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An Integrated Hydrological System for Arid Climates

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4!Collective Ecology


Architectural Association School of Architecture Graduate School Programmes Emergent Technologies and Design 2012-2013

Students:

Nicolas Cabargas Mori (M.Arch) Andrew Haas (M.Arch) Miguel Rus (M.Arch)

Contribution:

Mikaella Papadopoulou (M.Sc)

Title:

Collective Ecology, An Integrated Hydrological System for Arid Climates

Course:

Master of Architecture

Tutors: Advisors:

Michael Weinstock, George Jeronimidis Evan Greenberg, Mehran Gharleghi

Date:

14-02-2014

“We certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.�

Nicolas Cabargas

Andrew Haas

Miguel Rus !Preface!5


Acknowledgements We owe deep gratitude to Mike and George. Through their continual encouragement and guidance, the development of this book was made possible. In addition, we would like to thank Evan, Mehran, and Wolf, as well as the members of Architectural Association for providing continuous inspiration throughout our studies. Lastly, we are profoundly grateful for our friends and loved ones, whose constant encouragement and support inspires us to continually strive to achieve great things.


8!Collective Ecology


Table of Contents Acknowledgements Abstract

7 11

Introduction

13

Domain

27

Methods

45

Experiments

69

Design Development

127

Appendix

245

Introduction Overview Metabolism Water Stress Qatar Introduction References Domain Overview Scalability of the City Natural Water Treatment Systems Sociocultural and Climatic Aspects Research Proposal Domain References Methods Overview City Samples Case Studies Computational Techniques Methods References Experiments Overview Analysis of Streets and Public Spaces Subdivision and Integration Traditional Socio-Cultural Values Plot Distribution Strategies Network Strategies Building Morphologies Design Development Overview Site Cells Public Spaces Urban Wetland Integration Network Development Test Patch Building Morphologies Low Rise Low-Rise Sections High Rise High-Rise Sections Conclusions Algorithms Experiments

15 16 18 20 25

29 30 32 38 40 43 47 48 52 62 67

71 72 76 80 86 98 108

129 130 132 140 146 150 152 164 166 186 196 216 235

246 270

!Preface!9


Abstract COLLECTIVE ECOLOGY is an investigation focused on addressing the intensifying metabolic demands of growing urban populations by approaching the city as a dynamic complex system. It places emphasis on the feedbacks and critical thresholds of its ecological processes, climatic conditions and cultural modalities to drive the emergence of novel morphologies, social organizations and metabolic processes within a larger collective system. This systems-based model for urban growth explores the potential to minimise metabolic flow in and out of the system through integration of localised natural water

treatment processes. Acting as an agent for its own productivity, it symbiotically develops with the architectural and urban morphology to extend hydrological retention within the system through multiple cycles of use and treatment. The resulting heterogeneous landscape of emergent interactions presents a more homeostatic environment, in which the dynamic qualities of an urban system can better adapt to intensifying metabolic demands of a growing population.


Introduction Metabolism p.16 Water Stress p.18 Qatar p.20


Introduction Overview Exponential acceleration of urban growth around the world brings with it unsustainable levels of increased material processing and flow, with water being the largest material flux of any urban system. Current methods of urbanisation are often presumptuously confident that these resources will remain secure and abundant, even as population growth and metabolic demands continue to escalate. The epitome of this heedlessness is seen in the recent and dramatic urban growth of Doha, Qatar, the largest consumer of water per capita, yet also one of the most water stressed cities in the world.

Through an understanding of the metabolic processes and associative logics inherent to biological systems, a new conceptual and methodological framework can be established to address the complex interactions, organisations, and material exchanges within urban systems. This fundamental shift from the current paradigm of urban master planning allows for the development novel urban morphologies through emergent and dynamic systems, minimising the demands brought forth by relentless urban growth, not only within Doha, but in future emerging urban centres around the world.


Metabolism Processing and Flow of Energy & Material Photograph)1.1: Rain Forest Collective Metabolic System Source: <www.daz3d. com>

Source: Growth, innovation, scaling, and the pace of life in cities, Geoffrey West, et. al., 2007

16!Collective Ecology

Collective Metabolism Metabolism can also be seen in the allometric relationships of individuals within populations of a species and their surrounding environment.9 These higher levels of organisation begin to materialise through metabolic processes in the relationships between species, based on their density and distribution patterns, establishing a dynamic equilibrium within their ecosystem.10 The flow of energy and material is thus regulated by the collective metabolism of all the living forms within it, defining an ecosystem’s anatomical organisation,11 and over time modifying the fitness criteria for natural selection within it.12

Urban Metabolism Urban metabolism can be described as the flow and transformation of energy and material throughout an urban system into physical structures, biomass, and waste.13 It is the fundamental physical process that directs urban growth and organisation,14 in the forms of infrastructure, architectures and networks behaving in a similar branching fashion as biological metabolism.15 These network dynamics bring about economies of scale in cities, which require fewer infrastructures to operate as they increase in size, expanding only 85% as the population doubles, similar to the 75% scaling optimisation seen in biological systems.16

103 (kcal/h: log scale)

Figure)1.1: Graph, Scaling factors of organisms.

Biological Metabolism Biological metabolism can be described as the processing and flow of energy and material throughout an organism. It symbiotically develops with an organism’s morphology,1 emerging together through dynamic forces acting upon them2 in the conversion and movement of resources from the environment throughout the organism, and in the return of transformed materials back into the ecosystem.3 It functions through optimised, hierarchical branching networks4 that exhibit identical mathematical parameters in all species, at multiple scales,5 determining the rates at which energy is delivered, and setting the pace of physiological processes to regulate the size the organism.6 The rate of energy consumption per unit body mass declines at a scale of ¾ as the mass of an organism increases,7 the basis of Kleibers Power Law, which adduces that the larger the organism, the less amount of energy in relation to body mass is required to sustain it. (Fig. 1.1) This symbiotic relationship of an organism’s metabolism with its morphology is the critical factor in developing their sublinear metabolic rate.8

Metabolic rate

Emergence h and Form Emergence ol.104, No.17 Vol. 81, Iss. 4 ol.104, No.17 ure, Vol. 413 Emergence ure, Vol. 413 Vol. 81, Iss. 4 Emergence Vol. 80, Iss. 2 Ecosystem 14.  Ibid. Emergence ure, Vol. 467

100 10-3 10-6 10-9 10-12 10-12 10-9

10-6

10-3

100

103

106

109

Mass(g, log scale) Metabolic Scalability in Organisms Kleibers Power Law Equation = Y=Y0Mb Y= Observable magnitude, Y0= Constant, M= Mass b = ~ 3/4 = scaling exponent


[Cities] are dynamic, spatial and material arrays of buildings that are constructed, reworked and rebuilt over time, decaying, collapsing and expanding in irregular episodes of growth and incorporation. As they grow and develop, their systems for the movement of food, material, water, people and manufactured artefacts must grow and extend with them. From this perspective, cities are not static arrays of material structures, but are regarded as analogous to living beings, as they consume energy, food, water and other materials, excrete wastes and maintain themselves down through the generations.” 26 Michael Weinstock

Urban Metabolic Capacities Urban metabolism can be measured spatially through the amount of land an urban system requires to meet its metabolic needs.17 Throughout most of history, urban systems developed and expanded to limits based on their capacity to extract energy and materials from their environment, and their ability to manage and distribute flows throughout their system.18 Similar to an organism reaching a stable size at maturity,19 urban systems developed and matured until they were close to their critical threshold of stability, imposing a self-regulating limit to size and population capacity. 20 Globalisation, particularly in the last century, has enabled

17.  Decker 18.  Weinst 19.  ‘Betten 20.  Weinst 21.  Decker 22.  Weinst 23.  Weinst 24.  Decker 25.  Weinst

urban systems to no longer be self-reliant on their immediate environment to provide resources and absorb waste. 21 The removal of these critical, self-regulating, capacity limits have allowed for an escalation of urban population at unprecedented scales. Urban metabolisms, as a result, have developed independently from their morphologies as cities continue to mature and grow.22 This has led to increased energy and material flows in relation to the spatial patterns and forms of the city,23 and established a reliance on resources from outside their boundaries24 in order to meet urban demands at a super-linear metabolic rate. 25 With demographic pressures in cities increasing, these discrepancies of morphology and metabolic flows threaten the future sustainability of urban systems.

26.  Weinst

2% Figure)1.2: Graph, Energy consumption versus body mass and lifespan: elephant.

50%

Energy Consumption / Body Mass Lifespan

60 years

Figure)1.3: Graph, Energy consumption versus body mass and lifespan :mouse.

Energy Consumption / Body Mass 2-3 years !Introduction!17


Water Stress Largest Component Flux of Metabolism Photograph)1.2: Aral Sea, present state. Source: Documentary “Aral, el mar perdido” / Aral, the lost sea. < http://www. palmyrasculpturecentre.com/?attachment_id=3053>

NW-DPAC) vision, 2013 port 3, 2009 NW-DPAC)

Excessive Demand In 2013, the world population reached 7.2 billion, with one fifth of people living in areas of water scarcity.27 The UN estimates growth of an additional 3 billion people by 2050, with a majority living in developing countries that already suffer water stress.28 Demographic, economic and social activities and process can all exert excessive pressures on already limited water resources directly and indirectly. The ever demanding requirements for water to meet these increasing needs worldwide threaten the continued sustainability and growth of fragile ecosystems, both natural and urban. Demographic Drivers Pressures on freshwater resources brought on by shifting population dynamics (growth, gender and age distribution, migration) inevitably alter water demands and pollution levels. Transformations of the natural landscape associated with population dynamics can create additional pressures on local water networks and resources, and often lead to the necessity of more water-related services and infrastructures. Economic Drivers International economic growth and trade can both aggravate water stress in some areas and relieve it in others in the form of embedded water used in the production of consumer goods and agriculture.29 History has often demonstrated a link between how water has contributed to economic development and how, in turn, development has demanded an increased use of water resources. This urban growth often generates additional pressure on the local environment and hydrological networks, leading to financial competitiveness among consumers to be able to obtain it.

18!Collective Ecology

Social Drivers With increased affluence in Asia, Latin America and the Middle East, accelerating rates of water consumption will inevitably occur as new urban centres continue to develop and expand, influencing changes in lifestyles and consumption patterns. With 95% of urban population growth taking place in the developing world30, this rapid global rise in living standards threatens the sustainability of local water resources and environments that may be incapable handling dramatic influxes of demand.

Our requirements for water to meet our fundamental needs and our collective pursuit of higher living standards, coupled with the need for water to sustain our planet’s fragile ecosystems, make water unique among our planet’s natural resources.”31 UN World Water


Figure)1.4: Water Stress World map for 2025. Source: The WBCSD Water and Sustainable Development Program, Facts and Trends, 2006,

2025 less than 10%

20% to 10%

40% to 20%

more than 40%

32. 

In terms of sheer mass, water is by far the largest component of urban metabolism.”

32

Chris Kennedy

2,5% 0,5%

20%

Figure)1.5: Graph, Water flux percentage in urban metabolism Figure)1.6: Graph,Available fresh water in the world Source: The WBCSD Water and Sustainable Development Program, Facts and Trends, 2006,

97%

80%

Fresh Water Available

Water in Urban Metabolism Water

Fresh

Frozen

Seawater

!Introduction!19


Qatar The most water stressed nation and the highest water consumption in the world Photograph)1.3: Aerial view of Qatar’s most dense area. Source: Alexander Cheek <https:// www.flickr.com/ groups/1599245@ N24/>

cators p.571 34.  Ibid. 1–2016 p.214 36.  Ibid. file of Qatar 12 Revision

Figure)1.7: Qatar’s regional location.

20!Collective Ecology

Economic Growth and Population Increases The State of Qatar is a small, desert peninsula located halfway along the south coast of the Persian Gulf, bordered to the south by Saudi Arabia. This once quiet State has been transformed by the discovery of an estimated 900 trillion m3 of natural gas reserves beneath its landscape.33 It is currently the world’s third largest known reserve, projected to last well into the 22nd century.34 Despite this highly secure economic driver, Qatar is one of the few energy rich nations in the Middle East pushing for high levels of economic diversification; investing heavily in infrastructural expansion and other industries for continued population growth and urban development. This has spurred annual economic growth to reach 12.5% in 2012, making it the world’s fastest growing economy,35 and providing Qataris with the highest average per capita incomes in the world.36 These investments have stimulated development in both the urban and once rural sectors of Qatar, increasing its population tenfold in the last 30 years. With a population of 2.1 million people,37 it has maintained its position as the fastest growing country in the world since 2004, peaking at an increase of 17.5% in 2007.38

Mediterranean sea

QATAR

Indian Ocean


Average Monthly Rainfall 1901 to 2009

35

Rainfall (mm)

Temperature (oC)

Average Monthly Temperature 1901 to 2009

30 25 20 15

12 9

Figure)1.9: Graph, Average monthly rainfall in Qatar from 1901 to 2009

6 3 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure)1.8: Graph, Average monthly temperature in Qatar from 1901 to 2009

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Source: World Data Bank <http://data. worldbank.org/ country/qatar>, March 2013

Water Use Per Household 43

150 125

Source: M.A. Darwish & Rabi Mohtar, Qatar water challenges, Desalination and Water Treatment, Qatar Environment and Energy Research Institute, 2012

Floor Cleaning

Clothes Washing

5

Bathing

3%

Personal Washing

3%

11%

Toilet

2%

10% %

Garden Watering

2%

Car Washing

100 75

Dish Washing

21%

Cooking & Drinking

Water (L)

200 175

50 25

Figure)1.10: Graph, Water breakdown per household in Qatar.

%

Use

Water Use Qatar’s population growth and increased standards of living have led to a hyper-dramatic acceleration of water consumption in Qatar within the last few decades. Qatari nationals, in spite of being only 10% of the population,39 consume 40% of total daily fresh water used within Qatar, nearly 1,200 litres per capita, per day.40 Expatriate residents, however, consume on average only 150 litres per capita, per day,41 on par with most westernised nations. Taking these numbers into consideration for the total population, water use averages to 430 litres per capita, per day, the highest rate in the world.42 The leading factor for this high consumption rate is the Qatari government’s subsidisation of water costs for Qatari citizens and residents, leading to high levels of waste and network inefficiency.

Figure)1.11: 39.  Al-Moh Regional subdivision 40.  GSDP of Qatar. 41.  Ibid. 42.  KAHR 43.  Global 44.  Kardou

Doha

Acquisition Stress Excessive fresh water use has led to the depletion of once sufficient underground water reserves, creating a supply deficit and requiring substantial investment in water desalination programmes. With an average annual rainfall of only 70-90mm a year,43 and no natural surface bodies of fresh water,44 desalination is currently the only viable option for fresh water procurement. However, this current approach is far from a long term solution, given its high reliance on uninterrupted service from only two

!Introduction!21


Photograph)1.4: Aerial view of Doha and the main desalination plant: Ras Abu Fontas Kahrama. Source: Google Earth Photograph)1.5: Qatar â&#x20AC;&#x2DC;s main desalination plant and storage containers. Source: <http://www. utilities-me.com/ article-745-24bn-rasal-zour-contract-forsaudi-chinese-jv/#. UgkjNRY0pLw>

Doha

Plant

cators p.577 1â&#x20AC;&#x201C;2016 p.218 cators p.577 pen in 2015 nual Report Vision 2030 work Affairs work Affairs

large desalination plants, which require long lead times for production expansions. Currently, 99.9% of Qatarâ&#x20AC;&#x2122;s fresh water comes from desalination processes, with only 0.1% derived from ground water.45 Existing desalination throughout Qatar can offer 1,600,000m3 of water a day,46 with facilities running at 71% capacity.47 Construction of an additional plant is underway, but will only produce an additional 320,000m3 of water a day.48 Contributing these issues even further, the required decommission of an aging desalination plant is expected in 2020, at which time the population is expected to have reached over 2.6 million.49 These figures suggest that continued population growth and resultant water demand will outpace desalination plant capacities. Figure)1.12: Qatar solar map, showing demonstrating sun irradiance. Source: Rabi H. Mohtar, Qatar Foundation Vision in Energy Efficiency and Renewable Energy, Qatar Environment and Energy Research Institute 2012

Storage Stress Qatar uses various means of water storage including reservoirs, ground tanks, elevated tanks and water towers. Capacities for water storage facilities have increased alongside population growth patterns over the last decade, but even with these expansions, water storage capacity is estimated at less than 2.0 million m3, providing an emergency supply of only 2 days.50 As the Qatari population continues to escalate, preparations are underway for expanding the emergency storage capacity to 7.0 million m3 within the next decade.51 This will provide up to 7 days of storage, alleviating some risk of supply interruption, but is still a short term solution to a larger, long term water emergency storage issue. Water Reintegration Qatar currently reintegrates about 24% of its total grey water produced, a leader among other Gulf States which average only 16%. However, this reintegrated water is obtained

22!Collective Ecology

through large scale mechanised filtration plants, and primarily utilised for agricultural irrigation located often long distances from the source, leading to substantial issues of water loss within the network.52 Treated water is also used for landscape irrigation within Doha, and for artificial wetland reservoir top-off within the Abu Nakhla Reserve, located 20km away from central Doha.

