10%

0.67

1.00

0.48

4.94

5.00

9.94

L_Q_05

7958

1.66

0.45

17%

0.84

47

0.49

10%

0.67

0.90

0.43

4.58

4.97

9.55

L_Q_06

8774

1.83

0.56

14%

0.63

60

0.10

11%

0.52

1.30

0.62

3.50

5.48

8.98

L_Q_07

8685

1.81

0.55

16%

0.78

26

1.00

9%

0.83

1.10

0.52

5.48

5.43

10.91

L_Q_08

7142

1.49

0.32

16%

0.78

31

0.97

8%

1.00

1.10

0.52

5.68

4.46

10.14

Average:

7798

1.62

0.42

16%

0.77

38

0.73

9%

0.74

1.11

0.53

4.94

4.87

9.81

L_QD_01

9645

2.01

0.66

14%

0.63

52

0.34

11%

0.52

1.00

0.48

2.60

10.05

12.64

L_QD_02

10035

2.09

0.70

14%

0.63

37

0.79

11%

0.52

1.20

0.57

3.14

10.45

13.59

Q

L_QD_03

9309

1.94

0.63

14%

0.63

48

0.46

11%

0.52

1.20

0.57

2.81

9.70

12.51

50%

L_QD_04

9939

2.07

0.69

14%

0.63

57

0.19

11%

0.52

1.30

0.62

2.59

10.35

12.94

D

L_QD_05

9693

2.02

0.67

14%

0.63

51

0.37

11%

0.52

1.20

0.57

2.72

10.10

12.82

50%

L_QD_06

9741

2.03

0.67

14%

0.63

27

1.00

11%

0.52

0.90

0.43

3.21

10.15

13.36

L_QD_07

9501

1.98

0.65

13%

0.55

48

0.46

11%

0.52

1.30

0.62

2.70

9.90

12.60

L_QD_08

10928

2.28

0.77

13%

0.55

50

0.40

11%

0.52

0.80

0.38

2.40

11.38

13.78

Average:

9849

2.05

0.68

14%

0.61

46

0.50

11%

0.52

1.11

0.53

2.77

10.26

13.03

Q

20% D

80%

L_D_01

14889

3.10

1.00

12%

0.46

60

0.10

12%

0.37

1.20

0.57

1.18

21.71

22.89

L_D_02

15802

3.29

1.00

12%

0.46

58

0.16

11%

0.52

1.40

0.67

1.36

23.04

24.40

L_D_03

14547

3.03

1.00

11%

0.36

59

0.13

11%

0.52

1.20

0.57

1.16

21.21

22.38

L_D_04

13155

2.74

0.93

12%

0.46

46

0.52

13%

0.22

1.50

0.71

1.42

19.18

20.61

L_D_05

12387

2.58

0.88

13%

0.55

55

0.25

13%

0.22

1.20

0.57

1.28

18.06

19.35

L_D_06

14794

3.08

1.00

12%

0.46

46

0.52

11%

0.52

1.40

0.67

1.58

21.57

23.15

L_D_07

14544

3.03

1.00

13%

0.55

55

0.25

12%

0.37

1.60

0.76

1.49

21.21

22.70

L_D_08

14794

3.08

1.00

12%

0.46

60

0.10

12%

0.37

1.60

0.76

1.29

21.57

22.87

Average:

14364

2.99

0.98

12%

0.47

55

0.25

12%

0.39

1.39

0.66

1.35

20.95

22.29

Q Quality

OSR

D Density

FAR

SynchroniCity

CL

SFR

EV

Total Built Area (TBA) Floor Area Ratio (FAR) Open Space Ratio(OSR) Circulation Length (CL)

South Facing Surface Ratio (SFR) Enclosure Value (EV) Sky View Factor (SVF)

Eliminated in Evaluation 1- Sky View Factor Eliminated in Evaluation 2- Architectural Aspects


Typology 1 Typology 2 Typology 3

Q

80% D

20% M_Q_01

M_Q_02

M_Q_03

M_Q_05

M_QD_01

M_QD_04

M_QD_06

M_QD_07

M_D_05

M_D_06

M_D_07

M_D_08

Q

50% D

50%

Q

20% D

80%

Medium Plot

FAR

Typology 3

Typology 2

Typology 1

Size: 1024 m2

Q

80% D

20%

Q Quality

D Density

OSR

CL

SFR

EV

TBA (m2)

Value

Score

Value

Score

Value

Score

Value

Score

Value

Score

Quality Score

Density Score

Final Score

M_Q_01

5984

1.95

0.63

17%

0.84

24

1.00

11%

1.00

1.20

0.57

5.95

5.84

11.80

M_Q_02

6698

2.18

0.73

14%

0.63

35

0.85

11%

0.83

1.10

0.52

4.85

6.54

11.39

M_Q_03

5696

1.85

0.58

17%

0.84

36

0.82

12%

0.80

1.20

0.57

5.42

5.56

10.98

M_Q_04

5792

1.89

0.60

17%

0.84

30

1.00

12%

1.00

1.20

0.57

5.95

5.66

11.61

M_Q_05

6266

2.04

0.67

15%

0.71

27

1.00

12%

1.00

1.00

0.48

5.45

6.12

11.57

M_Q_06

5018

1.63

0.43

15%

0.71

24

1.00

12%

1.00

0.90

0.43

5.39

4.90

10.29

M_Q_07

5696

1.85

0.58

17%

0.84

28

1.00

10%

1.00

1.10

0.52

5.89

5.56

11.45

M_Q_08

3722

1.21

0.17

24%

1.00

0

1.00

11%

1.00

0.90

0.43

6.20

3.63

9.83

Average:

5609

1.83

0.55

17%

0.80

26

0.96

11%

0.95

1.08

0.51

5.64

5.48

11.11

M_QD_01

5696

1.85

0.58

14%

0.63

25

1.00

12%

0.52

1.20

0.57

3.35

9.27

12.62

M_QD_02

5690

1.85

0.58

11%

0.36

27

1.00

12%

0.52

1.00

0.48

2.72

9.26

11.98

Q

M_QD_03

5888

1.92

0.61

14%

0.63

56

0.22

10%

0.37

1.20

0.57

2.42

9.58

12.00

50%

M_QD_04

5984

1.95

0.63

14%

0.63

30

1.00

12%

0.37

1.20

0.57

3.20

9.74

12.94

D

M_QD_05

5354

1.74

0.51

12%

0.46

25

1.00

11%

0.37

1.10

0.52

2.81

8.71

11.53

50%

M_QD_06

5840

1.90

0.61

17%

0.84

25

1.00

11%

0.37

1.20

0.57

3.62

9.51

13.13

Q

20% D

80%

M_QD_07

7286

2.37

0.81

16%

0.78

37

0.79

11%

0.67

1.40

0.67

3.69

11.86

15.55

M_QD_08

5984

1.95

0.63

16%

0.78

51

0.37

11%

0.52

1.40

0.67

3.12

9.74

12.86

Average:

5965

1.94

0.62

14%

0.64

35

0.80

11%

0.46

1.21

0.58

3.12

9.71

12.82

M_D_01

6224

2.03

0.67

16%

0.78

25

1.00

10%

0.37

1.40

0.67

2.16

14.18

16.34

M_D_02

7142

2.32

0.79

16%

0.78

50

0.40

10%

0.37

1.50

0.71

1.83

16.27

18.10

M_D_03

6710

2.18

0.74

17%

0.84

57

0.19

13%

0.67

1.40

0.67

1.92

15.29

17.21

M_D_04

5888

1.92

0.61

16%

0.78

30

1.00

12%

0.37

1.50

0.71

2.19

13.42

15.60

M_D_05

6854

2.23

0.75

17%

0.84

50

0.40

11%

0.52

1.30

0.62

1.93

15.62

17.55

M_D_06

7046

2.29

0.78

16%

0.78

51

0.37

11%

0.52

1.50

0.71

1.90

16.06

17.95

M_D_07

6518

2.12

0.71

18%

0.90

47

0.49

10%

0.52

1.30

0.62

2.06

14.85

16.91

M_D_08

6176

2.01

0.66

16%

0.78

44

0.58

10%

0.52

1.30

0.62

1.97

14.07

16.04

Average:

6570

2.14

0.71

17%

0.81

44

0.55

11%

0.48

1.40

0.67

1.99

14.97

16.96

Q Quality

OSR

D Density

FAR

CL

SFR

EV Total Built Area (TBA)

Floor Area Ratio (FAR) Open Space Ratio(OSR) Circulation Length (CL)

South Facing Surface Ratio (SFR) Enclosure Value (EV) Sky View Factor (SVF)

Eliminated in Evaluation 1- Sky View Factor Eliminated in Evaluation 2- Architectural Aspects Appendix

245


Typology 3

Typology 2

Typology 1

Block Generation Catalogue- Quality and Density Parameter Values

Q

80% D

20% S_Q_01

S_Q_02

S_Q_04

S_Q_06

S_QD_01

S_QD_05

S_QD_06

S_QD_08

S_D_02

S_D_06

S_D_07

Q

50% D

50%

Q

20% D

80%

Small Plot

FAR TBA (m )

Typology 3

Typology 2

Typology 1

2

246

Q

80% D

20%

Q Quality

D Density

Size: 576 m2

S_D_08

OSR

CL

SFR

Value

Score

Value

Score

Value

Score

EV

Value

Score

Value

Score

Quality Score

Density Score

Final Score

S_Q_01

1738

1.01

0.08

44%

1.00

19

1.00

15%

0.08

0.80

0.38

4.85

3.02

7.86

S_Q_02

1795

1.04

0.10

32%

1.00

17

1.00

10%

0.67

0.90

0.43

5.74

3.12

8.85

S_Q_03

1784

1.03

0.08

36%

1.00

28

1.00

13%

0.22

1.00

0.48

5.17

3.10

8.27

S_Q_04

1795

1.04

0.10

32%

1.00

29

1.00

13%

0.22

1.00

0.48

5.17

3.12

8.29

S_Q_05

2509

1.45

0.30

15%

0.71

16

1.00

9%

0.83

0.70

0.33

5.02

4.36

9.37

S_Q_06

2611

1.51

0.34

22%

1.00

35

0.85

9%

0.83

1.10

0.52

5.89

4.53

10.42

S_Q_07

1751

1.01

0.08

44%

1.00

24

1.00

16%

0.08

0.80

0.38

4.85

3.04

7.89

S_Q_08

2509

1.45

0.30

15%

0.71

16

1.00

9%

0.83

0.70

0.33

5.02

4.36

9.37

Average:

2062

1.19

0.17

30%

0.93

23

0.98

12%

0.47

0.88

0.42

5.21

3.58

8.79

S_QD_01

3043

1.76

0.52

19%

0.95

30

1.00

13%

0.22

0.80

0.38

3.50

8.80

12.31

S_QD_02

2899

1.68

0.46

20%

1.00

25

1.00

11%

0.52

0.90

0.43

3.95

8.39

12.34

Q

S_QD_03

2275

1.32

0.22

26%

1.00

31

0.97

15%

0.08

1.00

0.48

3.53

6.58

10.11

50%

S_QD_04

2899

1.68

0.46

20%

1.00

27

1.00

10%

0.67

0.90

0.43

4.10

8.39

12.49

D

S_QD_05

3146

1.82

0.56

25%

1.00

37

0.79

11%

0.52

1.10

0.52

3.83

9.10

12.94

50%

S_QD_06

2323

1.34

0.24

25%

1.00

29

1.00

11%

0.52

0.90

0.43

3.95

6.72

10.67

S_QD_07

2899

1.68

0.46

10%

0.24

27

1.00

10%

0.67

0.90

0.43

2.58

8.39

10.97

Q

20% D

80%

S_QD_08

2611

1.51

0.34

22%

1.00

36

0.82

9%

0.83

1.10

0.52

4.17

7.55

11.73

Average:

2762

1.60

0.41

21%

0.90

30

0.95

11%

0.50

0.95

0.45

3.70

7.99

11.69

S_D_01

3578

2.07

0.69

21%

1.00

48

0.46

10%

0.67

1.30

0.62

2.25

14.49

16.74

S_D_02

3043

1.76

0.52

19%

0.95

21

1.00

10%

0.67

1.00

0.48

2.43

12.33

14.75

S_D_03

2899

1.68

0.46

20%

1.00

28

1.00

10%

0.67

1.20

0.57

2.54

11.74

14.29

S_D_04

3434

1.99

0.65

23%

1.00

42

0.64

12%

0.37

1.00

0.48

2.09

13.91

16.00

S_D_05

3578

2.07

0.69

22%

1.00

45

0.55

10%

0.67

0.90

0.43

2.19

14.49

16.68

S_D_06

3146

1.82

0.56

25%

1.00

45

0.55

13%

0.22

1.30

0.62

2.03

12.74

14.78

S_D_07

3578

2.07

0.69

22%

1.00

27

1.00

10%

0.67

1.10

0.52

2.52

14.49

17.01

S_D_08

3146

1.82

0.56

25%

1.00

42

0.64

10%

0.67

1.10

0.52

2.30

12.74

15.04

Average:

3300

1.91

0.60

22%

0.99

37

0.73

11%

0.58

1.11

0.53

2.29

13.37

15.66

Q Quality

OSR

D Density

FAR

SynchroniCity

CL

SFR

EV

Total Built Area (TBA) Floor Area Ratio (FAR) Open Space Ratio(OSR) Circulation Length (CL)

South Facing Surface Ratio (SFR) Enclosure Value (EV) Sky View Factor (SVF)

Eliminated in Evaluation 1- Sky View Factor Eliminated in Evaluation 2- Architectural Aspects


Genotype Archive of Generated Block Catalogues

Appendix

247


Cluster Footprint Generation Catalogue- Parameter Values

1 - 0.8 - 0.7 Standard Deviation Average Area (m2) Number of Spaces Porousity Ratio

97.0

130.5

108.3

126.5

97.0

108.6

41.2

44.0

40.0

30.7

39.0

35.3

6

5

7

6

6

4

4.4

3.0

4.8

2.3

4.3

1.3

35.8

51.3

56.6

66.6

69.3

77.3

31.9

22.9

35.9

49.4

27.5

35.8

7

7

7

8

8

4

3.6

1.8

4.3

8.1

3.6

1.3

101.1

107.4

118.5

70.8

93.2

96.1

27.0

49.6

47.7

28.2

37.5

36.1

8

8

6

9

8

7

3.5

8.0

5.5

4.7

6.0

4.4

0.8 - 0.8 - 0.8 Standard Deviation Average Area (m2) Number of Spaces Porousity Ratio

0.7 - 0.8 - 1 Standard Deviation Average Area (m2) Number of Spaces Porousity Ratio 248

SynchroniCity


6.5

97.0

108.6

97.2

112.4

111.5

108.1

39.0

35.3

34.6

31.5

26.6

35.1

6

4

5

4

5

7

3

4.3

1.3

2.5

0.9

1.1

4.3

6.6

69.3

77.3

103.7

86.9

75.3

62.8

9.4

27.5

35.8

36.5

32.0

39.2

43.0

8

4

6

6

5

4

1

3.6

1.3

3.4

2.7

2.7

2.2

0.8

93.2

96.1

81.2

80.1

85.9

102.4

8.2

37.5

36.1

29.0

24.6

32.6

40.6

8

7

8

11

9

8

6.0

4.4

4.0

5.1

5.7

6.6

0.7

7

Appendix

249


Cluster Generation Catalogue- Quality and Density Parameter Values

250

SynchroniCity

Q

Q

Cluster 1

Cluster 1

C_Q_01

371 Wh/m2

16%

C_Q_06

384 Wh/m2

9%

C_Q_02

398 Wh/m2

6%

C_Q_07

380 Wh/m2

6%

C_Q_03

388 Wh/m2

8%

C_Q_08

377 Wh/m2

9%

C_Q_04

390 Wh/m2

8%

C_Q_09

390 Wh/m2

7%

C_Q_05

389 Wh/m2

8%

C_Q_10

360 Wh/m2

11%


Q

Q

D

Cluster 2

D

Cluster 2

C_QD_01

392 Wh/m2

9%

C_QD_06

406 Wh/m2

5%

C_QD_02

387 Wh/m2

10%

C_QD_07

399 Wh/m2

7%

C_QD_03

413 Wh/m2

6%

C_QD_08

412 Wh/m2

6%

C_QD_04

420 Wh/m2

5%

C_QD_09

400 Wh/m2

8%

C_QD_05

413 Wh/m2

6%

C_QD_10

413 Wh/m2

7%

Appendix

251


Cluster Generation Catalogue- Quality and Density Parameter Values

252

SynchroniCity

D

D

Cluster 3

Cluster 3

C_D_01

403 Wh/m2

9%

C_D_06

375 Wh/m2

5%

C_D_02

410 Wh/m2

10%

C_D_07

385 Wh/m2

7%

C_D_03

410 Wh/m2

6%

C_D_08

394 Wh/m2

6%

C_D_04

385 Wh/m2

5%

C_D_09

401 Wh/m2

8%

C_D_05

408 Wh/m2

6%

C_D_10

391 Wh/m2

7%


Q Quality

D Density

FAR PSA (m ) 2

C_Q_01

892

BEC

ISR

SAP

EV

TBA (m )

POP

Value

Score

Value

Score

Value

Score

Value

Score

Value

Score

Quality Score

Density Score

Final Score

25642

1282

1.21

0.11

4%

0.64

371

0.99

16%

0.49

0.45

1.00

3.76

0.55

4.31

2

C_Q_02

974

25064

1253

1.18

0.09

3%

0.73

398

0.71

6%

0.83

0.61

0.19

3.19

0.45

3.64

C_Q_03

1007

25774

1289

1.21

0.11

8%

0.28

388

0.85

8%

1.00

0.57

0.32

2.73

0.55

3.28

C_Q_04

925

25855

1293

1.22

0.11

0%

1.00

390

0.83

8%

1.00

0.50

0.64

4.47

0.55

5.02

Q

C_Q_05

1100

25764

1288

1.21

0.11

0%

1.00

389

0.84

8%

1.00

0.41

1.00

4.84

0.55

5.39

Cluster 1

C_Q_06

1049

24682

1234

1.16

0.08

4%

0.64

384

0.90

9%

0.95

0.52

0.53

3.66

0.40

4.06

C_Q_07

960

23750

1188

1.12

0.06

1%

0.91

380

0.93

6%

0.83

0.42

1.00

4.58

0.30

4.88

Q

D

Cluster 2

C_Q_08

1030

25894

1295

1.22

0.11

2%

0.82

377

0.96

9%

0.95

0.58

0.28

3.83

0.55

4.38

C_Q_09

1000

24268

1213

1.14

0.07

0%

1.00

390

0.83

7%

0.95

0.68

0.04

3.82

0.35

4.17

C_Q_10

1065

25951

1298

1.22

0.11

0%

1.00

360

1.00

12%

0.49

0.61

0.19

3.68

0.55

4.23

Average:

1000

25265

1263

1.19

0.10

2%

0.80

383

0.88

9%

0.85

0.53

0.52

3.86

0.48

4.34

C_QD_01

849

30338

1517

1.51

0.54

7%

0.37

392

0.8

9%

0.95

0.64

0.12

2.61

2.70

5.31

C_QD_02

1032

29985

1499

1.49

0.49

2%

0.82

387

0.87

10%

0.83

0.78

0.01

3.35

2.45

5.80

C_QD_03

865

30250

1513

1.50

0.53

4%

0.64

413

0.38

6%

0.83

0.51

0.61

3.10

2.65

5.75

C_QD_04

847

29855

1493

1.48

0.48

1%

0.91

420

0.38

5%

0.83

0.51

0.58

3.61

2.40

6.01

C_QD_05

862

29040

1452

1.44

0.38

5%

0.55

413

0.38

6%

0.83

0.50

0.62

2.93

1.90

4.83

C_QD_06

860

30425

1521

1.51

0.55

3%

0.73

406

0.52

5%

0.83

0.62

0.17

2.98

2.75

5.73

C_QD_07

887

30256

1513

1.50

0.53

8%

0.28

399

0.69

7%

0.95

0.62

0.15

2.35

2.65

5.00

C_QD_08

835

30400

1520

1.51

0.55

5%

0.55

412

0.38

6%

0.83

0.49

0.70

3.01

2.75

5.76

C_QD_09

888

30138

1507

1.50

0.51

3%

0.73

400

0.67

8%

1.00

0.55

0.41

3.54

2.55

6.09

C_QD_10

866

30017

1501

1.49

0.50

5%

0.55

413

0.38

7%

0.95

0.50

0.65

3.08

2.50

5.58

Average:

879

30071

1504

1.49

0.51

4%

0.61

406

0.55

7%

0.88

0.57

0.40

3.06

2.53

5.59

C_D_01

867

38352

1918

1.98

0.99

11%

0.10

403

0.60

8%

1.00

0.51

0.58

2.38

4.95

7.33

C_D_02

815

37082

1854

1.91

0.98

14%

0.10

410

0.38

7%

0.95

0.61

0.19

1.72

4.90

6.62

C_D_03

818

37144

1857

1.91

0.98

6%

0.46

410

0.38

10%

0.83

0.66

0.08

2.21

4.90

7.11

C_D_04

889

36851

1843

1.90

0.97

9%

0.19

385

0.89

11%

0.49

0.72

0.01

1.77

4.85

6.62

D

C_D_05

818

36977

1849

1.91

0.98

12%

0.10

408

0.46

6%

0.83

0.72

0.01

1.50

4.90

6.40

Cluster 3

C_D_06

878

36599

1830

1.89

0.97

8%

0.28

375

0.97

10%

0.83

0.78

0.01

2.37

4.85

7.22

C_D_07

890

36851

1843

1.90

0.97

4%

0.64

385

0.89

11%

0.49

0.72

0.01

2.67

4.85

7.52

C_D_08

894

38099

1905

1.96

0.99

9%

0.19

394

0.77

9%

0.95

0.65

0.09

2.19

4.95

7.14

C_D_09

866

38352

1918

1.98

0.99

8%

0.28

401

0.65

8%

1.00

0.63

0.13

2.34

4.95

7.29

C_D_10

860

39062

1953

2.01

1.00

12%

0.10

391

0.82

9%

0.95

0.67

0.05

2.02

5.00

7.02

Average:

860

37537

1877

1.93

0.98

9%

0.16

396

0.68

9%

0.83

0.67

0.12

1.96

4.91

6.87

Radiance Wh / m2 450 430 410 390 370 350 330 310 290 270 250

Q Quality

BEC

D Density

FAR

ISR

SAP

Floor Area Ratio (FAR) EV Block Edge Condition(BEC) Incident Solar Radiation (ISR) Shaded Area Proportion (SAP) Enclosure Value (EV)

Public Space Area (PSA) Total Built Area (TBA)

Eliminated in Evaluation - Ground Network

Appendix

253


Evaluation : Block Level Q

D

80% 20%

Large Plot- Typology 1 (Q)

1 4 Q

D

50% 50%

D

20% 80%

2 3 4

L_QD_02 SVF: 0.65

2 4

L_QD_03 SVF: 0.52

L_QD_04 SVF: 0.62

Large Plot- Typology 3 (D)

L_D_01 SVF: 0.54

Evaluation 1

Large Plot

Evaluation Criteria 1

Typology 1 (Q) Typology 2 (QD) Typology 3 (D) Sky View Factor < 0.5 SynchroniCity

Sky View Factor Sky View Factor Sky View Factor

L_D_03 SVF: 0.53

L_D_02 SVF: 0.48

Evaluation 2

Sky View Factor L_D_01

254

L_Q_04 SVF: 0.61

Large Plot- Typology 2 (QD)

L_QD_01 SVF: 0.69

Q

3 4 L_Q_03 SVF: 0.53

L_Q_02 SVF: 0.53

L_Q_01 SVF: 0.59

Architectural Aspects L_D_01

L_D_04 SVF: 0.50

Selected Individuals

Generated Block Individuals

Average

L_Q_01

L_Q_02

L_Q_03

L_Q_04

L_Q_05

L_Q_06

L_Q_07

L_Q_08

0.59

0.53

0.53

0.61

0.59

0.72

0.59

0.60

L_QD_01

L_QD_02

L_QD_03

L_QD_04

L_QD_05

L_QD_06

L_QD_07

L_QD_08

0.69

0.65

0.52

0.62

0.48

0.65

0.68

0.70

L_D_01

L_D_02

L_D_03

L_D_04

L_D_05

L_D_06

L_D_07

L_D_08

0.54

0.48

0.53

0.50

0.38

0.42

0.42

0.51

0.60 0.62 0.47

Evaluation Criteria 2 Architectural Aspects

1 2

Elevated network Informal passway

3 Extension capacity 4 Interconnection capacity Selected Individuals


1 3 4

L_Q_05 SVF: 0.59

L_QD_05 SVF: 0.48

L_D_05 SVF: 0.38

L_Q_06 SVF: 0.72

2 3 4

L_QD_06 SVF: 0.65

L_D_06 SVF: 0.42

1 4

2 3 4

L_Q_07 SVF: 0.59

L_Q_08 SVF: 0.60

L_QD_07 SVF: 0.68

L_QD_08 SVF: 0.70

L_D_07 SVF: 0.42

L_D_08 SVF: 0.51

Appendix

255


Evaluation : Block Level Q

D

80% 20%

Medium Plot- Typology 1 (Q)

2 3 Q

D

50% 50%

D

20% 80%

M_QD_01 SVF: 0.64

M_Q_03 SVF: 0.72

M_Q_04 SVF: 0.61

M_QD_02 SVF: 0.74

M_QD_03 SVF: 0.71

M_D_02 SVF: 0.43

M_D_03 SVF: 0.43

1 3

M_QD_04 SVF: 0.63

Medium Plot- Typology 3 (D)