Direct normal irradiance (kWh/m2 p.a.) >2,225 2,201 - 2,225 2,176 - 2,220 2,151 - 2,175 2,126 - 2,150 2,101 - 2,125 2,076 - 2,100 <= 2,075 Doha


250 200

300 250

150

200 150

100 50

Year

Desalination Production

Figure)1.13: Graph, Increasing desalinated water production versus decreasing water availability per person. Population is growing faster than additional plants can be built: Decreasing total amount of water available per capita.

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1991

1992

100 50

Availability Per Capita (m3)

400 350

1990

Production (Million m3)

Desalination Production / Availablity Per Capita

Source: KAHRAMAA Statistics Report 2008, 2007/ Qatar National Vision 2030, 2009

Water Availability Per Capita

Year

Fresh Water

Figure)1.14: Graph, Population growth versus fresh water availability in Qatar. Source: World Data Bank <http://data. worldbank.org/ country/qatar>, March 2013

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

0.5 0.25 1972

400 200 1970

1 0.75

1968

800 600

1966

1.5 1.25

1964

1200 1000

Population (Millions)

2 1.75

1962

Freshwater Resources Per Capita (m3)

Population of Qatar / Renewable Freshwater Resources

Population

17%

Figure)1.15: Graphs, Wastewater use in Qatar.

17%

9.8 million m3 / yr

Source: KAHRAMAA Statistics Report 2008, 2007/ Qatar National Vision 2030, 2009

20% 57.7 million m3 / yr 2005

53%

20% 11.6 million m3 / yr

57.7 million m3 / yr 2005

63% 36.4 million m3 / yr

10% Use of Treated Wastewater

Location of Used Treated Wastewater El-Rakeya Farms

Doha Landscape

Abu Nakhla Lake

El-Refaa Farms

Agriculture Irrigation

Landscape Irrigation

Lake Top-Up

!Introduction!23


1.  Weinstock, M. (2010) The Architecture of Emergence 2.  Thompson, D. (1917, 1961) On Growth and Form

14.  Ibid. 15.  Weinstock, M. (2010) The Architecture of Emergence

3.  Weinstock, M. (2010) The Architecture of Emergence 4.  Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17 5.  Weinstock, M. (2011) ‘The Metabolism of the City’ AD Vol. 81, Iss. 4 6.  Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17 7.  West, G., et. al. (1997) ‘A General Model for the Origin of Allometric Scaling Laws in Biology’ Nature, Vol. 413 8.  Weinstock, M. (2010) The Architecture of Emergence 9.  West, G. (1997) ‘A General Model for the Origin of Allometric Scaling Laws in Biology’ Nature, Vol. 413 10.  Weinstock, M (2011) ‘The Metabolism of the City’ AD, Vol. 81, Iss. 4 11.  Weinstock, M. (2010) The Architecture of Emergence 12.  Weinstock, M. (2010) ‘Emergence and the Forms of Metabolism’ AD, Vol. 80, Iss. 2 13.  Decker, E., et. al. (2000) Energy and Material Flow through the Urban Ecosystem

16.  Bettencourt L., et. al. (2010) ‘A Unified Theory of Urban Living’ Nature, Vol. 467 17.  Decker, E., et. al., (2000) Energy and Material Flow through the Urban Ecosystem 18.  Weinstock, M. (2010) ‘Emergence and the Forms of Cities’ AD, Vol. 80, Iss. 3 19.  ‘Bettencourt L., et. al. (2010) A Unified Theory of Urban Living’ Nature, Vol. 467 20.  Weinstock, M. (2010) ‘Emergence and the Forms of Cities’ AD, Vol. 80 21.  Decker, E., et. al. (2000) Energy and Material Flow through the Urban Ecosystem 22.  Weinstock, M. (2008) ‘Metabolism and Morphology’ AD, Vol. 78, Iss. 2 23.  Weinstock, M. (2011) ‘The Metabolism of the City’ AD, Vol. 81, Iss. 4 24.  Decker, E., et. al. (2000) Energy and Material Flow through the Urban Ecosystem 25.  Weinstock, M. (2008) ‘Metabolism and Morphology’ AD Vol. 78, Iss. 2 26.  Weinstock, M. 2011, The Metabolism of the City


Introduction References 27.  UN-Water Decade Programme on Advocacy and Communication 2 (UNW-DPAC)

42.  KAHRAMAA Statistics Report 2008 (2009) 43.  Global Water Market (2011) Qatar - General Indicators p.571

28.  World Population Prospects - The 2012 Revision, 2013 29.  The United Nations World Water Development Report 3, 2009 30.  UN-Water Decade Programme on Advocacy and Communication 4 (UNW-DPAC) 31.  The United Nations World Water Development Report 3, 2009 32.  Kennedy, C. et.el (2007) ‘The changing metabolism of cities’ Journal of Industrial Ecology 33.  Global Water Market (2011) Qatar - General Indicators p.571 34.  Ibid. 35.  GSDP (2011) Qatar National Development Strategy 2011–2016 p.214 36.  Ibid. 37.  ESCWA (2012) The Demographic Profile of Qatar 38.  United Nations (2013) World Population Prospects - The 2012 Revision 39.  Al-Mohannadi, et. al. (2003) Controlling Residential Water Demand in Qatar 40.  GSDP (2011) Qatar National Development Strategy 2011–2016 p.218 41.  Ibid.

44.  Kardousha, M. Dr. (2009) Qatar Biodiversity 45.  Global Water Market (2011) Qatar - General Indicators p.577 46.  GSDP (2011) Qatar National Development Strategy 2011–2016 p.218 47.  Global Water Market (2011) Qatar - General Indicators p.577 48.  Qatar Electricty & Water (2013) Ras Abu Fontas Plant to Open in 2015 49.  The Demographic Profile of Qatar (2012) ESCWA Annual Report 50.  GSDP (2009) Qatar National Vision 2030 51.  Al Malki, A.S. (2009) Water Network Affairs 52.  Ibid.


Domain Scalability of the City Natural Water Treatment Systems Sociocultural and Climatic Aspects Research Proposal Statement

p.30 p.32 p.38 p.40


Domain Overview Concentrating on urban growth and metabolism, initial research investigates the scalability of cities, for a mathematical understanding of the dynamics, development, and organisation of urban systems. In order to address these issues, subsequent research focuses on passive infrastructural systems for cleansing and reintegrating water. Investigating constructed natural water treatment systems provides guidelines and establishes limits for their viability throughout an urban framework. The ability to appropriately integrate these systems within a regionally specific context is explored to ensure a more applicable approach. Processes of quantifying and analysing the sociocultural modalities expressed in the morphologies of cities are explored to drive the development of culturally relevant architectural and urban morphologies. These precedents are crucial for determining various parameters in the research and how urban metabolic rates are linked to urban growth.


Scalability of the City Quantifying urban systems Photograph*2.6: Aerial view of New York, New York, USA Source:Tim Sklyarov < http://timsklyarov. com/new-york-cityaerial/#more-1351>

Cities are the crucible of human civilization, the drivers towards potential disaster, and the source of the solution to humanity’s problems. It is therefore crucial that we understand their dynamics, growth and evolution in a scientifically predictable, quantitative way.” Geoffrey West66

Figure*2.16: Graph, Scale factors of organism demonstrating sub-linear behaviour. Source: Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17

30!Collective Ecology

Unified Theory of Scaling West and his transdisciplinary team of physicists and biologists are in the process of developing a unified theory and predictive framework of scaling in urban systems applicable to cities around the world. Their work hypothesises in part that the solutions to problems associated with urban scaling phenomena can be found in the structural and organisational principles among organisms.54 Through natural selection, the expectation for any degree of correlation between organisms of differing scales seems improbable, since each organism and it’s subsystem evolved in its own unique environmental niche. However, looking at all the elements comprising a mouse and all the elements of an elephant, they have remarkably similar power laws in relation to one another in terms of scale and the processing

of flows within their systems.55 This consistency is sustained through hierarchical, branching network structures, and can be measured in any physiological variable, such as energy consumption, heart rates, life span, and diffusion rates across surfaces. The variables follow a ¼ scaling law, either increasing by ¼ or decreasing by ¼,56 and reflect the general mathematical, physical, and topological properties of all organisms, regardless of each individual’s morphology and size.57 (Fig. 2.16) Urban Scaling West and his colleagues further investigated whether this scaling phenomenon was also found in urban systems. Their analysis is based on large urban data sets, from 103 (kcal/h: log scale)

Urban Transitions As population and urbanisation continue to increase throughout the world, cities are facing several large challenges as they develop and grow. Health concerns, habitat destruction, and environmental impacts imposed through increasing densification presents what Geoffrey West and his colleagues describe as ‘…an urgent requirement for establishing a science-based understanding of the dynamics, growth and organisation of cities for future sustainable development.’53

Metabolic rate

ol.104, No.17 54.  Ibid. 55.  Ibid. 56.  Ibid. ure, Vol. 413

100 10-3 10-6 10-9 10-12 10-12 10-9

10-6

10-3

100

Mass(g, log scale)

103

106

109


Y

ß

95% CI

Adj-R2

Observations

Country–year

New patents

1.27

[1.25,1.29]

0.72

331

U.S. 2001

Inventors

1.25

[1.22,1.27]

0.76

331

U.S. 2001

Private R&D employment

1.34

[1.29,1.39]

0.92

266

U.S. 2002

“Supercreative” employment

1.15

[1.11,1.18]

0.89

287

U.S. 2003

R&D employment

1.26

[1.18,1.43]

0.93

295

China 2002

Total wages

1.12

[1.09,1.13]

0.96

361

U.S. 2002

Total bank deposits

1.08

[1.03,1.11]

0.91

267

U.S. 1996

GDP

1.13

[1.03,1.23]

0.94

37

Germany 2003

Total electrical consumption

1.07

[1.03,1.11]

0.88

392

Germany 2002

New AIDS cases

1.23

[1.18,1.29]

0.76

93

U.S. 2002–2003

Serious crimes

1.16

[1.11,1.18]

0.89

287

U.S. 2003

Total housing

1

[0.99,1.01]

0.99

316

U.S. 1990

Total employment

1.01

[0.99,1.02]

0.98

331

U.S. 2001

Household electrical consumption

1

[0.94,1.06]

0.88

377

Germany 2002

Household water consumption

1.01

[0.89,1.11]

0.96

295

China 2002

Gasoline stations

0.77

[0.74,0.81]

0.93

318

U.S. 2001

Gasoline sales

0.79

[0.73,0.80]

0.94

318

U.S. 2001

Length of electrical cables

0.87

[0.82,0.92]

0.75

380

Germany 2002

Road surface

0.83

[0.74,0.92]

0.87

29

Germany 2002

Scaling exponent

Driving force

Organization

Growth

ß < 1 (Sub Linear)

Optimization, efficiency

Biological

Sigmoidal: long-term population limit

ß > 1 (Super Linear)

Creation of information, wealth and resources

Sociological

Boom/collapse: finite-time singularity/unbounded growth; accelerating growth rates/discontinuities

ß = 1 (linear)

Individual maintenance

Individual

Exponential

Table*2.2: Scaling exponents for urban indicators vs city size Source: Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17

58.  Betten 59.  Ibid. 60.  Betten 61.  Bettenc 62.  Betten 63.  Betten 64.  Ibid. 65.  Betten

innovation, wealth creation and education all reflecting the same 115% scaling ratio.64 (Fig. 2.17) These researchers have made great discoveries in the development of this theoretical framework on urban scaling, demonstrating two main systematic characteristics in relation to population growth. The first is that the space required per capita is sub-linear, minimised through denser development and utilisation of branching network infrastructures. Second, they demonstrate that the pace of socio-economic activity accelerates, leading to superlinear productivity and cultural growth. This combination of densification and economies of scale with and increased productivity, diversification, interdependence and cultural expression, clearly outline the benefits to continued global urbanisation. 65 Figure*2.17: Graph, Demonstrating super-linear socio-economic behaviour.

27 β=1.12

26 (log scale)

Total Wages

hundreds of cities around the world, spanning several decades, and has revealed remarkably universal, quantifiable scaling features of urban systems.58 Their findings indicate systematic behaviour in urban growth patterns and processes, shared among all cities throughout differing nations and points in time, following specific power law functions.59 (Table 2.2) Looking across infrastructural aspects of cities, they find a clear similarity to biology. Road networks and utilities such as electrical, water and sewer networks, among other aspects, all require fewer infrastructures as cities get larger, signifying efficiency found in economies of scale per capita.60 This demonstrated growth occurs in a sublinear fashion, as a population doubles the infrastructure only increases at a scale of 85%, similar to systems found within biological organisms.61 However, in socio-economic aspects of cities, they find the opposite is true. Quantities and pace grow at an increased rate in relation to population growth.62 This proves both problematic and beneficial for urban systems. Aspects such as energy consumption, disease, crime, waste production and pollution all increase in a super-linear fashion, demonstrating that as a population doubles these elements increase at a scale of 115%.63 (Table 2.1) West explains that cities may hold these negative features, but the answer to these problems can also be found in cites themselves, through their increased productivity,

Table*2.1: Classification of scaling exponents for urban properties and their implications. CI: Confidence Interval; Adj-R2: Adjusted R2; GDP: Gross Domestic Product; RD: Research Development

R2=0.97

25 24 23 22 21 20

10

11

12

13

14

15

16

17

Source: Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17

Population(millions, log scale) !Domain!31


Natural Water Treatment Systems Ecosystem use for purifying water. Photograph*2.7: Aerial view of Jebel Ali, Power and Desalination Plant, UAE Source:< http://images. cfa-uat.com/gallery/ index.php/Grace/ Energy-and-Industrial/Jebel-Ali-Power-and-Desalination-Plant_UAE

Photograph*2.8: Constructed wetlands in Houtan Park, Shangai, China Source:< http://www. phaidon.com>

Impact at City Scale As Qatarâ&#x20AC;&#x2122;s population growth and socio-economic activity continue to accelerate, super-linear rates of consumption will soon meet their capacity limits for water production. Exploring the details of their consumption modalities will offer insight into potential solutions for addressing this water stress. Qatari daily water consumption averages 430L per capita, per day, and can be separated into several categories. (Fig. 2.18) The first distinction made from this breakdown demonstrates that only a mere 2% (8.6L) of the water input to the system necessitates potable quality water, with the remaining 98% (421.4L) capable of utilising non-potable quality water. The second large distinction is that of the average waste output, per capita, per day, 77% (331L) is grey water, with only 11% necessitating extensive treatment as black water. This percentage is extremely high, in comparison with of other city samples taken from the United Kingdom and Australia, where the majority of the waste output is black water, with only 42% as grey water. (Fig. 2.19) Qatarâ&#x20AC;&#x2122;s high percentage of grey water offers a unique opportunity for treatment and reintegration within its hydrological system, rather than being lost to conventional offsite black water treatment plants. The ability to reclaim grey water to supply Qatar's non-potable water needs would 32!Collective Ecology

dramatically reduce the necessary water input to the system, addressing its growing metabolic hydrological needs through multiple cycles of use and treatment.