M_D_01 SVF: 0.45

Evaluation 1

Evaluation 2

Sky View Factor M_D_01

Medium Plot

Evaluation Criteria 1

Typology 1 (Q) Typology 2 (QD) Typology 3 (D) Sky View Factor < 0.5 256

2 4

M_Q_02 SVF: 0.63

Medium Plot- Typology 2 (QD)

1 3 4 Q

2 4

M_Q_01 SVF: 0.76

SynchroniCity

Sky View Factor Sky View Factor

Selected Individuals

Generated Block Individuals M_Q_01

Sky View Factor

Architectural Aspects M_D_01

M_D_04 SVF: 0.47

M_Q_02

M_Q_03

M_Q_04

M_Q_05

M_Q_06

Average M_Q_07

M_Q_08

0.76

0.63

0.72

0.61

0.63

0.67

0.74

0.74

M_QD_01

M_QD_02

M_QD_03

M_QD_04

M_QD_05

M_QD_06

M_QD_07

M_QD_08

0.64

0.74

0.71

0.63

0.68

0.74

0.67

0.66

M_D_01

M_D_02

M_D_03

M_D_04

M_D_05

M_D_06

M_D_07

M_D_08

0.45

0.43

0.43

0.47

0.74

0.66

0.71

0.66

Evaluation Criteria 2 Architectural Aspects

0.69

1 2

0.68

3 Extension capacity

Elevated network Informal passway

4 Interconnection capacity

0.57


3 4 M_Q_05 SVF: 0.63

M_QD_05 SVF: 0.68

2 3 4

M_D_05 SVF: 0.74

M_Q_06 SVF: 0.67

1 4

M_QD_06 SVF: 0.74

2 3 4

M_D_06 SVF: 0.66

M_Q_07 SVF: 0.74

1 3 4

M_QD_07 SVF: 0.67

3 4 M_D_07 SVF: 0.71

M_Q_08 SVF: 0.74

M_QD_08 SVF: 0.66

2 3

M_D_08 SVF: 0.66

Appendix

257


Evaluation : Block Level Q

D

80% 20%

Small Plot- Typology 1 (Q)

1 3 4 Q

D

50% 50%

D

20% 80%

S_Q_02 SVF: 0.82

S_QD_01 SVF: 0.73

S_QD_02 SVF: 0.79

1 3

S_QD_03 SVF: 0.81

S_QD_04 SVF: 0.72

S_D_03 SVF: 0.75

S_D_04 SVF: 0.80

S_Q_04 SVF: 0.79

Small Plot- Typology 3 (D)

1 4

S_D_01 SVF: 0.84

S_D_02 SVF: 0.71

Evaluation 1

Evaluation 2

Sky View Factor S_D_01

Small Plot

Evaluation Criteria 1

Typology 1 (Q) Typology 2 (QD) Typology 3 (D) Sky View Factor < 0.5 258

S_Q_03 SVF: 0.87

Small Plot- Typology 2 (QD)

1 3 Q

1 4

S_Q_01 SVF: 0.83

SynchroniCity

Sky View Factor Sky View Factor Sky View Factor

Architectural Aspects S_D_01

Selected Individuals

Generated Block Individuals

Average

S_Q_01

S_Q_02

S_Q_03

S_Q_04

S_Q_05

S_Q_06

S_Q_07

S_Q_08

0.83

0.82

0.87

0.79

0.81

0.62

0.83

0.81

S_QD_01

S_QD_02

S_QD_03

S_QD_04

S_QD_05

S_QD_06

S_QD_07

S_QD_08

0.73

0.79

0.81

0.72

0.85

0.81

0.77

0.63

S_D_01

S_D_02

S_D_03

S_D_04

S_D_05

S_D_06

S_D_07

S_D_08

0.84

0.71

0.75

0.80

0.86

0.76

0.80

0.84

0.80 0.76 0.80

Evaluation Criteria 2 Architectural Aspects

1 2

Elevated network Informal passway

3 Extension capacity 4 Interconnection capacity Selected Individuals


S_Q_05 SVF: 0.81

1 4

S_QD_05 SVF: 0.85

S_D_05 SVF: 0.86

1 3 4

S_Q_06 SVF: 0.62

1 3 4

S_QD_06 SVF: 0.81

1 4

S_D_06 SVF: 0.76

S_Q_07 SVF: 0.83

S_QD_07 SVF: 0.77

1 4

S_D_07 SVF: 0.80

S_Q_08 SVF: 0.81

1 4

S_QD_08 SVF: 0.63

1 4

S_D_08 SVF: 0.84

Appendix

259


Evaluation : Cluster Level

C_Q_01

C_Q_02

Q

Cluster 1 15 Blocks

x5 Large Block

x5 Medium Block

x5 Small Block

C_Q_03

3

3

3

Typology 2 Q D

2 1 0

2 1 0

2 1 0

Typology 3

0 1 2

0 1 2

0 1 2

Typology 1

Q

D

Block Design Catalogue

1 2

C_Q_04

Centralised network pattern Continuous ring streets

Cluster 1 Evaluation Cluster 1 individuals were generated to accommodate more number high quality blocks (typology 1). Overall, it was observed that the individuals consisted of higher porosity at the ground level. This also meant that the individuals contained many informal passageways that could provide the cluster with high connectivity. Thus, it would help to create more opportunities for social interaction within the cluster. Four best individuals out of ten were selected to form the cluster 1 design catalogue.

C_Q_05

260

SynchroniCity


C_Q_06

C_Q_07

C_Q_08

C_Q_09

C_Q_10

Public Space Area (m2)

Population

FAR

Incident Solar Radiation (Wh/m2)

Enclosure Value

Shaded Area

C_Q_01

892

1282

1.21

371

0.45

16%

C_Q_02

974

1253

1.18

398

0.61

6%

C_Q_03

1007

1289

1.21

388

0.57

8%

C_Q_04

925

1293

1.22

390

0.50

8%

C_Q_05

1100

1288

1.21

389

0.41

8%

C_Q_06

1049

1234

1.16

384

0.52

9%

C_Q_07

960

1188

1.12

380

0.42

6%

C_Q_08

1030

1295

1.22

377

0.58

9%

C_Q_09

1000

1213

1.14

390

0.68

7%

C_Q_10

1065

1298

1.22

360

0.61

12%

Average:

1000

1263

1.19

383

0.53

9%

Plot Area: 20148 m2 Appendix

261


Evaluation : Cluster Level

C_QD_01

C_QD_02

Q

D

Cluster 2 15 Blocks

x5 Large Block

x5

x5

Medium Block

Small Block

2 1 0

2 1 0

2 1 0

3

3

3

0 1 2

0 1 2

0 1 2

C_QD_03

Typology 1

Q

Typology 2 Q D Typology 3

D

Block Design Catalogue

1 2

Centralised network pattern Continuous ring streets

C_QD_04

Cluster 2 Evaluation The Cluster 2 individuals were generated with equal weighting on quality and density. They have showed less porosity and connectivity as compared to cluster 1, which had higher weighting on quality. This was because the majority of the blocks (typology 2) chosen for aggregation had equal weighting on quality and density. These clusters could provide a higher density within them while maintaining the spatial quality. As they consisted of fewer passageways, the public spaces were well integrated and C_QD_05

262

SynchroniCity

connected within the clusters.