Necessitates Potable Water

2% - 8.6L

12.9L - 3%

Floor Cleaning

21.5L - 5%

Garden Watering

98% - 421.4L

Clothes Washing

12.9L - 3%

Dish Washing

43L - 10%

Personal Washing Bathing Toilet

Lost

12%

51.6L

8.6L - 2%

Car Washing Accepts Non-Potable Water

Figure*2.18: Analysis of the water breakdown per capita, per day in Qatar.

8.6L - 2%

Cooking & Drinking

90.3L - 21%

Grey Water (Capable of Reintegration)

331.1L

184.9L - 43% 47.3L - 11%

Black Water (Requiring Treatment)

47.3L

430L 161L

77%

101L

Grey Water

42% Qatar

United Kingdom

Wetlands Impact

18%

Islington Density(p/ha) 143

Amsterdam Density(p/ha) 55

33%

36% Doha Density(p/ha) 109

11%

Figure*2.19: Comparison of grey water produced as percentage of tolal water consumed per capita per day.

42% Australia

53%

47%

Frankfurt Density(p/ha) 100

77%

Figure*2.20: Impact of wetlands considering the Qatar water consumption (430L per person/ per day) in cities with different densities.

Manhattan Density(p/ha) 162

28% Masdar Density(p/ha) 83

!Domain!33


Photograph*2.9: Natural wetland Source: < http://red6747. pbworks.com/w/ page/26462365/ Wetlands%204>

Constructed wetlands are a natural, low-cost, eco-technological biological wastewater treatment technology designed to mimic processes found in natural wetland ecosystems”75 UN-HABITAT, 2008

ion Service 68.  Ibid. ure Vol. 387 Storm water ion Service

Photograph*2.10: Surface Flow constructed natural water treatment system. Source: < http://red6747. pbworks.com/w/ page/26462365/ Wetlands%204>

34!Collective Ecology

Natural processes passively clean water in a multitude of efficient ways. Rivers, lakes, and streams are absorbed through wetlands and sedimentary rock aquifers, acting as natural filters that trap or utilise sediment and microorganisms to provide natural water purification. Utilising these strategies, constructed natural water treatment systems allow for the break down and transformation of pollutants and bacteria into nutrients and cleansed water through a combination of vascular plants and communities of microbes and invertebrates.67 Acting as a self-adjusting system, constructed natural water treatment systems are tolerant of fluctuations in flow cycles prevalent in most urban hydrological systems.68 This ensures low operation and maintenance expenses, which are periotic rather than continuous as in conventional treatment systems, offering long term natural capital in the future.69 While constructed natural water treatment systems are utilised primarily for water re-integration, they also provide other benefits such as enhancing the landscape for recreation, remediating progressive effects of desertification and establishing local wildlife habitats.70 These additional factors allow for easier integration into urban landscapes, incorporating the land required to operate within urban areas as dispersed localised treatment systems. There are conventionally three main constructed natural water treatment systems in use around the world: Surface Flow, Sub-surface Flow, and Vertical Flow,71 each bringing

with them advantages and disadvantages for re-integrating water. (Fig. 2.21) Through evaluation of each system, it is clear that Sub-surface Flow Water Treatment Systems are the most appropriate for a location such as Qatar. They provide quick and efficient grey water treatment, through a closed system, without exposing surface water to reduce evaporation and easily integrate into urban contexts. (Fig. 2.22)


Figure*2.21: Systems of constructed natural water treatment systems in relation to the space require to meet Qatari water consumption needs per capita, per day. (430 litres)

Surface Flow

Sub-surface Flow

Vertical Flow

14m2 40m

2

90m2

Surface Flow Water Treatment72 - Land requirement to purify water per person – 90 sqm - Water flow occurs on the surface, increasing odours and evaporation - Quickly treats incoming water, but less efficient - More difficult integration into urban systems, larger architectural impact - Open water beds increase wildlife diversity - Initial capital and operation costs are relatively low Sub-surface Flow Water Treatment73 - Land requirement to purify water per person – 40 sqm - Water flow occurs below the surface, resulting in less odours and reduced evaporation - Quickly and efficiently treats incoming water - Easy integration into urban systems, least amount of architectural impact - Covered water beds allow for less wildlife diversity - Initial capital and operation costs are relatively high Vertical Flow Water Treatment74 - Land requirement to purify water per person – 14 sqm - Water flow is percolated from above the surface, increasing odours and evaporation - Slowly but efficiently treats incoming water

72.  Ibid. 73.  Ibid. 74.  Ibid.

- Most difficult to integrate into urban systems, largest architectural impact - Open water beds increase wildlife diversity - Initial capital and operation costs are relatively high

soil or gravel

Figure*2.22: Cross section of subsurface flow constructed natural water treatment systems.

slotted pipe water inlet Wetland Plants

0.6m inlet stone distributor

slope of 0.5%

rhizome network

effluent outlet

watertight membrane

Cross-Section of Sub-surface Flow Water Treatment System

!Domain!35


Photograph*2.11: Typha Domingensis selected plant specie for the project. Source: <www. ecohusky.uconn.edu> Photograph*2.12: Phragmites Australis, selected plant specie for the project. Source: Alan Cressler http://www.flickr. com/photos/alan_ cressler/479137579/ lightbox/ Photograph*2.13: Juncus Rigidus selected plant specie for the project. Source: <www. ecohusky.uconn.edu>

Ecology

d Wetlands age Systems

Figure*2.23: Simplified Pollutant Removal Mechanism in wetlands. Source: UN-HABITAT, 2008. Constructed Wetlands Manual. UN-HABITAT Water for Asian Cities Programme Nepal, Kathmandu.

36!Collective Ecology

Strategies for developing constructed natural water treatment systems within Qatar must address several issues to ensure viability within such a harsh climate. Extreme sun exposure, dryness, and heat can limit options for plant selection and types of water treatment systems. Focus is placed on integrating native and regional vegetation, to ensure robustness within the severe arid climate. Constructed natural water treatment systems consist of a multitude of plant species with capabilities of breaking down pollutants through biological, chemical and physical processes which interact in a complex fashion to filter grey water. Vegetation absorbs dissolved inorganic nutrients such as ammonia, nitrates, and phosphates, incorporating them into their tissue and digested further by bacteria and fungi which utilise their carbon compounds and nutrients.76 These processes occur most rapidly in high temperatures, as it stimulates microbial activity,77 ideal for an area with continual elevated heat such at Qatar. Of these most abundant species found in the within the region, three are commonly used in constructed natural water treatment systems: Phragmites Australis, Typha Domingensis and Juncus Rigidus (Fig. 2.24). They range in characteristics from full sun exposure to full shade, enabling coverage within the heterogeneous typologies found in urban systems.

Volatisation Water Inlet

Plant Metabolism

Roots Filtration & Absortion Water Outlet

Sediment

P

Bacterial Degradation

Sedimentation, precipitation & absortion

Simplified Pollutant (P) Removal Mechanism


Wastewater Constituents Removal Mechanism Suspended Solids

Soluble organics

Phosphorous

Sedimentation

Adsorption and cation exchange

Filtration

Complexation Metals

Aerobic microbial degradation Anaerobic microbial degradation

Plant uptake

Matrix sorption

Microbial Oxidation /reduction

Plant uptake

Sedimentation

Ammonification followed by microbial nitrification

Filtration

Denitrification Nitrogen

Precipitation

Source: UN-HABITAT, 2008. Constructed Wetlands Manual. UN-HABITAT Water for Asian Cities Programme Nepal, Kathmandu.

Natural die – off Pathogens

Plant uptake

Table*2.3: Pollutant removal mechanisms in constructed wetlands.

Predation UV irradiation (SF system)

Matrix adsorption

Excretion of antibiotics from roots of macrophytes

Ammonia volatilization (mostly in SF system)

Scientific Name

Vernacular Name

Regional Viablity

Schoenoplectus spp.

Clumbrush species

No

Iris pseudacorus

Yellow iris

No

Typha latifolia

Common reedmace

Yes

Carex spp.

Sedge species

No

Sagittarius spp.

Arrowhead species

No

Juncus spp.

Rush species

Yes

Phragmites australis

Common reed

Yes

Butomus umbellatus

Flowering rush

No

Phalaris arundinacea

Reed canary grass

No

Acorus calamus

Sweet - flag

No

Table*2.4: Common species used in constructed natural water treatment systems, highlighting regionally appropriate species. Sources: J. B. Ellis, et. al., “Constructed Wetlands and Links with Sustainable Drainage Systems” / Kardousha, Mahmoud M. Dr., 2009. Qatar Biodiversity

80%

66%

100%

3-4m 2-3m

Figure*2.24: Spatial implications and characteristics of selected Sub-Surface constructed natural water treatment systems.

1m

Phragmites Australis Exposure Tolerance: Average Height: Flowers: Characteristics:

Nearly Full Sun 2m Yes Highly Invasive

Typha Domingensis Exposure Tolerance: Average Height: Flowers: Characteristics:

Full Sun 3m Yes Dense & Dominant

Juncus Rigidus Exposure Tolerance: Average Height: Flowers: Characteristics:

Full or Partial Sun 0.5 - 1 m No Aridity Tolerance

!Domain!37


Sociocultural and Climatic Aspects Drivers for a Site Specific Design. Photograph*2.14: Opem ublic space, Jamaa El Fna Square, Marrakesh, Morocco Source: Nicolas Cabargas Mori Photograph*2.15: Traditional Arab town in Algeria Source: <http:// dc370.4shared.com/ doc/Eja7JHlN/ preview004.png> Photograph*2.16: Traditional Arab town of Tripoli, Libya. Source: <http://www. jasonhawkes.com/ blog/2013/03/aerialviews-in-libya/>

During the second half of the 20th century, Qatar witnessed dramatic rates of modernised urbanisation, linked to its rapidly increased levels of wealth and population growth. Influenced and guided by teams of Western consultants, its development and planning has focused heavily on zoning plans and land policies of several uncoordinated parties over multiple decades. The resulting situation in its capital city of Doha presents a highly fragmented and sprawling urban landscape, comprised of suburban developments and areas of high-rise central business districts, unrepresentative of sociocultural modalities and unresponsive to climatic conditions of the region. The culture and climate that had previously shaped Dohaâ&#x20AC;&#x2122;s built environment reflected not only how its spaces were functionally used, but they also expressed the inner world of its society, highly responsive to its natural environment and religious or tribal traditions. The urbanisation model shaping Doha today has established sparsely dispersed buildings across the landscape, necessitating vehicular transportation for nearly all movement throughout the city, inhibiting previously prominent features for social interaction found throughout urban morphologies of the region for millennia. This dispersal has dissolved nearly all public traditional gathering spaces, and inhibited local community interaction and the establishment of neighbourhood affiliations. Climatically, Dohaâ&#x20AC;&#x2122;s dispersal has substantially encouraged urban heat islands throughout 38!Collective Ecology

its landscape, and promoted high levels of solar exposure on building facades. The dissolution of Qatar's sociocultural modalities and lack of consideration for climatic conditions presents a developmental process with multiple long term potential difficulties. Through incorporation of these aspects in the design process, a more culturally relevant and climatically appropriate developmental methodology can be established.


Photograph*2.17: Urban development of Doha, Qatar Source: Alexey Sergeev <http://www.asergeev. com/pictures/archives/ cmpress/2013/1160/06. htm>compress/2013/1160/06. htm>

!Domain!39


Research Proposal Collective Ecology: An Integrated Hydrological System for Arid Climates

The incessant financial prosperity and economic diversification seen in Qatar for the last two decades has driven unprecedented levels of population growth and large scale development within a dramatically short timeframe. To meet the needs of this growing economy and population, Qatar’s ambitious investment in economic and social infrastructures has laid the foundation for potential continued growth well into its future. However, conspicuous consumption and waste brought forth by increased affluence and substantially improved standards of living has led to the highest rates of water consumption in the world, rapidly accelerating and expanding the demands placed on Qatar’s water supply. In light of its strong financial position and lack of natural water resources, Qatar has recently relied solely upon large scale desalination processes to meet these immense water supply needs. However, with consideration of the sheer amount of hydrological flow required to meet their long term growth ambitions, it is clear that its economic ability to acquire boundless supplies of water should not be considered a permanent solution. The escalating difficulty to ensure a constant and reliable supply of water is becoming increasingly evident, with projected levels of demand outpacing proposed production capabilities and emergency water reserves limited to only 2 days’ supply. Re-examination of this current model paves the way for development of a new urban system, capable of addressing and sustaining the intensifying metabolic demands of a growing urban population within Qatar well into its future. Collective Ecology proposes a systems-based model for urban growth which will consider natural water treatment systems as an integral part of its architectural and urban morphologies. In contrast to the conventional practice of expelling grey water as waste for offsite treatment, the urban environment will be considered as an ecological system, minimising freshwater demands through multiple cycles of use and filtration. This offers the ability to treat and re-integrate nearly 77% of Qatar’s used water and will allow for the majority of fresh water currently supplied from 40!Collective Ecology

desalination plants to be significantly minimised. This dramatic reduction of the systems metabolic input will greatly extend the magnitude of achievable population growth, and significantly reduce the immense amount of energy that would otherwise be required with additional desalination expansions. This shift from the majority of water reliance placed on centralised desalination plants to dispersed natural water treatment processes and storage facilities will have many advantages. It will allow for minimised network spans and connections to prevent water loss, and provide increased emergency water reserves through a series of decentralised locations throughout the network. The fundamental properties of network organisation and metabolic flow will be symbiotically developed with the distribution and morphology of the urban environment. Driven by the requirements of the ecological processes, environmental analysis, and spatial patterns and social programmes abstracted from case studies, novel architectural and urban morphologies will be generated throughout the system. These arrangements will produce highly performative urban organisations capable of negotiating environmental conditions, managing hydrological flows, arranging infrastructural networks and creating complex spatial and microclimatic environments. This dynamic complex system places emphasis on the interactions and connectivity of the flows through its infrastructures, and on the feedbacks and critical thresholds that will drive the emergence of new morphologies social organisations and in response to the specific ecological, climatic and cultural modalities of the State of Qatar.


Ecological Processes

Sociocultural Modalities

Climatic Conditions

Morphological Development

Social Organisations

Metabolic Performance

Ecological Processes: Research into natural water treatment systems for use within the region will establish sets of data outlining the treated water production capabilities, and the spatial impacts of their morphological characteristics and coverage requirements. It will present the implications of dispersed natural water treatment systems and influence strategies for potential viability throughout urban the urban system.

Morphological Development: The allometric development of morphologies throughout the collective system is driven in response to the ecological processes, sociocultural modalities, and climate conditions of its environment. These parameters will simultaneously drive a system of growth which will be moulded in direct response to a multitude of regionally specific qualities and influences.

Sociocultural Modalities: Through examination of several regional case studies, a system of analysis will be developed to extract and quantify the cultural values and social parameters of several relevant urban tissues. It will establish a catalogue of descriptive metrics and mathematics expressed through the sociocultural modalities of the region, providing a culturally relevant and socially sensitive approach to the system.

Social Organisations: The thresholds of privacy hierarchies throughout the system will be informed by the sociocultural influences of the region, the morphological and spatial conditions of the ecological environments, and strategies for the development of comfortable microclimates. It will allow for a development of differentiated social spaces throughout the system in a methodological manner in accordance to multiple factors of influence.