C_QD_06

C_QD_07

C_QD_08

C_QD_09

C_QD_10

FAR

Incident Solar Radiation (Wh/m2)

Enclosure Value

Shaded Area

1517

1.51

392

0.64

9%

1032

1499

1.49

387

0.78

10%

865

1513

1.50

413

0.51

6%

C_QD_04

847

1493

1.48

420

0.51

5%

C_QD_05

862

1452

1.44

413

0.50

6%

C_QD_06

860

1521

1.51

406

0.62

5%

C_QD_07

887

1513

1.50

399

0.62

7%

Public Space Area (m2)

Population

C_QD_01

849

C_QD_02 C_QD_03

C_QD_08

835

1520

1.51

412

0.49

6%

C_QD_09

888

1507

1.50

400

0.55

8%

C_QD_10

866

1501

1.49

413

0.50

7%

Average:

879

1504

1.49

406

0.57

7%

Plot Area: 20148 m2 Appendix

263


Evaluation : Cluster Level

C_D_01

C_D_02

D

Cluster 3 15 Blocks

x5 Large Block

x5

x5

Medium Block

Small Block

2 1 0

2 1 0

2 1 0

0 1 2

0 1 2

0 1 2

3

3

3

C_D_03

Typology 1

Q

Typology 2 Q D Typology 3

D

Block Design Catalogue

1 2

Centralised network pattern Continuous ring streets

C_D_04

Cluster 3 Evaluation Cluster 3 individuals were generated with the aim to form a high density cluster. These clusters have achieved the highest FAR values of 2.01 while still maintain a high spatial quality as they consisted of the least amount of porosity and connectivity. While they were able to achieve some large open spaces at the ground level, the C_D_05

264

SynchroniCity

informal passageways within the blocks were at a minimum.


C_D_06

C_D_07

C_D_08

Public Space Area (m2) C_D_09

C_D_10

Population

FAR

Incident Solar Radiation (Wh/m2)

Enclosure Value

Shaded Area

C_D_01

867

1918

1.98

403

0.51

8%

C_D_02

815

1854

1.91

410

0.61

7%

C_D_03

818

1857

1.91

410

0.66

10%

C_D_04

889

1843

1.90

385

0.72

11%

C_D_05

818

1849

1.91

408

0.72

6%

C_D_06

878

1830

1.89

375

0.78

10%

C_D_07

890

1843

1.90

385

0.72

11%

C_D_08

894

1905

1.96

394

0.65

9%

C_D_09

866

1918

1.98

401

0.63

8%

C_D_10

860

1953

2.01

391

0.67

9%

Average:

860

1877

1.93

396

0.67

9%

Plot Area: 19401 m2 Appendix

265


Genetic Algorithm Scripts for Parameters

Geometry Generation Logic

Enclosure Value

Sky View Factor

266

SynchroniCity


South Facing Surface

Circulation Length

Incident Solar Radiation

Appendix

267


Genetic Algorithm Scripts for Geometry Aggregation Geometry Aggregation Logics

Building Archive for Generated Geometry Catalogue

268

SynchroniCity


Network Generation from osm format Maps

Network OďŹ&#x20AC;set for Space Syntax Analysis

Appendix

269


Genetic Algorithm Scripts for Spatial Configuration Evaluation

Spatial Network Analysis (Copyright (C) 2000-2010 University College London, Alasdair Turner) Source Code avalaible from: https://github.com/SpaceGroupUCL/Depthmap/blob/master/src/depthmap/AgentAnalysisDlg.cpp#L1

// AgentAnalysisDlg.cpp : implementation file // #include “stdafx.h” #include “depthmap.h” #include “AgentAnalysisDlg.h” #ifdef _DEBUG #define new DEBUG_NEW #undef THIS_FILE static char THIS_FILE[] = __FILE__; #endif ///////////////////////////////////////////////////////////////////////////// // CAgentAnalysisDlg dialog CAgentAnalysisDlg::CAgentAnalysisDlg(CWnd* pParent /*=NULL*/) : CDialog(CAgentAnalysisDlg::IDD, pParent) { //{{AFX_DATA_INIT(CAgentAnalysisDlg) m_release_location = -1; m_fov = 0; m_frames = 0; m_release_rate = 0.0; m_steps = 0; m_timesteps = 0; m_occlusion = -1; m_record_trails = FALSE; m_trail_count = 0; //}}AFX_DATA_INIT

}

m_trail_count = 50; m_occlusion = 0; m_gatelayer = -1; m_release_location = 0;

void CAgentAnalysisDlg::DoDataExchange(CDataExchange* pDX) { CDialog::DoDataExchange(pDX); //{{AFX_DATA_MAP(CAgentAnalysisDlg) DDX_Control(pDX, IDC_LAYER_SELECTOR, m_layer_selector); DDX_Radio(pDX, IDC_RELEASE_LOCATION, m_release_location); DDX_Text(pDX, IDC_FOV, m_fov); DDX_Text(pDX, IDC_FRAMES, m_frames); DDX_Text(pDX, IDC_RELEASE_RATE, m_release_rate); DDX_Text(pDX, IDC_STEPS, m_steps); DDX_Text(pDX, IDC_TIMESTEPS, m_timesteps); DDX_CBIndex(pDX, IDC_OCCLUSION, m_occlusion); DDX_Check(pDX, IDC_RECORD_TRAILS, m_record_trails); DDX_Text(pDX, IDC_TRAIL_COUNT, m_trail_count); DDV_MinMaxInt(pDX, m_trail_count, 1, 50); //}}AFX_DATA_MAP } BEGIN_MESSAGE_MAP(CAgentAnalysisDlg, CDialog) //{{AFX_MSG_MAP(CAgentAnalysisDlg) //}}AFX_MSG_MAP END_MESSAGE_MAP() ///////////////////////////////////////////////////////////////////////////// // CAgentAnalysisDlg message handlers BOOL CAgentAnalysisDlg::OnInitDialog() { CDialog::OnInitDialog(); for (size_t i = 0; i < m_names.size(); i++) { m_layer_selector.AddString( CString(m_names[i].c_str()) ); } m_layer_selector.SetCurSel(m_gatelayer + 1);