Climatic Conditions: Analysis of the regionâ&#x20AC;&#x2122;s climatic conditions will outline the environmental qualities and sensory characteristics expressed through the mathematics of humidity, temperature and solar exposure levels. These parameters will drive aspects of solar accessibility and environmental comfort throughout the system based on the specific challenges presented by the climatic conditions of the region.

Figure*2.25: Develomental process of incorporating feedback parameters rather than linear flow.

Metabolic Performance: The potentials to reduce the metabolic input of the system will be explored through localised natural water treatment processes within the ecological environment. Their dispersal will be organised by the spatial impacts of their coverage requirements, the parameters of the climatic conditions and the consumption demands of the culture.

!Domain!41


53.  Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17 54.  Ibid. 55.  Ibid. 56.  Ibid. 57.  West G., et. al. (2001) ‘A General Model for Ontogenetic Growth’ Nature, Vol. 413 58.  Bettencourt L., et. al. (2010) ‘A Unified Theory of Urban Living’ Nature, Vol. 467 59.  Ibid. 60.  Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17 61.  Bettencourt L., et. al. (2010) ‘Urban Scaling and Its Deviations’ PLoS ONE, Vol. 5, Iss. 11 62.  Bettencourt L. et. al. (2006) ‘Growth, Innovation, Scaling, and the Pace of Life in Cities’ PNAS, Vol.104, No.17 63.  Bettencourt L., et. al. (2010) ‘Urban Scaling and Its Deviations’ PLoS ONE, Vol. 5, Iss. 11


Domain References 64.  Ibid. 65.  Bettencourt L., et. al. (2010) ‘A Unified Theory of Urban Living’ Nature, Vol. 467 66. Ibid. 67.  Luise, D. (1998) ‘A Handbook of Constructed Wetlands, Volume 1’ USDA-Natural Resources Conservation Service 68.  Ibid. 69.  Costanza, R. et. al. (1997) ‘The Value of the World’s Ecosystem Services and Natural Capital’ Nature Vol. 387 70.  Melbourne Water (2005) Water Sensitive Urban Design - Engineering Procedures: Storm water 71.  Luise, D. (1998) ‘A Handbook of Constructed Wetlands, Volume 1’ USDA-Natural Resources Conservation Service 72.  Ibid. 73.  Ibid. 74.  Ibid. 75.  UN-HABITAT, 2008. "Constructed Wetlands Manual". UN-HABITAT Water for Asian Cities Programme Nepal, Kathmandu. 76.  Ellis, J.B. et.el. (2003) Guidance Manual for Constructed Wetlands

77.  Ellis, J.B. et.el. (2003) Constructed Wetlands and Links with Sustainable Drainage Systems


Methods City Samples p.48 Case Studies p.52 Computational Techniques p.62


Methods Overview The current conditions within Doha, Qatar are analysed and evaluated with city samples from around the world to compare relationships between population density, public space and road networks, among many other factors. Their guidance, coupled with strategies garnered from several relevant regional case studies helps establish the principle driving factors for our urban system. Computational methods were then researched and evaluated to find the best strategies for informing decisions and analysing approaches within the design.


City Samples Extraction of Metrics and Relationships Photograph(3.18: Aerial view, sample Doha, Qatar Source: Google Earth

Figure(3.26: Built area, sample Doha, Qatar Figure(3.27: Roads area, sample Doha, Qatar

Doha, Qatar Sample Patch Analysis (500x500m) Number of Buildings

48!Collective Ecology

535

buildings

Streets Min. Width

3

m

2745

persons

Built Area

95353.43

m2

Population

Unbuild Area

154,646.57

m2

Density

109.8

people / hectare

Wetland needed

109800

m2

Percentage Build

38.14%

%

Minimum Blocksize

351.84

m2

Maximum Blocksize

10996.33

m2

Block Area

165823.19

m2

Percentage Block Are Build

0.58%

%

Average Blocksize

3616.78

m2

Minimum Building Footprint

3.376

m2

Maximum Building Footprint

1657.21

m2

Average Area

193.24

m2

Minimum Stories

1

Floors

Maximum Stories

5

Floors

Avearage Stories

3.60

Floors

Public Green Space

3122.78

m2

Courtyards

10,048.99

m2

Streets Area

84176.81

m2

Streets Intersections

39

nodes

Streets Max. Width

25

m

Though examination city samples from Doha, Frankfurt, New York City, and Amsterdam, several elements of data are extracted and analysed to reveal the metrics and mathematics of their network systems and geometries. These comparative data sets offer quantifiable insight into the characteristics and organisational principles of multiple urban contexts from which to set and gauge the desired properties of our urban system.


Photograph(3.19: Aerial view, sample Amsterdam, Netherlands Source: Google Earth

Figure(3.28: Built area, sample Amsterdam, Netherlands

Figure(3.29: Roads area, sample Amsterdam, Netherlands

Amsterdam

Amsterdam, Netherlands Sample Patch Analysis (500x500m) Number of Buildings

1125

buildings

Amenities

6501.47

m2

Built Area

76974.34

m2

Streets Area

11814.3

m2

Unbuild Area

173,025.66

m2

Streets Intersections

65

nodes

Percentage Build

30.79%

%

Streets Max. Width

71

m

Minimum Blocksize

252.35

m2

Streets Min. Width

3

m

Maximum Blocksize

8699.1

m2

Residential Building Area

18442

m2

Block Area

119506.48

m2

Residential Building

29.00%

%

Percentage Block Are Build

0.64%

%

Office Building Area

6903

m2

Average Blocksize

2987.662

m2

Office Building

11.00%

%

Minimum Building Footprint

5.56

m2

Mixed Use Office Area

18147

m2

Maximum Building Footprint

3499.59

m2

Mixed Use Office Building

28.00%

%

Average Area

68.42

m2

Retail Building Area

8484

m2

Minimum Stories

2

Floors

Retail Building Percentage

13.00%

%

Maximum Stories

7

Floors

Other Use Building Area

12206

m2

Avearage Stories

3.68

Floors

Other Use Building

19.00%

%

Floor Area

283557.26

m2

Population

1250

persons

Building Density

1.13

floor area/ patch size

Density

55

people / hectare

Public Water spaces

54758.42

m2

Wetland needed

50000

m2

Public Green Space

14489.61

m2

Courtyards

28,042.53

m2

!Methods!49


Photograph(3.20: Aerial view, sample Frankfurt, Germany Source: Google Earth

Figure(3.30: Built area, sample Frankfurt, Germany Figure(3.31: Roads area, sample Frankfurt, Germany

Frankfurt

Frankfurt, Germany Sample Patch Analysis (500x500m)

50!Collective Ecology

Number of Buildings

492

buildings

Amenities

6183.64

m2

Built Area

88306.4

m2

Streets Area

75107.35

m2

Unbuild Area

161,693.60

m2

Streets Intersections

41

nodes

Percentage Build

35.32%

%

Streets Max. Width

25

m

Minimum Blocksize

287.65

m2

Streets Min. Width

7.2

m

Maximum Blocksize

20132.88

m2

Residential Building Area

49488

m2

Block Area

174011.45

m2

Residential Building

56.00%

%

Percentage Block Are Build

0.51%

%

Office Building Area

3580

m2

Average Blocksize

5,800.38

m2

Office Building

4.00%

%

Minimum Building Footprint

3.12

m2

Mixed Use Office Area

5320

m2

Maximum Building Footprint

3715.66

m2

Mixed Use Office Building

6.00%

%

Average Area

179.48

m2

Retail Building Area

18373

m2

Minimum Stories

1

Floors

Retail Building Percentage

21.00%

%

Maximum Stories

9

Floors

Other Use Building Area

11308

m2

Avearage Stories

4.67

Floors

Other Use Building

13.00%

%

Floor Area

412201.17

m2

Population

2500

persons

Building Density

1.65

floor area/ patch size

Density

100

people / hectare

Public Water spaces

0

m2

Wetland needed

100000

m2

Public Green Space

881.2

m2

Courtyards

85705.05

m2


Photograph(3.21: Aerial view, sample Manhattan, New York, US Source: Google Earth

Figure(3.32: Built area, sample Manhattan, New York, US Figure(3.33: Roads area, sample Manhattan, New York, US

New york, Manhattan

Manhattan, New York, USA Sample Patch Analysis (500x500m) Number of Buildings

345

buildings

Amenities

0

m2

Built Area

137487.33

m2

Streets Area

46073.18

m2

Unbuild Area

112512.67

m2

Streets Intersections

12

nodes

Percentage Build

54.99%

%

Streets Max. Width

37

m

Minimum Blocksize

23472.5

m2

Streets Min. Width

20.4

m

Maximum Blocksize

26462.66

m2

Residential Building Area

40962

m2

Block Area

203926.82

m2

Residential Building

31.00%

%

Percentage Block Are Build

0.67%

%

Office Building Area

26317

m2

Average Blocksize

24462.8

m2

Office Building

20.00%

%

Minimum Building Footprint

27.89

m2

Mixed Use Office Area

29899

m2

Maximum Building Footprint

2492.09

m2

Mixed Use Office Building

23.00%

%

Average Area

398.51

m2

Retail Building Area

13737

m2

Minimum Stories

1

Floors

Retail Building Percentage

11.00%

%

Maximum Stories

20

Floors

Other Use Building Area

19733

m2

Avearage Stories

6.86

Floors

Other Use Building

15.00%

%

Floor Area

943,093.64

m2

Population

4050

persons

Building Density

3.77

floor area/ patch size

Density

162

people / hectare

Public Water spaces

0

m2

Wetland needed

162000

m2

Public Green Space

0

m2

Courtyards

30804.33

m2

!Methods!51


Case Studies Regionally Specific Research Photograph(3.22: Doha, Qatar, skyline 2012. Source: Shutterstock: 84373246

Doha, Qatar

METU.JFA file of Qatar METU.JFA Architecture n Doha City METU.JFA METU.JFA

52!Collective Ecology

Doha has a surprisingly short history. Established in the early 19th century as a small fishing village, it became a British protectorate in 1916, before achieving independence in 1971. Despite foreign involvement, Doha maintained its vernacular architectural methodologies and held consistent population levels up until the discovery of its oil and gas reserves in the middle of the 20th century.78 It has since emerged as a modern urban centre with more than 1 million inhabitants.79 Doha originally developed and dispersed in a bottom up manor through the settlement and clustering of tribes along the coast. Climate and culture shaped its urban morphology, reflecting not only how its spaces were used in functionality, but also in how these spaces expressed the societal norms and tribal affiliations.80 Designs followed Islamic tradition, with high degrees of privacy, and complex systems of winding streets and alleys throughout neighbourhoods. Buildings were constructed with local materials and varied from simple one room homes, to two story courtyard houses.81 These were clustered in close proximity with one another, in a highly dense fashion, often built wall to wall, encouraging and resembling the close social relationships within communities. These adjacent building schemes helped to develop shading strategies for walkways and exposed walls from the harsh desert conditions. Oil revenues brought investment and development of infrastructure to Doha, quickly changing the conditions of the city from a bottom up organisational approach to a series of fragmented master plans and architectures imported by foreign developers.82 Nearly all of Dohaâ&#x20AC;&#x2122;s original urban morphology and vernacular approaches have

since been replaced by fractured zone planning, orthogonal gird blocks, wide roads, conventional cement architecture, and centralised glass high rises.83 This approach during the oil boom led to vast urban sprawl comprised of low density urban typologies, dispersed throughout the landscape and connected by far spanning road networks, developing an extreme reliance on automobiles.84 Analysis of vernacular design principles and consideration of cultural traditions will define strategies to achieve conditions similar Dohaâ&#x20AC;&#x2122;s pre-oil urban morphology and social connectivity. The relationships between building adjacencies, shading strategies and street layouts will inform the development of dense, well connected architectures and networks within our urban system.


Figure(3.34: Pedestrian Road East-West orientation. Source: <http://www10. aeccafe.com>

Figure(3.35: Aerial Image showing the projected patch. Source: <http://www. adjaye.com/projects/ civic-buildings/ msheireb-downtown-doha/>

Figure(3.36: Nolli Plan showing the building density and proportions of roads. The East-West roads demonstrate a narrowed condition to reduce solar exposure. Source: <http://www10. aeccafe.com>

Msheireb Downtown Doha Project Many Middle Eastern cities have developed and grown at staggering speeds, leading to potential long-term difficulties brought on by short sighted modernisation, disregarding extreme climate, urban centralisation and economic diversification. Trust in continued global demand for oil exports has led to unsustainable building typologies, migration to suburbs, and the fragmentation of urban centres, leading to inevitable loss of community and cultural traditions. Doha has been a prime example of this kind of development for the last half century. However, the government of Doha has recently pursued an alternative model for the continued future growth of their city. Their first initiative is a comprehensive revitalisation of central Doha, funded entirely by the Qatari Royal family to the tune of $5.5 billion USD. The decade long project will transform a 31 hectare area into a dense, mixed-use development, based on vernacular Qatari architecture and utilising regional urban design strategies. Comprised of more than 100 new buildings, the development will combine retail, commercial and leisure programmes with housing to service 25,000 people, while also creating public spaces that are usable in Dohaâ&#x20AC;&#x2122;s intense climate. The aim is to restore a sense of community through a sequence of densely planned urban neighbourhoods with walkable access to services and amenities within the new urban centre. At an urban scale the plan takes into account several factors to better encourage pedestrian street activity. To minimise heat and maximise solar shading, the streets are as narrow as possible in relation to the heights of buildings, and are oriented to capture the cool sea breezes from the northsouth wind. The resulting grid has also been manipulated in response to sounding site conditions, keeping the curvature

of the main thoroughfare to the south, retaining the Meccaorientation of the main market street, and bypassing several vernacular houses utilised as museums. Additionally, all car parks and service roads have been hidden underground to enhance the public pedestrian realm. These urban scale considerations are dramatically different then current conditions in Qatar, where even the shortest distances require the need for vehicles. Architecturally, several small, but important considerations have been made for building strategies. Semi-private courtyards have been reintroduced in an initiative to return Doha back to its cultural traditions. These allow for social environments to develop through interactions in a shared space by clusters of related families. In addition, features such as covered colonnades, shading canopies, and evaporative water strategies have all been incorporated to develop a habitable environment for pedestrians from intense the sunlight and heat. These approaches to environmental, cultural and social issues within Doha are critical steps to revitalising the city's cultural centre and reversing the trend of residential migration to surrounding suburbs. It is a difficult task to develop an interest for this form of urban morphology which is currently in small demand by Qatari citizens, but Msheireb Downtown Doha has the potential to change these opinions once completed in 2020. This new district gives hope to radically shift expectations of planned urban design if it can prove a commercial and cultural success; with the potential to change the face of many cities across the Middle East and other urbanising regions around the world.

!Methods!53


Photograph(3.23: Aerial view of Shibam, Yemen Source: <http:// webodysseum.com/art/ shibam-the-city-madeout-of-mud-bricks/>

Photograph(3.24: Shibam, Yemen Source: <http:// webodysseum.com/art/ shibam-the-city-madeout-of-mud-bricks/> Photograph(3.25: Shibam, Yemen Source: <http://annadingding.blogspot. co.uk/2011/08/ travellers-wishlist.html>

Shibam, Yemen Climatic conditions are one of the key parameters in the development of urban morphology, defining the vernacular approaches of local built environments. Analysis of how these methods affect performance in extreme heat and sun exposure is essential when developing design proposals within the Middle East. Shibam, located in Yemen, is an excellent case of vernacular adaptability within the region. First established during the pre-Islamic period, it developed its current urban morphology as a walled city in the 16th century.85 This cluster of tall, mud brick, tower houses has been described as the 'Manhattan of the desert' composed of dwellings five to eleven stories high, developed on a fortified, rectangular grid plan.86 Shibamâ&#x20AC;&#x2122;s compactness allows for a walkable city throughout its narrow streets and public squares. These passages and openings provide the 7000 inhabitantâ&#x20AC;&#x2122;s with sufficient shading throughout a majority of the day through strategies developed over many centuries in the layout of its urban morphology and building relationships. Analysis of design principles within Shibam will define strategies to achieve similar urban conditions in other arid, desert climates. The relationships between building heights and street widths, orientation of roads and buildings, and the dispersal of public spaces can all inform how a city can develop and maintain a balanced relationship between the building morphology, layout and the climate.