}

return TRUE; // return TRUE unless you set the focus to a control // EXCEPTION: OCX Property Pages should return FALSE

void CAgentAnalysisDlg::OnOK() { m_gatelayer = m_layer_selector.GetCurSel() - 1; }

270

CDialog::OnOK();

SynchroniCity


Visual Controllability Analysis

Appendix

271


272

SynchroniCity


BIBLIOGRAPHY

Bibliography

273


Image Links Page Nnumber

Link

13

http://www.buildinghappiness.org/wp-content/uploads/2011/11/shenzen-china-urban-growth1.jpg https://lh3.ggpht.com/g2orGFGiS5-ID1Dxbr1xuX6htgKE2w0FFNERcQONCWyHTKiG6JyNqtcEno_oQxciRkSR=s136 https://lh4.ggpht.com/nxqu8LY1sswkt7nl0jAPKZKPfgxbOlcXlA3Ot-gnA6z86NYlithQh7K0Qjk5-olMvX1bZiw=s114 https://lh5.ggpht.com/DNDCF6T6J1xgSaEJW4sByPf8cipOMVfUiHYEwJT3NXOpRY1WTsw2O0zqY2V_y_z3bEmKUQ=s151 http://traditions.cultural-china.com/chinaWH/upload/upfiles/2010-08/11/the_five_elements_of_chinese_culture512e89eb9cfcc925dee5.jpg http://www.thechinaguide.com/hutong_tour1/Hutong_Beijing_01.jpg copyright c peter danford http://www.soci.org/Chemistry-and-Industry/CnI-Data/2011/18/~/media/Images/CnI/Issue%20Image/2011/18/indiancity.ashx?w=200&h=126&as=1 http://res.cloudinary.com/hrscywv4p/image/upload/c_limit,h_1200,w_2000/ur5ywfcxts3enbp8knov.jpg http://www.bloomberg.com/image/iab3fbjaJ_n8.jpg http://news.xinhuanet.com/politics/2007-03/28/xinsrc_1820304280734812317435.jpg http://esa.un.org/unup/Maps/maps_urban_1960.htm http://esa.un.org/unup/Maps/maps_urban_1980.htm http://esa.un.org/unup/Maps/maps_urban_2011.htm http://esa.un.org/unup/Maps/maps_urban_2025.htm

14

Fig 1.2 Fig 1.3 Fig 1.4

15

Fig 1.5

16

Fig 1.6

20 21

Fig 2.1.1 Fig 2.1.3 Fig 2.1.4 Fig 2.1.5 Fig 2.2.1 Fig 2.2.2 Fig 2.2.3 Fig 2.2.4 Fig 2.2.5 Fig 2.2.6 Fig 2.2.7 Fig 2.2.8 Fig 2.2.9 Fig 2.2.10 Fig 2.2.11 Fig 2.3.1 Fig 2.3.2 Fig 2.3.3 Fig 2.3.4 Fig 2.3.5 Fig 2.3.6 Fig 2.4.1 Fig 2.4.2 Fig 2.4.3 Fig 2.4.4 Fig 2.4.5 Fig 2.5.1 Fig 2.5.2 Fig 2.5.3 Fig 2.5.4 Fig 2.5.5 Fig 2.5.6

http://2.bp.blogspot.com/_bxhgp6OZooM/TEqP7oa_V6I/AAAAAAAABWk/EVVnjbjLCGI/s1600/Screen+shot+2010-07-24+at+12.55.59+AM.png http://mytomnet.com/wp-content/uploads/2012/03/8710_24_y.jpg http://img5.pcpop.com/ArticleImages/picshow/900x675/20130721/2013072121010199462.jpg http://info.aia.org/aiarchitect/2010/multimedia/articles/1119_SiheyuanHutong/pic2.jpg http://images.asc.ohio-state.edu/is/image/ha/0061231_c.JPG?size=668,668&qlt=30&fmt=jpeg http://identityhousing.files.wordpress.com/2009/11/tiss2.png http://identityhousing.files.wordpress.com/2009/11/tiss1.png http://images.china.cn/site1006/20070806/00105cadc1c5082100c407.jpg http://www.cultural-china.com/chinaWH/images/exbig_images/705f3c42c64b1f505a15689c29b3f55c.jpg http://www.ashgatepublishing.com/isbn/9781409405030 http://arquiproyecto.com.ec/imagenes/big/su-12.jpg http://identityhousing.files.wordpress.com/2009/11/giu-6.png http://identityhousing.files.wordpress.com/2009/11/giulianelli2.png http://dome.mit.edu/bitstream/handle/1721.3/58436/150032_sv.jpg?sequence=2 http://identityhousing.files.wordpress.com/2009/11/giu-1.png http://picpost.postjung.com/data/173/173328-pic-5.jpg http://static.dezeen.com/uploads/2009/07/mahanakhon-by-oma-88.gif http://ad009cdnb.archdaily.net/wp-content/uploads/2011/12/1323468859-mvrdv-yongsan-birdeye.jpg http://architecture.org.nz/wp-content/uploads/2011/12/cloud9.jpg http://www10.aeccafe.com/blogs/arch-showcase/files/2011/12/Sky-Village-facade_copyright-ADEPT-MVRDV.jpg http://lh5.ggpht.com/_QzdUvyDN87g/SRD2Im8yn6I/AAAAAAAABiU/k790gBMNy4o/s800/SkyVillage.gif http://blogs.voanews.com/china-wangre/2011/12/06/falling-property-prices-create-net-buzz/ http://theperfectslum.blogspot.co.uk/ http://www.skyscrapercity.comshowthread.phpt=755418 http://www.freeimageslive.co.uk/free_stock_image/beijingconstructionjpg http://www.thenewmans.id.au/blog/wp-content/uploads/2007/10/beijing-019.jpg : http://cfs11.tistory.com/image/18/tistory/2009/01/23/15/23/4979626160358 http://pr2012.aaschool.ac.uk/students/Metabolism_and_Culture http://pr2013.aaschool.ac.uk/EMERGENT-TECHNOLOGIES/Autonomous-Infrastructures http://www.saf.co.il/noa/new_4806 http://pr2012.aaschool.ac.uk/students/Metabolism_and_Culture http://pr2013.aaschool.ac.uk/EMERGENT-TECHNOLOGIES/Autonomous-Infrastructures