54!Collective Ecology


Photograph(3.27: Aerial view of Kerman, Iran Source: Herdeg, K, (1990) Formal Structure of Islamic Architecture. Photograph(3.26: Courtyard organisation within the urban fabric. Source: http://overland2010.blogspot. co.uk/2010/12/ south-through-desertyazd-kerman.html

Photograph(3.28: Main public buildings defining the skyline. Source: http:// overland-2010.blogspot. co.uk/2010/12/ south-through-desertyazd-kerman.html

Kerman, Iran The organizational principles which inform the morphological development of many Arab cities are typically influenced by the desire to establish multiple layers of segregation between groups within the city.87 Analysis of how these methods of separation affect the layout of networks and placement of public spaces will be essential for creating a culturally sensitive proposal within Qatar. Kerman, Iran, was once one of the best representations of Arab culture and values manifested within an urban form, prior to its substantial urban redevelopment in the middle of the 20th century. Its previous street network characterised clearly the systematic hierarchy of privacy thresholds within Arab culture and cities. Its old branching streets and paths acted as fault lines within a seemingly homogeneous urban landscape, creating multiple layers of seclusion to distinguish ethnic and religious fractions throughout the city. 88 These layers of privacy are also expressed in the organisation of public spaces and private courtyards, guiding their dispersal and form throughout Kerman. Large public open spaces spread out from central areas along the streets as bazaars containing the daily activities of business and leisure within the city. Smaller local paths branch off from these main streets, leading to common more semiprivate spaces for members of a specific group or sect, and finalise as a private courtyard for families.89 Analysis of the design principles within the original urban fabric of Kerman, Iran before its present day redevelopment, will define strategies to achieve similar cultural conditions through the development of networks and organisation of public spaces and within the city.

87.â&#x20AC;&#x201A; Ragette 88.â&#x20AC;&#x201A; Herdeg 89.â&#x20AC;&#x201A; Asquit

!Methods!55


Photograph(3.29: Jamaa El Fna Square, Marrakesh, Morocco Source: Nicolas Cabargas Mori

Traditional Architecture

Region p.50 1.â&#x20AC;&#x201A; Ibid p.51 2.â&#x20AC;&#x201A; Ibid p.53

Arab Urban Environments Traditional Arab urban environments are the result of dense groupings of courtyard buildings over long periods of time. They provide light and air through central internal spaces, diminishing heat gain and allow for wall adjacencies with neighbouring buildings. Typically surrounding a central square, the gradual agglutination of buildings establish irregularly patterned streets which narrow as they spread away from the centre, finalizing as a semi-private dead ends serving multiple building entrances. Groups of buildings usually have a common open space and are organised through ethnic or religious affiliations, establishing several distinct quarters within the city.90 Urban Gradients Cultural modalities reinforce the delicate tension between the segregation and togetherness of ethnic and religious groups throughout an Arab city. This notion establishes clear distinctions of public, semi-public and private spaces within building and urban scale environments.91 The largest public spaces are in front of the Friday masque, which typically serve several quarters within city. Functions taking place in this space are public and open for everyone without distinction, gradating into more segregated and private groupings of the city as they are distanced from the public square. The narrowing and irregularity of the streets adds visual barriers within the urban landscape and reinforces the desired levels of seclusion between groups within the city.92

56!Collective Ecology


Photograph(3.30: Aerial view of Rheris Valley, Morocco. Source: <http://3. bp.blogspot. com/-zBs4LvWZUo8/ Tg9YA4UC7YI/ AAAAAAAAFgI/ vvstva5fe0M/s1600/ Yann+Arthus-Bertrand+++morocco.jpg> Photograph(3.31: Kuwaiti Interior Courtyard. Source: <http://www. traveladventures.org/ continents/asia/beitkhalid03.html> Photograph(3.32: Aerial view of El Atteuf in Algeria Source: <http://voffka. com/archives/2010/ 12/14/065155.html>

Courtyards Courtyards are the core of most Arab buildings, serving as a central common circulation space with peripheral rooms organised around it. The introverted character of Muslim family life closes off the home from the outside, establishing a set hierarchy of spaces, experienced as a succession of volumes throughout the building. They are typically subject to regularity and have set ideal proportions in their distribution. As the surroundings rooms are distanced from the courtyard, there is a progression of increased privacy and segregation, establishing clear thresholds of separation throughout the building.93

Passive Strategies As a response to the harsh climatic conditions within areas of the Middle East, a number of passive design strategies have been implemented within traditional Arabic buildings and their organizations to create relatively cool microclimates throughout the city. The sun is typically the principle factor for governing the approaches to habitation within arid climates. It drives the orientation building elements in order to maximise building shading and reduce solar exposure on surfaces. The exceptions to this strategy however are Islamic religious buildings, which govern their orientation with regards to Mecca.94 The organization and orientation of these buildings organise and shape the roads and alleyways throughout the city. The tapering of their widths as they move away from the central square provide increased shading to retain any cool air that may be deposited during the night, and their irregular orientations prevent excessive air movement, which carries with it sand and dust. Interior courtyards are also excellent modifiers of hot and dry climates, acting as an air-well collecting dense, cool air at night. As the sun heats the courtyard, the air heats up and rises, creating convection currents, and circulating air flow in from adjacent cool streets and alleyways, successfully cooling surrounding spaces well into the day.95

93.  Ibid, p. 94.  Ibid p. 95.  Asquit

!Methods!57


Photograph(3.33: Aereal view of Abu Nakhla Wetland, Qatar. Source: Google Earth Photograph(3.34: Abu Nakhla Wetland, Qatar Source: <http:// sahilonline.blogspot. co.uk/2008/12/birdingin-al-khor-qatar.html> Source : Jdylan - Green Zone bordering Water Reservoir

Natural Wetlands: Abu Nakhla, Qatar

Biodiversity Biodiversity Biodiversity

58!Collective Ecology

The Abu Nakhla Reserve is unique given that it is not used for the purification of water or as an overflow containment wetland, but as an experimental reserve operated by the Department of Biological and Environmental Sciences at Qatar University. Located on the outer edge of Qatarâ&#x20AC;&#x2122;s largest city, Doha, it receives a fluctuating supply of treated municipal water discharged from local sewage treatment stations which continually adjusts the wetlandâ&#x20AC;&#x2122;s depth and the reserves topography.96 Given that Qatar is one of the few countries in the world that contains no natural surface bodies of fresh water, 97 researchers initially lacked local wetland precedents for examples as they developed the reserve. As a result, they introduced species of wetland plants from other areas around the Persian Gulf that have adapted to similar soil conditions, animal communities and climate in order to survive within the Abu Nakhla Reserve. The research conducted by Qatar University has demonstrated high levels of plant diversity within the reserve, and has successful introduced a number of submerged, littoral, and above-ground plant species for establishing future wetlands in Qatar. 98


Photograph(3.35: Qunli Constructed Wetland Park By Turenscape Source: <http://www. archdaily.com/446025/ qunli-stormwater-wetland-park-turenscape/> Photograph(3.36: Inhabitant interaction of Qunli Constructed Wetland Park By Turenscape Source: <http://www. archdaily.com/446025/ qunli-stormwater-wetland-park-turenscape/> Photograph(3.37: Relation with the city, Qunli Constructed Wetland Park By Turenscape Source: <http://www. archdaily.com/446025/ qunli-stormwater-wetland-park-turenscape/>

Qunli National Urban Wetland: Haerbin, China Located in Haerbin, China, the Qunli National Urban Wetland project demonstrates strategies for integrating wetlands within an area of dense urban development. The wetland serves a natural water treatment system, collecting and cleansing stormwater runoff from surrounding road networks. It provides a natural habitat for many species of flora and fauna, and offers much needed local public space within a dense urban environment. The majority of the wetland is established as a ‘Surface Flow Constructed Wetland’, with exposed surface water as small ponds between mounds of earth throughout the site. Surrounding the perimeter of the site is a ring of ‘Sub-Surface Flow Constructed Wetlands’ which establishes solid terrain to inhabit, and acts as a buffer zone for the core wetland, acting as a transition between nature and the city. (Reference Chapter: 2 Domain/Wetlands) Native wetland grasses and meadows are grown throughout the site at various heights, establishing a series of transitions from one type of environmental condition to another. A network of elevated paths link the perimeter of the wetlands, branching out and infiltrating into areas above the ponds and terrain, allowing visitors to experience the wetland from multiple perspectives at different levels of elevation. The Qunli National Urban Wetland is an successful example of constructed wetland integration within an urban context. Its local filtration of polluted water runoff and incorporation of public space throughout the wetland demonstrates strategies that can be utilised for integration wetlands as public space throughout our urban system.

!Methods!59


Photograph(3.38: Student Rooms, Hooke Park, Architectural Association School of Architecture Educational Facilities, Dorset, UK. Source: Nicolas Cabargas Mori Photograph(3.39: California Academy of Sciences by Renzo Piano Source: Tim Griffith <http://seedmagazine. com/slideshow/ california_academy_ of_sciences/>

Figure(3.37: Vertical Farming project by SOA Architects. Source: <http:// www.verticalfarm. com/designs?folder=510aa317-7750-417692b0-99f7e98cddb1>

Integrating Vegetation Within Buildings

Vol 33 No. 1 Evaluation

The integration of vegetation into building morphology has primarily been assimilated through green roofs on existing structures and as unbuilt proposals of vertical farming within skyscrapers. These approaches have shown to be environmentally and economically beneficial, and technically possible to engineer, demonstrating strategies that can be utilised for successfully integrate wetlands into buildings. Green Roofs Green roofs have several proven benefits at a building and urban scale. They can provide great thermal performance to moderate temperature levels inside of buildings, subsequently requiring 50-90% less continuous artificial heating and cooling.99 Collectively, a concentration of green roofs in an urban area can also improve air quality and reduce the city's average temperatures through evapotranspiration.100 Buildings with integrated green roofs however, must be able to accommodate the additional static loading of soil substrate and retained water, requiring additional structural support within the building morphology.

60!Collective Ecology

Vertical Farming Proposals of vertical farming have been increasingly developed within the last decade, but there has yet to be a built example of a large scale vertical farm. Proponents of vertical farming argue they allow for localised production and increased yields within a controlled setting, but environmental scientists and engineers strongly criticise these proposals as being unrealistically feasible and question their necessity. These proposals demonstrate that although architectural engineering and agricultural technology are capable of integrating vegetation and allowing growth within buildings, the economic and productive yield feasibility of vertical farms within skyscrapers is currently not a viable option.


Figure(3.38: Singapore Airport Interior by CPG Corporation Source: <http://www. businessinsider.com/ the-10-best-airports-inthe-world-2013-4> Figure(3.39: Vertical Forest by Stefano Boeri Source: <http:// thisbigcity.net/ worlds-first-vertical-forest-under-construction-in-milan/> Photograph(3.40: Park Royal on Pickering by WOHA Source: <http://www. archdaily.com/363164/ parkroyal-on-pickering-woha-2/51756334b3fc4b748700014c_parkroyal-on-pickering->

Green Walls The addition of green walls to new or existing buildings provides benefits to urban environments in a number of ways. Typically applied to areas with high solar exposures, green walls alter their local microclimates and act as filters within the surrounding urban environment. The vegetation allows for the cooling of the interiors of buildings and the surrounding urban areas through the processes of shading, reduction of reflected heat, and evapotranspiration. Green walls also improve air quality though absorption of heavy metal particulate from the atmosphere, reducing levels of urban dust brought in from the surrounding dessert.101 Integrating plants capable of absorbing dissolved nutrients in the green wall system can also act as filters for localised grey water treatment.102

Vertical Plant Integration There have been many proposed projects integrating green spaces within skyscrapers, with the few constructed used as luxury residences and hotels. These often aim at introducing more outdoor natural spaces within an urban context, and are typically in the form of private terraces, filled with local vegetation. With similar benefits as green walls, vertical plant integration helps protect from solar radiation, and helps control dust particles and acoustic pollution. Proposals and built projects often feature vegetation integration capable of filtering and reusing grey water produced by the building, demonstrating the viability of localized water reintegration.

101.â&#x20AC;&#x201A; Hoyan 102.â&#x20AC;&#x201A; AIA G

!Methods!61


Computational Techniques Digital strategies used for the development of the project Figure(3.40: Evolution of Tuscan column through genetic algorithms. Source: Adapted figure from Frazer, J. (1995) An Evolutionary Architecture.

Genetic Algorithms

orithm.html nking p.120 Algorithms rithms p.63 ithms, p. 65 rithms p. 65 ptimization

A genetic algorithm (GA) is a class of adaptive stochastic optimisation algorithms involving heuristic search, mimicking the processes of natural selection.26 GAâ&#x20AC;&#x2122;s are routinely used to generate efficient solutions to optimisation and search problems in many different fields. The process starts with the development of a population of candidate solutions to an optimisation problem that can be evolved toward continually better solutions based on single or multiple fitness criteria. Each candidate, called an individual, has a given set of properties or characteristics that can be mutated and altered to allow for progressive optimisation through a series of solutions.27 The evolution is an iterative process, starting from a population of randomly generated individuals, with the population in each iteration called a generation. For every generation, the fitness of each individual in the population is evaluated and ranked, based on an established fitness function, assessing the desirability of their condition for the optimisation problem being solved. Individuals that scored best are stochastically selected with their characteristics recombined to establish the next generation of individuals in a population. This process for creating new generations of candidate solution terminates when a user defined maximum number of generations has been produced, or the population has reached an established satisfactory fitness level for the optimisation criteria.28 Multi-objective Optimisation Genetic algorithms involving more than one simultaneous objective can yield multiple sets of possible solutions, with each set optimised according to differing fitness criteria.29 GAâ&#x20AC;&#x2122;s are well suited to multi-objective optimization

62!Collective Ecology

problems as they are fundamentally based on biological processes which are inherently multi-objective.30 These multi-objective optimization algorithms allow for the option to assess the advantages and trade-offs between different design morphologies comprised of mutually concurrent and often conflicting objectives.31 This approach develops many options to evaluate rather than only one optima solution as found in single objective genetic algorithms.32 Utilizing multi-objective genetic algorithms in the development of urban and building scale morphologies explores and exploits the gradual improvements of multiple environmental and cultural characteristics to establish optimised design strategies and increase performance. They will be employed for the integration of wetlands into the building morphology, and in the organisation of public and private spaces throughout the urban topography.


Photograph(3.41: Sunlight in the streets of London

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Source: By Stefan Powell from Toronto; Canada (golden energy) Figure(3.41: Sunlight hours analysis of a public space using computational models.

Solar Analysis Daylight Sunlight refers to direct sunshine throughout the day, and experiences significant changes in intensity at differing latitudes and times of year.110 It is an important factor to consider in the development and layout of an urban system in order to either embrace or obscure it through design strategies. Diffused Sky Radiation however refers to the level of natural light scattering in the atmosphere to be reflected off terrestrial surfaces.111 It is a very effective light source, illuminating a given area without projecting heat to be absorbed by surfaces, a critical factor to consider for design strategies where extreme climate conditions are an issue. Luminance Emissions or reflections from surfaces are known as Luminance, which indicates how much luminous power the eye will detect while viewing the surfaces from a particular angle of view.112 Luminance is thus an indicator of how bright the surface will appear, and will never be more than equal to the input light source.113

Illuminance Illuminance is the density of photons which fall within a given surface area.114 For a given light source, the closer to a light source the illuminated area is, the higher the Illuminance value of light striking a surface. 115 Illuminance values are more critical to consider in a design than luminance, as buildings require differing amounts of intensity based on tasks undertaken in them.