47

Fig 3.2.5 Fig 3.2.6 Fig 3.3.2

48

Fig 3.3.3

http://nexus.umn.edu/Reviews/Marshall.pdf http://nexus.umn.edu/Reviews/Marshall.pdf http://www.db-rep.net/wp-content/uploads/2011/11/Ecotect-ShadowRanges-666x500.png http://www.grasshopper3d.com/forum/topics/geco-responsive-surface-openings http://bio.illinoisstate.edu/kaedwar/images/HawaiianDrosImages/HawaiiFig2.jpg https://maps.google.co.uk/maps?hl=en&tab=wl https://maps.google.co.uk/maps?hl=en&tab=wl http://2.bp.blogspot.com/_bxhgp6OZooM/TEqddUc42fI/AAAAAAAABYs/S8BV0id0lp4/s1600/agna+square-02.jpg http://www.flickr.com/photos/70876789@N00/170055978 http://4.bp.blogspot.com/_bxhgp6OZooM/TEqdQCjrwzI/AAAAAAAABX0/hMvBcdABCas/s1600/Ranwar+Square-01+copy.jpg

22 23 24 25 26

28 29

30 31

32

33

41

56 58

159 160

274

Fig 7.2.2 Fig 7.2.3 Fig 7.2.4

SynchroniCity

https://maps.google.co.uk/maps?hl=en&tab=wl http://farm4.staticflickr.com/3651/3375075152_39d43076e8_o.jpg http://pic4.nipic.com/20090720/895017_140154004_2.jpg


Image Links Page

Nnumber

Link

161

163 164

Fig 7.2.5 Fig 7.2.7 Fig 7.2.8 Fig 7.2.10 Fig 7.2.12

165

Fig 7.2.13

178 181 185 194

Fig 7.2.14 Fig 7.2.15 Fig 7.4.2 Fig 7.4.6 Fig 7.4.14 Fig 7.5.6

198

Fig 7.5.14

202

Fig 7.6.1 Fig 7.6.2 Fig 7.6.15 Fig 7.6.16

http://en.wikipedia.org/wiki/Beijing http://www.fotopedia.com/wiki/Maharashtra#!/items/flickr-2228329938 http://pmzp.findart.com.cn/1596057.html http://upload.wikimedia.org/wikipedia/commons/c/c8/CIDCO_Aerial-_Canought_Place.jpg http://static.manoramaonline.com/ranked/online/MM/The_Week/CouragetoGoBeyond/24-Aerial.jpg http://realestateindia.anandmahal.com/wp-content/uploads/2012/08/affordable-housing.jpg http://www.census2011.co.in/census/district/355-thane.html http://www.census2011.co.in/census/metropolitan/305-mumbai.html http://thanecity.gov.in/news_detail.php?id=4 http://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/India_location_map_3.png/375px-India_location_map_3.png http://somemarkets.files.wordpress.com/2010/04/frei-otto-optimized-path-system.jpg http://bbs.godeyes.cn/upload/2007/04/24/191814.jpg http://24.media.tumblr.com/tumblr_m8cezjSS8P1qa46j9o1_500.jpg http://i1.ytimg.com/vi/EB9inujCuKc/hqdefault.jpg?feature=og http://postimg.org/image/ay2vd3r47/ http://postimg.org/image/db87skarr/ http://www.flickr.com/photos/70876789@N00/170055978 http://inapcache.boston.com/universal/site_graphics/blogs/bigpicture/hawkes_04_29/h08_00000015.jpg http://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/India_location_map_3.png/375px-India_location_map_3.png http://1.bp.blogspot.com/_bxhgp6OZooM/TEqdfc6PXvI/AAAAAAAABY0/2lHA416NL7A/s1600/Ranwar+aerial-04.jpg http://inapcache.boston.com/universal/site_graphics/blogs/bigpicture/hawkes_04_29/h08_00000015.jpg http://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/India_location_map_3.png/375px-India_location_map_3.png http://postimg.org/image/4vy1c871d/ http://2.bp.blogspot.com/_bxhgp6OZooM/TEqdMnQHICI/AAAAAAAABXk/GaOdZzOTZs0/s1600/Ranwar+Square-06.jpg

208

233

Fig 8.2.1 Fig 8.2.2

235

Fig 8.2.3 Fig 8.2.4

http://media-cache-ec0.pinimg.com/originals/da/c1/e9/dac1e99d7221a7c6253bbc029b3483b1.jpg http://www.globespots.com/pictures/middleeast/iran/593377033_d057899a62_b.jpg http://upload.wikimedia.org/wikipedia/commons/3/33/Hampi_aug09_243.jpg http://infranetlab.org/drupal7/sites/default/files/blog/wp-content/uploads/2009/07/09_07_18_stepwells03.jpg http://www.designboom.com/architecture/venice-architecture-biennale-08-rebirth-brick-in-the-chinese-pavilion/ http://www.gooood.hk/_d275610375.htm

Reading References Courtyard Housing and Cultural Sustainability, Donia Zhang, Oxford Brookes University, UK, May 2013. Life Between Buildings. Jan Gehl, Arkitektens Forlag, 1987. Street Patterns, Stephen Marshall, Spon Press, 2005 The Image of The City, Kevin Lynch, The MIT Press, Cambridge, 1960 Metabolism And Culture, Fatermeh Nasseri and Yasaman Mousavi, Architectural Association, 2012 Urban Change: Complexity and Emergence, Cities and Complexity, Michael Batty, The MIT Press, Cambridge, 2007 The Architecture of Emergence, Michael Weinstock, John Wiley and Sons Ltd, 2010 AD- System City, Michael Weinstock, John Wiley and Sons Ltd, 2013 The Death and Life of Great American Cities, Jane Jacobs, Vintage Books, 1993 The Architecture of Complexity, Herbert A. Simon, Carnegie Institute of Technology, 1962 Housing & urbanisation , Charles Correa , Thames & Hudson Ltd , 1999 Raj Rewal, Brian Brace Taylor , Mimar, 1991 Charles Correa , Charles Correa and Kenneth Frampton , Thames & Hudson Ltd, 1996 Machiya and Transition , John Linam , 1999 A Study on the Eastern Waterfront of Mumbai , Rahul Mehrotra, Pankaj Joshi, Anirudh Paul , Urban Design Research Institute , 2005

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2

Synchronicity  

Urban Adaptation of Indigenous Spatial Attributes . EmTech Thesis 2013 - 2014 . Abhinav Champaneri . Yunfu Yi .

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