110.  111.  112.  Long, 113.  (2009 114.  Long, 115.  (2009)

Sunlight Hours Analysis of sun exposure will evaluate and visualise how many hours of sunlight will fall on a given surface. This can be particularly helpful when quantifying solar access of key spaces and surfaces to allow sufficient sun exposure for solar panels and plants, or provide adequate shading for people within a space. Solar Fan Identifying the solar path for a specific location creates a volume describing the solar azimuth and solar zenith throughout the year. This defines the volume that should not be encroached upon to receive maximum solar penetration.

!Methods!63


Figure(3.42: Multimodal Shortest Path Tree of Portland, Oregon. Public transit branches are coloured red, while walking branches are coloured black. The width of a branch corresponds to the number of shortest paths that travel through that branch. Source: <http://graphserver.sourceforge.net/ gallery.html> Figure(3.43: Minimum Spanning Tree Source: <http://www. flickr.com/photos/ ethanhein/ 2306994386/>

Graph Theory

Graph.html> s Vol. 11 No. oi Diagrams 3–800, 1934 iangulation gTree.html> , 4th Edition

Introduction In mathematics and computer science, graph theory is the study of mathematical structures used to model relationships between entities.116 Many practical problems can be represented by graphs to demonstrate pairwise relations among objects and to process dynamics in physical, biological, social and information systems.117 A graph is constructed of ‘nodes’ representing objects and ‘edges’ demonstrating their connections. We utilise graph theory and its subsets in several portions of our project, for optimisation of networks, site tessellation, and to establish hierarchies within the system. Shortest path Within graph theory, Dijkstra’s or Bellman-Ford’s algorithms can be utilised to establish the shortest path between a given set of nodes so that the sum of the lengths of its constituent edges is minimised. These paths can represent physical entities such as distances on a map with intersections represented as nodes, and roads represented as edges to find the fastest route. Shortest Path can also represent abstract entities such that nodes embody states and edges describe their possible transitions, or it can be used to find an optimal sequence of choices to reach a certain goal state. Delaunay Triangulation The application of Delaunay Triangulation to a given set of nodes within a graph can be exploited to compute the most efficient paths for a Euclidean Minimum Spanning Tree through the same nodes.118 A triangulation is formed by constructing edges between pairs of nodes so that the

64!Collective Ecology

edges form a non-overlapping set of triangles. 119 Delaunay Triangulation ensures that the circumcircle associated with each triangle created contains no more than three nodes in its interior, maximising the minimum angle of each triangle within the triangulation120 and developing a characteristic that each connection is no longer than 2.418 times the Euclidean distance between the two nodes. Minimum Spanning Tree Given a connected set of points in graph, a minimum spanning tree of that graph is created by establishing the shortest possible length that connects all points in a set without creating any closed loops.121 A single graph can have many different spanning trees, and the minimal spanning tree is found by summing the lengths of each edge connection within each spanning tree and comparing them to one another.122


Figure(3.44: Space Syntax axial analysis using graph theory. Centrality to see the integration of different roads in London, UK Source: Modified Space Syntax <www. spacesyntax.com> Figure(3.45: Graphical representation of contacts within social clusters and their connections. Source: Brand Tao <http://brandtao. wordpress. com/2011/04/18/ inmaps-social-network-connections-visualised/>

Centrality There are several methods for measuring the centrality of a node within a connected graph to determine its relative importance.123 This research was initially developed for social analysis to quantify the influence a person has within a social network, and has since been adapted to calculate road connectivity within urban networks. Degree Centrality The first method developed is conceptually the simplest for measuring importance within a graph, which is defined by the number of links incident upon a node in relation to others. If seen in terms of flow throughout the network, nodes with increased connectivity have higher levels of Degree Centrality, facilitating flow to them more often than others.

node acting as a bridge is fractional, which means that if there is more than one possible shortest path this will get distributed between the different solutions. Nodes that have a high probability to occur on a path between two random nodes being evaluated will have a higher level of betweenness. Within our research this method measures the probability of a road being used in the network helping identify intersections that could be utilised as locations for points of interest in the system.

123.â&#x20AC;&#x201A; Newm 124.â&#x20AC;&#x201A; Sabid 125.â&#x20AC;&#x201A; Newm

Closeness Centrality In every connected graph there is a distance between all pair of nodes, this distance can be defined by the shortest path. The farness of a node is defined as the sum of its distances to all other nodes, and its closeness is defined as the inverse of the farness.124 With this you can define how central a node is in respect to all others. The lower this value is, the closer it is in average to the rest of the nodes.125 Our research will utilise this method for locating instances of key functions within the urban system. Betweenness Centrality The quantification of the number of times a node acts as a bridge along the shortest path between two other nodes is known as Betweenness Centrality. The count of the !Methods!65


53.  Weidmann, F. (2012) ‘The Urban Evolution of Doha’ METU.JFA 54.  ESCWA (2012) The Demographic Profile of Qatar 55.  Weidmann, F. (2012) ‘The Urban Evolution of Doha’ METU.JFA 56.  Jaidah, I., et.al. (2009) The History of Qatari Architecture 57.  Al Buainain, F. (1999) Urbanisation in Qatar: A Study of the Residential and Commercial Land Development in Doha City 58.  Weidmann, F. (2012) ‘The Urban Evolution of Doha’ METU.JFA 59.  Ibid. 60. 

61.  62.  Ragette, F. (2006) Traditional Domestic Architecture of the Arab Region p.50 63.  64.  Asquith, L. et. al. (2006) Vernacular Architecture in the Twenty-First Century p.208 65. Ragette, F. (2006) Traditional Domestic Architecture of the Arab Region p.50 66.  Ibid p.51

67.  Ibid p.53 68.  Ibid, p.59 69.  Ibid p.84 70.  Asquith, L. et. al. (2006) Vernacular Architecture in the Twenty-First Century p.208 71.  Kardousha, Mahmoud M. Dr., 2009. Qatar Biodiversity 72.  Ibid. 73.  Ibid.. 74.  Gill, S. et.el. (2007) “Adapting Cities for climate Change: The Role of the Green Infrastructure.” Built Environment Vol 33 No. 1 75.  Liu, K. et.el. (2003) Thermal Performance of Green Roofs Through Field Evaluation 76.  Hoyano, A (1988) Climatological uses of plants for solar control and the efforts on the thermal environmental of a building, Energy and Buildings 11 77.  AIA Green Wall Committee (2008) Introduction to Green Walls Technology, Benefits & Design 78.  Rowland, T. et. al. "Genetic Algorithm." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/GeneticAlgorithm.html 79.  Menges, A. et. al. (2011) Computational Design Thinking p.120 80.  Mitchell, M. (1999) An Introduction to Genetic Algorithms


Methods References 81.  Koumoutsakos, P. et.al (2000) Multiobjective optimization using evolutionary algorithms p.63

94.  B. Delaunay: Sur la sphère vide, Izvestia Akademii Nauk SSSR, Otdelenie Matematicheskikh i Estestvennykh Nauk, 7:793–800, 1934

82. Ibid. p. 65 83.  Ibid. 84.  Zuluaga, M. et.al (2012) Active Learning for Multi-Objective Optimization 85.  86.  87.  88.  (2009)

95.  Lee, D. et. al. (2007) Protocol Design for Dynamic Delaunay Triangulation 96.  Weisstein, Eric W. "Minimum Spanning Tree." From MathWorld - A Wolfram Web Resource <http:// mathworld.wolfram.com/ MinimumSpanningTree.html> 97.  Sedgewick, R. (2013) Algorithms, 4th Edition 98.  Newman, M .E.J. (2010) Networks: An Introduction 99.  Sabidussi, G. (1966) ‘The Centrality Index of a Graph’ Psychometrika Vol. 31, Iss. 4 100.  Newman, M .E.J. (2005), "A measure of betweenness centrality based on random walks" Social Networks Vol. 27 No. 1

89.  90.  (2009) 91.  Weisstein, Eric W. "Graph." From MathWorld - Wolfram Web Resource <http://mathworld.wolfram.com/Graph.html> 92.  Pirzada, S. (2007) 'Applications of Graph Theory' Journal of the Korean Society for Industrial and Applied Mathematics Vol. 11 No. 93.  Okabe, A.; et.al (1992) Spatial Tessellations: Concepts and Applications of Voronoi Diagrams


Experiments Analysis of Streets and Public Spaces p.72 Subdivision and Integration p.76 Traditional Socio-Cultural Values p.80 Plot Distribution Strategies p.86 Network Strategies p.98 Building Morphologies p.108


Experiments Overview The possibility of moving away from reliance on desalination plants and towards localised water treatment in constructed wetlands is examined through analysing their spatial requirements within the urban context and possible integration within architecture. These approaches, coupled with analysis of Shibam, Yemen, demonstrate strategies for urban organisation and building morphologies which enable better performance in the harsh climatic conditions similar to those seen in Doha, Qatar. It further informs strategies for developing relationships between locations of interest in a network and their level of connectivity within an urban system, leading to experiments in patch development focusing on the affects patterns of subdivisions have on levels of connectivity within networks.


Analysis of Streets and Public Spaces The case study of Shibam, Yemen Photograph*4.42: Aerial view of Shibam, Yemen Source: <http:// webodysseum.com/art/ shibam-the-city-madeout-of-mud-bricks/>

Overview The unique layout and morphology of Shibam, Yemen demonstrates a well-connected and dense urban fabric capable of handling the extreme levels of solar exposure and high temperatures. Through analysing these characteristics, geometric ratios are abstracted, and quantifiable data is collected, which can be utilised to drive network layout and building morphologies within similar climatic conditions, such as Doha, Qatar. Study 1: Network Analysis Methods for analysing degree, closeness and betweenness centrality within networks measure the relative level of connectivity and importance of a node within a graph. Utilising these techniques creates an understanding of the network topology within Shibam and the impact or correlation it might have on the location of public squares or important buildings. Degree Centrality The diagram analysis demonstrates clearly that there are four highly connected vertices, all of which are nodes representing public squares. (Fig 4.46) The bar chart demonstrates that many nodes are connected to only one additional neighbour, meaning there is a high level of impasse streets. Additionally, a majority of the nodes connect to three other neighbours, signifying that many intersections connect only three roads in the network. (Fig 4.47) Closeness Centrality The central public square in Shibam contains two highly integrated nodes, with additional surrounding nodes 72!Collective Ecology

decreasing in value as they move away from this central point. (Fig 4.48) Analysis of the bar chart reveals the integration values of the nodes follow a bell curve, with very few nodes being highly integrated and very little nodes being hardly integrated. (Fig 4.49) This signifies that a majority of Shibam is fairly evenly connected with a few main points of interest and just several areas that are isolated. Betweenness Centrality The importance a node has within the network can be measured by the probability it has of being used to pass from one node to any other. The bar chart reveals that there are a few nodes which have an extremely high likelihood of being used within Shibamâ&#x20AC;&#x2122;s network. (Fig 4.50) Analysis of the diagram reveals that these nodes are located within the public squares and receive a high level of traffic through them. (Fig.4.51) Qualities Measured: -Betweenness Centrality -Closeness Centrality -Degree Centrality Analysis: All of the centrality analysis clearly demonstrates two primary locations within Shibamâ&#x20AC;&#x2122;s network. These two public squares contain a majority of the activity within the city and demonstrate a correlation between the network topology and the location of its architecture, primarily important points of interest such as mosques and areas where markets congregate.


Degree Centrality Figure*4.46: Diagram of most integrated nodes in Shibam using Degree Centrality analysis.

Degree Centrality Number of Nodes

100 80 60

Figure*4.47: Graph, Number of nodes versus degree values, resulting from Degree Centrality analysis.

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Closeness Centrality Figure*4.48: Diagram of most integrated nodes in Shibam using Closeness Centrality analysis.

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Figure*4.49: Graph, Number of nodes versus centrality values resulting from Closeness Centrality analysis.

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Betweenness Centrality Figure*4.50: Diagram of most integrated nodes in Shibam using Betweenness Centrality analysis.

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Figure*4.51: Graph, Number of nodes versus centrality values resulting from Closeness Centrality analysis.

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low

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!Experiments!73


Shibam Figure*4.52: Top view plan of Shibam .

3.46m i. 3.46m

Figure*4.53: Analysis of Shibam streets width

A

ii.

iii.

B

5.2m

3.46m

Entrance

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Mosque

i.

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2.33m iii.

Figure*4.54: Space A: Orientation North-South. Analysis of the main public spaces in Shibam.

Figure*4.55: Space B: Orientation East-West. Analysis of the main public spaces in Shibam,

A 3542m2

Study 2: Solar Exposure Studies Analysis of solar exposure levels throughout the year within Shibamâ&#x20AC;&#x2122;s public squares and streets will establish strategies for developing usable outdoor public spaces and promote pedestrian street use. Investigations into the two main public squares demonstrate the impacts of how orientation and geometry influence the use of the spaces throughout the seasons. Examinations of the street network will provide insight into how its organisation can influence shading conditions. Located in the centre of Shibam, the first public space analysed is located near the centre of the city, its orientation is along the north-south axis and is located adjacent to the largest mosque in Shibam. (Fig. 4.54) To the south east of this space another public square is located which is the largest open space within the city, and has approximately a 2:1 width to length ratio, with its geometry primarily facing towards the east-west axis. (Fig. 4.55)

74!Collective Ecology

B 4331m2

Qualities Measured: -Average number of sunlight access hours. Analysis: Solar analysis studies reveal that the orientation of the central public square provides sun protection year round through shading provided from adjacent buildings along the East and West edges. (Fig. 4.65) This strategy limits exposure to only 4 hours of direct sunlight throughout the day, with the majority of this exposure confined to areas closest to the street intersections. The southeast public square however is allowed access to sunlight year round, with daily exposures ranging from 7-13 hours during the summer and only 4-8 hours during the winter. Extreme solar exposure is not desirable during the summer months, but is welcomed within the public square during the winter as temperatures fall to a 10-15 °C average. Further analysis into the solar exposure maps reveals additional strategies within the street networks of Shibam. The grid has been massaged into a wavy set of non-linear

2.33m


Figure*4.56: Sun exposure analysis for different seasons in Shibam.

roads which is the key to its successful shading qualities. The buildings have orchestrated their positions to avoid similarly aligned facades and street widths, ensuring sun exposure for only minimal periods of time throughout the day. This arrangement, coupled with building heights, provides a nearly completely shaded pedestrian street environment throughout the entire year. The few streets that demonstrate higher levels of exposure tend to be along extended stretches of the east-west axis, creating seasonal network systems which provide pathways of preferable climatic conditions throughout extremes of both hot and cool weather.

Photograph*4.43: Shibamâ&#x20AC;&#x2122;s streets. Source: UNESCO / Maria Gropa

!Experiments!75


Subdivision and Integration Recursive Subdivision and Centrality Analysis

Influence of the attractor Figure*4.57: Example of subdivision experiment and analysis using Betweenness Centrality in a 500 x 500m patch, with attractors.

Level 3 Level 2

Level 1

Level 1

Resulting Subdivision

Overview A systematic approach for developing a heterogeneous set of block types and network organisations is established through evaluation of block proximity to elements on the site. The initial experiment explores how subdivisions are controlled and manipulated through gradual, recursive iterations based on location in relation to a set attractor. The smaller the distance between a parcel and attractor, the further each parcel subdivides into subsequently smaller parcels. (Fig 4.58) The resulting network is evaluated with betweenness centrality to assess how integrated each of the nodes are within the overall network, mapping out the most connected nodes. Parameters Level of subdivision Amount of attractor lines Location of attractor lines Experiment A series of patches measuring 500x500 meters were evaluated to test the placement of attractor lines and their resulting subdivided quadrilateral parcels. This reveals how many levels of subdivision are required to reach the size of a 76!Collective Ecology

block (150x70m), high-rise building footprints (90x90m) and low-rise building footprints (10x10m). Six studies were conducted with varying attractor positions, and two different starting conditions: a. The initial patch as one complete square b. The initial patch as a square separated into two triangles The resulting parcel distributions are analysed with Betweenness Centrality to measure the connectivity of each node. Observations Case 1 The analysis indicates that with a single linear attractor placed at the top of the patch, the most integrated nodes are in very similar locations for both case starting conditions. The topologies of the nodes differ however, with the recursive subdivision within the triangle being more integrated and more dispersed within the patch. (Fig. 4.59) Case 2 Placement of a single linear attractor through the middle of the patch demonstrates much different results in the level of connectivity between nodes. Within the patch subdividing a square, the most integrated nodes are located closer to the attractor, gradually minimising the level of connectivity as the distance from the centre of the attractor increases.


Recursive Subdivision of Square

Recursive Subdivision of Triangles Figure*4.58: Recursive subdivision logic for squares and triangles

Case 01_a

Case 01_b Figure*4.59: Betweenness centrality analysis of recursive subdivision experiments using squares and triangles.

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!Experiments!77


Case 03_a

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Figure*4.60: Betweenness centrality analysis of recursive subdivision experiments using squares and triangles.

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However, the recursive subdivisions within the triangle patch establishes nodes that have connectivity levels dispersed in a non-uniform manor located both near and away from the attractor curve. (Fig. 4.69) Case 3 The location of a single linear attractor running diagonally through the patch results in most integrated node dispersal throughout any of the cases tested. Although the patch is highly connected, the most integrated nodes are all relatively close to each other in a uniform manor rather than distinct moments of dispersal across the site. This success however is not seen in the patch subdividing the triangles, which demonstrates almost the opposite behaviour, with very low connectivity in two opposite corners of the patch. Case 4 An additional attractor curve is incorporated to explore methods of achieving higher levels of distribution for integrated nodes. The placement of attractors at opposite 78!Collective Ecology

Case 04_b

Double Attractor - Linear

ends of the patch demonstrates very similar results in both types of patches. They concentrate the most integrated nodes in the centre of the patch, with gradual dispersal out toward the edges. The subdivision of the triangle however disperses the subdivisions in a less homogeneous way while maintaining a high level of integration. Case 5 A third attractor is added, which decreases the amount of the most integrated nodes, but increases the overall connectivity levels. The dispersal is similar to the previous case, but with increased sub-divisions throughout the patch. The subdivision of the triangle patch disperses the more integrated areas throughout a larger portion of the site, establishing four zones of high connectivity. Case 6 The introduction of a non-linear attractor gives some insight into how parcel size behaves along curves and its affects the subsequent connectivity of the resulting nodes.


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The patches subdividing a square result in a clustering of nodes along the most planar portion of the attractor line. The opposite is true however for the patch subdividing triangles, in which the resulting sub divisions contain areas of highest integration where the angles of curvature along the attractor line are most intense. Conclusions These case studies, demonstrate that recursive subdivisions within the square and triangle patches lead to a series of unique network topologies with increased connectivity. Attractor curves, regardless of their placement, will establish further subdivided parcels and will return increased levels node connectivity within the network. Understanding and cataloguing these results will help inform strategies to influence and control areas of high connectivity in the development an integrated network.

!Experiments!79


Traditional Socio-Cultural Values Kerman, Iran Photograph*4.44: Ancient Kerman urban fabric. Source:: Ragette, F. (2006) Traditional Domestic Architectur e in Arab Regiona Figure*4.61: xxx

Overview Analysis of the ancient urban fabric of Kerman, Iran, before its present day redevelopment, gives insight into the influence its culture had on the organisation of buildings and public spaces, and development of networks within the city. Its once prominent branching of streets and paths, together with its dispersal of distinctly private courtyards and public open spaces, visibly demonstrates the privacy hierarchies and the organisation logics found in the architectures of ancient Arab cities. Through examining these characteristics, their dimensions and ratios are collected, which can be utilised to drive an atavistic approach to the organisation of buildings, and dispersals of private and public open spaces and networks within Doha, Qatar. Table*4.5: Urban fabric ratios.

Study 1: Quantifying the Urban Fabric Methods for measuring building dimensions and their aggregate relationships will help create an understanding of the role building clusters play in the development of public spaces between them, and establish guidelines for the proportions of building geometries and their associations throughout our urban system. Qualities Measured: -Densities -Proportions -Sizes Analysis: The tested sample demonstrates that a majority of the urban fabric is dominated by built area, at ratios higher than those found in all the city samples. (Chart 4.65) (Reference

80!Collective Ecology

Chapter: 3 Methods/Case Studies) Of the unbuilt area, very little consists of street space, with a majority of the area utilised as private courtyards within buildings. (Chart 4.66) The building clusters reveal a large range of plot sizes, however a majority of the plots range from 150-350 sqm with other sizes gradually tapering off to the extremes found in the sample. (Chart 4.64) Floor heights also varied throughout the sample area, with a majority characterised as double stories and none above three stories. (Table 4.50) Based on the average household size and amount of built space occupied by residential built area, the estimated population density is consistent to densities found in both

Urban Fabric Ratios Number of Buildings

517

Units

Built Area

163989

m2

Unbuilt Area

86011

m2

% Built Area

66%

% Unbuilt Area

34%

Open Space Area (w/o streets)

71789

m2

Minimum Stories

1

Floors

Maximum Stories

3

Floors

Average Stories

2

Floors

Floor Area

289679

m2

Building Density

1.16

floor area/ patch size

Residential Built Area

125690

m2

Public Built Area

38299

m2

Population (Estimated)

2274.08

persons

Density (Estimated)

91

people / hectare


Figure*4.62: Covered public space Figure*4.63: Built/UnBuilt Area

the Frankfurt and Doha city samples, although the Kerman sample reveals a built/unbuilt ratio nearly double both cities and even higher than Manhattan. (Reference Chapter: 3 Methods/Case Studies/Frankfurt, Doha)

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Public Open

Private Courtyards

Streets

Public Built !Experiments!81


Photograph*4.45: Building Rooftops Source:: Source:: Ragette, F. (2006) Traditional Domestic Architectur e in Arab Regiona

Qualities Measured: -Area -Frequency -Quantity -Distance Table*4.6: Courtyard and Public Spaces

82!Collective Ecology

Analysis: Analysis of privacy found in Kerman at the urban scale provides insight into the strategies Arab culture uses for establishing multiple thresholds of separation throughout the city. Public open spaces vary in a range of sizes (Chart 4.70) and are primarily focused around the main bazaar running North-South, with additional public open spaces scattered periodically throughout the remainder of the city sample. Private courtyards tend to be more evenly dispersed and are of a more equal size in relation to one another. (Chart 4.67) Their distance from the nearest public open space is often within a similar range, with only a few distances exceeding this average transition distance from one level of privacy to another. (Chart 4.71)

Private Courtyards Size 250

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Study 2: Analysis of Privacy Hierarchies Methods for establishing the relationships between multiple thresholds of privacy found in Kerman will help measure the frequency of each level of privacy and the distances from one threshold to the other within the city. The qualities that define each type of space will also be quantified to establish guidelines for creating social spaces throughout our urban system. These methods will create an understanding of the role public and private spaces play within the city, and establish guidelines for forming these cultural distinctions and separations throughout the urban system.


Figure*4.68: Public Open Spaces Figure*4.69: Private Open Space

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Figure*4.73: Graph, Public Open Space Area / Private Courtyards Area

Public Open Space !Experiments!83


Figure*4.74: Building Dispersal Source:: Source:: Ragette, F. (2006) Traditional Domestic Architectur e in Arab Regiona Figure*4.75: Network Dispersal

Study 3: Network Analysis Methods for measuring and quantifying the dimensions and relationships within the network will help create an understanding of the role streets play establishing the hierarchy of thresholds between public and private space within the city and establish guidelines for establishing these cultural distinctions and separations throughout the urban system. Qualities Measured: -Street Lengths -Street Widths -Intersections Table*4.7: Network Ratios

Analysis: There are three distinct types of street networks demonstrated in the area of analysis. These primary, secondary, and tertiary streets decrease in width, but increase in total street lengths as they transition from one type to another respectively. (Chart 4.78) The primary streets are typically straight and direct, with secondary streets establishing connections between them. Tertiary streets branch off from both of these in an obfuscatory manner and finalise as a dead end, with no street with straight segments exceeding 20m. (Table 4.70) Conclusions: Analysing Kerman has revealed several strategies developed within Arab culture for mitigating privacy through a series of morphological differentiations at both urban and building scales. The network organisation and building aggregation seen throughout the urban fabric has a direct relationship with the privacy hierarchies and the formations of social spaces

84!Collective Ecology

found throughout the urban system. The manner in which the network has intricately emerged between clusters of building plots in an often non-linear fashion, narrowing and finalising as dead ends, helps develop pockets of public and semi-public open spaces throughout the system. The proportions, dimensions and orientation of these spaces also have a direct relationship with the climatic conditions found in the region, often responding in a manner of reducing exposed surface area and increasing instances of shading. Quantifying these strategies found in Kerman, the data, ratios, and relationships extracted can be utilised as drivers Network Ratios Amount  Primary Roads

6

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10

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87

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54

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62%

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6%

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10%

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84%

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1402

m

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2108

m

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3758

m

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20355

m2

Uncovered Streets Area

14222

Streets Intersections

188

nodes

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Figure*4.76: Main network Figure*4.77: Main network and soial spaces

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in the development of our urban system. They will help produce the most appropriate approach embedding social and cultural aspects into the development of urban form while also responding to the extreme climatic conditions found within the Arab region.

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Connected Tertiary Roads !Experiments!85


Plot Distribution Strategies Aggregation of plots developing emergent social spaces Experiment 3 / Iteration 1 / Population 2 (Fittest Population)

Coverage Ratio (CR)

Figure*4.81: Low-rise plot distribution outcome.

Covered area (m2) / Boundary area (m2)

Coverage Ratio(CR): 0.99 Porosity Ratio(PR): 0.99 Proximity Average(PA): -0.87 Frequency Ratio(FR): 0.86

Overview Through the previous analysis of traditional building geometries and the organisational relationships found in Kerman, Iran, (Reference Chapter: 3 Methods/Case Studies/ Kerman) a range of plot sizes and associative patterns are extracted and used as drivers for building dispersal within a test area. Several strategies are explored to develop compact aggregations of building plots, while generating well connected emergent open public spaces between them. The resulting configurations are measured and evaluated based on several evaluation criteria that will drive the dispersal of geometries and interstitial spaces throughout the urban system. Methods A generative algorithm aggregates assorted plots throughout the test area based on their ability to cluster around a centroid with minimum space covered. The interstices between the plots define semi-public spaces with differentiated privacy hierarchies throughout the aggregation.

86!Collective Ecology


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Evaluation Criteria Coverage Ratio (CR) (Fig. 4.82) Covered Area ( sqm) / Block Area ( sqm) - Measures the floor area of the buildings (including open spaces within the cluster) against the area outside of the cluster. - Ambition: Maximise ground coverage by buildings within test boundary area.

Figure*4.82: Four main fitness criteria for development.

Table*4.8: Average comparison between itterations of experiments.

Frequency Ratio for Public Space (FR) (Fig. 4.82) Quantity of Public Spaces / Quantity of Building Plots - Measures the frequency of public spaces in relation to number of building plots. - Ambition: Maximise the quantity of public spaces among building plots.

Porosity Ratio (PR) (Fig. 4.82) Perimeter Length (m) / Bounding Box Length (m) - Measures the perimeter length of the cluster of buildings against the perimeter of the clusterâ&#x20AC;&#x2122;s bounding box. - Ambition: Maximise perimeter condition, ensuring increased inlets that can be utilised as public spaces. Proximity Average for Public Space (PA) (Fig. 4.82) Distance (m) - Measures the average distance from the centroid of each building to the centroid of the closest public spaces. - Ambition: Minimise the average distance of each building to the nearest public space.

!Experiments!87


Aggregation Sequence Fittest Population: Experiment 3 / Iteration 1 / Population 2 Plot Aggregation 00

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!Experiments!89


Experiment 1A: Starting Point for Plot Aggregation Experiment 1 / Iteration 1

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Figure*4.84: Differentation of aggregation start point.

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Experiment 1A: Starting Point for Plot Aggregation This initial investigation will explore the variations found in building plot aggregations resulting from multiple starting point locations. Each iteration will be initiated at a different point of origin, from which building plots aggregate until reaching an established plot quantity limit or reaching the boundary condition of the test area. Each of the iterations will produce 10 populations, which will be analysed according to the fitness criteria, and evaluated to find the most optimal start condition.

Table*4.9: Analysis of multiple iterations

Parameters: -Plots Size: Randomly range between 100-400 sqm (Extracted from Kerman study) -Boundary Area: Rectangle of 100x125m (Extracted from Kerman study) -Plot Quantity: 50 units or until boundary condition is reached -Plot Orientations: North-South axis Variables: -Aggregation Origin Position within Boundary Area: Boundary Area Centroid (Fig. 4.84), Corner (Fig. 4.84), Centred along longest edge (Fig. 4.85) and Centred along shortest edge (Fig. 4.85)

90!Collective Ecology

Average Comparison Experiment 1 Fitness Criteria

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Coverage Ratio

0.88

0.91

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0.90

Porosity Ratio

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1.07

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Proximity Average for Public Space

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Frequency Ratio for Public Space

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Highlighted Value

Top 1


Experiment 1 / Iteration 3

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Figure*4.85: Several examle results.

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Observations: It can be observed that the populations within ‘Iteration 3’ achieved the best overall average of the Fitness Criteria. Although ‘Iteration 1’ demonstrated the best Proximity Average for Public Space, and ‘Iteration 2’ averaged the highest Coverage Ratio, ‘Iteration 3’ outscored for highest Porosity Ratio and Frequency Ratio for Public Space, accumulating highest fitness overall. (Table 4.90) Analysis: The success of ‘Iteration 3’ reveals that the fittest starting point is centred along the longest edge of the boundary area, with the least successful position located at the centre of the block, as seen in the results of ‘Iteration 2’. It is also found that establishing a fixed maximum quantity of plots within the experiment limited the coverage ratio and the porosity within the iterations, which will be explored in subsequent experiments. ‘Experiment 1A’ included 4 Iterations, each iteration containing 10 populations. Full results can be referenced in the Appendix (Appendix/Experiments/Plot Distribution Strategies/Experiment 1A)

!Experiments!91


Experiment 1B: Starting Point for Plot Aggregation Iteration 1 / Population 0

Experiment 1B / Iteration 1 Figure*4.86: Adjuested Staring Point Locations

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Experiment 1B / Iteration 2

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Experiment 1B: Starting Point for Plot Aggregation Expanding from Experiment 1A, the two best cases are analysed again, however these iterations do not limit the ‘Plot Quantity’, allowing for additional plots to aggregate until reaching the edges of the boundary condition. Each of the two iterations will produce 10 populations, which will be analysed according to the fitness criteria, and evaluated to find the most optimal start condition. Parameters: -Plots Size: Randomly range between 100-400 sqm -Boundary Area: Rectangle of 100x125m -Plot Quantity: No limit until expansion reaches boundary condition -Plot Orientations: North-South axis

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92!Collective Ecology

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Table*4.10: Comparison chart of Iteration 1 and 2.

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Ratio for Public Space, ‘Iteration 2’ averaged the highest Coverage Ratio, Porosity Ratio and Proximity Ratio for Public Space, accumulating highest fitness overall. (Table 4.10) Analysis: The success of ‘Iteration 2’ reveals again that the fittest starting point is centred along the longest edge of the boundary area. This position will ensure the most optimal location for initiating the building plot aggregation throughout further experiments. ‘Experiment 1B’ included 2 Iterations, each iteration containing 10 populations. Full results can be referenced in the Appendix (Appendix/Experiments/Plot Distribution Strategies/Experiment 1B)

Variables: -Aggregation Origin within Boundary Area: Corner (Fig. 4.86) and Centred along longest edge (Fig. 4.86)

Average Comparison Fitness Criteria

Iteration 1

Iteration 2

Observations: It can be observed that removing the limit to the Plot Quantity was beneficial to the populations of both iterations, allowing for better overall averages in their Fitness Criteria evaluation. The biggest improvement is seen in the coverage ratio value, which creates more reliable results. Although ‘Iteration 1’ demonstrated the best Frequency

Coverage Ratio

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Proximity Average for Public Space

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Frequency Ratio for Public Space

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Top 1

Experiment 1B


Experiment 2: Establishing Plot Orientation Experiment 2 / Iteration 1

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Experiment 2: Establishing Plot Orientation Moving away from a rectangular boundary test, aggregations of building plots are tested within a nonorthogonal boundary condition to analyse the impacts of orientating buildings parallel to the longest edge condition or along the North/South axis. An optimum orientation will be established for use throughout the urban system to best fit within the non-orthogonal block parcels. Parameters: -Plots Size: Randomly range between 100-400 sqm (Extracted from Kerman study) -Boundary Area: Quadrilateral (13,080 sqm) -Plot Quantity: No limit until expansion reaches boundary condition -Starting Point for Aggregation: Centred along the longest edge Variables: -Plot Orientation: Orientated parallel to North-South axis (Fig. 4.87) and Orientated parallel to longest edge of boundary condition (Fig. 4.87) Observations: Both iterations demonstrate very little difference in their result averages. ‘Iteration 2’ however has a slight advantage

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in all Fitness Criteria evaluated, with the largest gain in the Frequency Ratio for Public Spaces. (Table 4.11) Analysis: The slight advantage of ‘Iteration 2’ demonstrates that orientation of the plots would be most appropriate along the longest edge of the boundary condition. Variations in the orientation of multiple boundary conditions within the urban system will consequently help minimize solar exposure levels along the north/south axis. ‘Experiment 2’ included 2 Iterations, each iteration containing 10 populations. Full results can be referenced in the Appendix (Appendix/Experiments/Plot Distribution Strategies/Experiment 2) Average Comparison Experiment 2 Fitness Criteria

Iteration 1

Iteration 2

Coverage Ratio

0.96

0.96

Porosity Ratio

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1.07

Proximity Average for Public Space

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14.37

Frequency Ratio for Public Space

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3.95

Highlighted Value

Top 1

Table*4.11: Comparison chart of orientation results.

!Experiments!93


Experiment 3: Optimisation of Public Spaces Experiment 2 / Iteration 2

Experiment 2 / Iteration 2 / Population 0 45

Figure*4.88: Public space production throughout the building plos.

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Social Spaces Optimisation

Table*4.12: Comparison of iteration results.

94!Collective Ecology

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Experiment 3: Optimisation of Public Spaces With the starting point and plot orientation established, further refinement of the aggregation system focuses on the public spaces being produced between the plots. Through re-evaluation of the fittest iteration in ‘Experiment 2’, it is altered by removing open areas that fail to meet a minimum size established for sufficient use as gathering points. This altered iteration is tested in comparison to the original, in order to reveal the most suitable system for developing well dispersed, and properly sized interstitial public spaces throughout the aggregation of plots within the boundary area. To identify the most successful solutions, the evaluation method is refined to provide both a collective assessment of each iteration, and to develop a ranking system between their individual populations. Evaluation Refinement: The evaluation method used in previous experiments permitted selection of the fittest individual based on an average of the values found per iteration. However, each of the fitness criteria contained vastly different value ranges, prohibiting the system to establish a cumulative value, limiting accurate analysis of the behaviour of each individual population in comparison to another. The data collected and analysed for ‘Experiment 3’ will have the values remapped to a value within a uniform domain to address this limitation. Remapping will allow for more

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clear comparison between evaluation data results, and ensure equally weighted values for multiple evaluation criteria. Without remapping the data, priority will be given to evaluation criteria that have data values with higher numerical ranges, inaccurately ranking its importance within the overall fitness of individuals of an iteration. Remapping is conducted by identifying the range of data values found in individuals of an iteration, and re-assigning the value of the highest ranked individual. The remaining individuals are evaluated as a ratio of this individual, and are assigned new values within the range of 0.00-1.00, or in situations where a lower value is more beneficial, its values range from -1.00-0.00. This process subsequently gives all Average Comparison (Remapped) Experiment 3 Fitness Criteria

Exp. 2 Iteration 2

Exp. 3 Iteration 1

Coverage Ratio

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0.98

Porosity Ratio

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Proximity Average for Public Space

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Frequency Ratio for Public Space

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0.80

Total Evaluation

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1.88

Highlighted Value

Top 1


Experiment 2 Iteration 2 (Remapped values and ranking) Fitness Criteria

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Proximity Average for Public Space

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Total Evaluation

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1.79

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Ranking

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Proximity Average for Public Space

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Total Evaluation

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5

9

data in the experiment the same range from which to be evaluated, and allows for the fittest individual population and collective iteration to be selected through summation of the total value of all the aspects assessed. Parameters: -Plots Size: Randomly range between 100-400 sqm (Extracted from Kerman study) -Boundary Area: Quadrilateral (13,080 sqm) -Plot Quantity: 50 units -Starting Point for Aggregation: Centred along the longest edge -Plot Orientations: Orientated parallel to longest edge of boundary condition Variables: -Refinement of Public Spaces Observations: Through removal of open areas that are below a minimum established size for use as public spaces ( 1m), the ‘Coverage Ratio’ and ‘Porosity Ratio’ have very little change in value, showing only slight improvement. The ‘Proximity Average for Public Space’ and ‘Frequency Ratio for Public Space’ show mixed results, with some population values demonstrating improvement, while others fare worse. The Total Evaluation value of Population 2 in

Table*4.13: Comparison of the most fit opulations within iteration of Experiment 2.

Table*4.14: Comparison of the most fit opulations within iteration of Experiment 3.

‘Experiment 2/Iteration 2’ reveals that it ranks the highest of all populations tested. When comparing the cumulative averages of the Total Evaluation values in both iterations, ‘Experiment 3/ Iteration 1’ demonstrates a slightly better average value, ranking it as the fittest iteration overall. (Table 4.12) Analysis: The method utilized to create ‘Experiment 3/Iteration 1’ minimized the irregular and impractical interstitial open areas throughout the plot aggregation, and provided more accurate measurements of the ‘Proximity Averages for Public Space’ in the experiment. This increased precision and reduction of unsuitable public spaces revealed that the collective performance of the populations within ‘Experiment 3/Iteration 1’ outweighed the higher individual performance of some of the populations within ‘Experiment 2/Iteration 2’ and is the most suitable system for developing well dispersed, and properly sized public spaces in our urban system. ‘Experiment 3’ included 2 Iterations, each iteration containing 10 populations. Full results can be referenced in the Appendix (Appendix/Experiments/Plot Distribution Strategies/Experiment 3)

!Experiments!95


Ranking nยบ 1

Ranking nยบ 2

Experiment 3 / Population 2

Figure*4.89: Individiuals ranked

CR: 0.99

PR: 0.99

PA: -0.87

FR: 0.86

Experiment 3 / Population 1

Total Evaluation: 1.96

CR: 0.99

PR: 0.95

Ranking nยบ 3

PR: 0.98

PA: -1.00

FR: 1.00

FR: 0.96

Total Evaluation: 1.96

Ranking nยบ 4

Experiment 3 / Population 6

CR: 0.98

PA: -0.94

Experiment 3 / Population 5

Total Evaluation: 1.96

CR: 1.00

PR: 1.00

Plot Distribution Overall Conclusions: Conducting these multiple experiments helped successfully establish a system for developing compact aggregations of building plots, with well-connected emergent open public spaces between them. Through establishing multiple evaluation criteria, the system takes into account several objectives to drive the dispersal of geometries and their resultant interstitial spaces throughout the parcel, developing a system with similar characteristics to those found within the case studies. This system will be utilised throughout the site on each parcel, and further evaluated to develop optimised networks throughout their organizations.

PA: -0.82

FR: 0.75

Total Evaluation: 1.92

Ranking nยบ 5

Experiment 3 / Population 8

The five fittest individuals of the last study are selected to continue with the next set of experiments in order to develop the connecting network.

CR: 0.99

96!Collective Ecology

PR: 0.99

PA: -0.79

FR: 0.72

Total Evaluation: 1.92


!Experiments!97


Network Strategies Developing the connecting network Nodes (N)

Network Experiments Figure*4.90: Completed evaluation of the network within a parcel. Outcome Experiment 2

Outcome Experiment 3

Maximise Network and Public Space Nodes Differential Weighting: 1.0

Outcome Experiment 1

Defining the connecting network

Overview Several approaches are explored within the context of the five fittest individuals from the previous experiment (Reference Chapter: 4 Experiments/Plot Packing Strategies) to generate the minimum network required for fully connected parcels throughout the site, and to produce appropriate transitions through the multiple levels of privacy found in Arab culture. The resulting configurations will then be measured and assessed through several evaluation criteria, to establish the best strategy for network dispersal throughout the building plots and intestinal public spaces of the urban system. Evaluation Criteria Data results will be remapped to a value within a set domain to allow for a clearer comparison of populations across multiple evaluation criteria. Remapping is conducted by identifying the range of data values found within an evaluation criterion, and re-assigning the value of the highest ranked individual to a new domain limit. The remaining individuals within this evaluation criterion are remapped accordingly as a ratio of this individual, and are assigned new values within the range of 0.00-1.00, or in situations where a lower value is more beneficial, its values range from -1.00-0.00. This process subsequently gives all data in the experiment the same range from which to be evaluated, allowing for differential weighting to be applied 98!Collective Ecology

among the multiple evaluation criteria. It is conducted by multiplying a value designated by the user with the values of an evaluation criterion, providing the user a metaheuristic approach to defining differential levels of importance among multiple conflicting objectives in an experiment. Remapping data values from the refined nodes allows for better evaluation of the weighted criteria within each experiment, however, when analysing results in relation other experiments, the remapped values cannot be properly compared. Examination of their original values (Appendix/ Experiments/Network Development Strategies) is required to appropriately analyse value differentials across multiple experiments.


36

x

x

x

x

28

x

x 27

25

Length (x)

x

Minimise Network Road Segments Differential Weighting: 1.0

x

Minimse Length of Longest Linear Connection Differential Weighting: 1.5

Average Comparison

x

x

x

x

x

Length (x)

10 26

x

x x

x

Maximise Cumulative Length of Network Differential Weighting: 2.0

Average Comparison (Remapped Values)

Fitness Criteria

Experiment 1

Experiment 2

Experiment 3

Fitness Criteria

Experiment 1

Experiment 2

Experiment 3

Nodes (N)

115.00

59.00

59.40

Nodes (N)

1.00

0.51

0.52

Road Segments (RS)

53.80

29.20

29.20

Road Segments (RS)

-1.00

-0.54

-0.54

Connection Length 37.60 (CL)

26.50

33.60

Connection Length -2.00 (CL)

-1.41

-1.79

Network Length (NL)

618.78

668.13

Network Length (NL)

1.50

0.62

0.67

Total Evaluation

-0.50

-0.82

-1.15

Ranking

1

2

3

Highlighted Value

Top 1

1499.50

Figure*4.91: Evaluation Criteria for development of the network.

x

31

x

x

4 32

9

x

6

22 16

11 19

x

3

x

x

x

21 18

x 2 30

13

x

x

x

x

34

17

x

23

29

12

x

38

20

x

x

0 24

x

14

35 7

1

x

8

33

x

39

5

Network Length (NL)

x

37 15

Connection Length (CL)

x

Roads Segments (RS)

Table*4.15: Average Comparison results. Table*4.16: Remapped values.

Nodes (N) (Fig. 4.91) -Quantity of Nodes (units) -Differential Weighting: 1.00(x) -Ambition: Maximise the number of nodes throughout the parcel, located at intersections of network segments and public spaces, minimising opportunities for longer segments throughout the network to increase privacy. Road Segments (RS) (Fig. 4.91) -Quantity of Road Segments (units) -Differential Weighting: 1.00(x) -Ambition: Minimise the number of segments throughout the network, reducing redundancies in the network while ensuring full connectivity. Connection Length (CL) (Fig. 4.91) -Length (m) -Differential Weighting: 1.50(x) -Ambition: Minimise the length of the longest linear connection between nodes to increase areas of privacy. Network Length (NL) (Fig. 4.91) - Length (m) -Differential Weighting: 2.00(x) -Ambition: Maximise the cumulative length of the network for the development of larger areas of semi-private space throughout the parcel. !Experiments!99


Cannecting Logic Figure*4.92: Network Process

Connecting Path

Base Grid

Minimum Spanning Tree of connected points

All possible roads

Connecting Path + Base Grid

Resulting Network

Topological relationship to define the network

Connecting network defined by MST

Possible Connections

MST

Connecting all the shared nodes

Table*4.17: Experiment Evaluation

Experiment 1 This first investigation will explore development of a method to fully connect all the nodes in a parcel with the minimum possible cumulative length of the network. Nodes will be extracted from all building plot intersections, the centre of any open public space, and at the intersection of any building plot with the boundary condition of the parcel. The set of nodes established within the parcel will be connected with one another in all possible configurations and evaluated to establish its Minimum Spanning Tree. (Reference: Chapter 3/Methods/Graph Theory/Minimum Spanning Tree) This will reveal a refined topological graph, representing the shortest configuration for connecting the parcelâ&#x20AC;&#x2122;s node set without creating any closed loops within the path. The optimised topological graph will be overlaid on the base grid of the network to establish the minimal network required to fully connect all nodes throughout the parcel. Variables: -Nodes: Extracted from all building plot intersections within the site and along the border condition -Connecting Logic: Connection between all nodes throughout the parcel -Connecting Path: Minimum Spanning Tree -Base Grid to Evaluate: The edge components of all building plots within the boundary condition of the parcel

100!Collective Ecology

Experiment 1 Fitness Criteria

Population 1

Population 2

Population 5

Population 6

Population 8

Nodes (N)

98.00

124.00

130.00

106.00

117.00

Road Segments (RS)

51.00

59.00

61.00

47.00

51.00

Connection Length 44.00 (CL)

43.00

33.00

38.00

30.00

Network Length (NL)

1426.10

1655.79

1469.85

1632.66

1313.10


Experiment 1 / Population 1

Experiment 1 / Population 2

Experiment 1 / Population 5 Figure*4.93: Connecting Logic and Shortest Walk

N: 0.75

RS: -0.84

CL: -2.00

NL: 1.19

N: 0.95

NR: -0.97

CL: -1.95

NL: 1.29

N: 1.00

Experiment 1 / Population 6

N: 0.82

NR: -0.77

CL: -1.73

NR: -1.00

CL: -1.50

NL: 1.50

Experiment 1 / Population 8

NL: 1.33

N: 0.90

NR: -0.84

CL: -1.36

NL: 1.48

Observations: Development of a network to connect all the nodes throughout the site established mixed results across the multiple fitness criteria examined. All the tested criterion reveal high values, representing positive results for those which reward maximised value outputs such as the ‘Nodes’ and ‘Network Length’ criteria. However, criteria that reward minimum value outputs, including ‘Road Segments’ and ‘Connection Length’, demonstrate negative results that would not efficiently drive the network. (Table 4.18) Analysis: The resulting outcome provided the minimum length possible to connect all nodes within the network; however, a majority of individuals and clusters of building plots demonstrate multiple redundant connections within the network. This lack of refinement decreases the ability to offer more defined paths of transition from one hierarchical level of privacy to another throughout the parcel, as found in the Kerman case study. (Reference: Chapter 3/Methods/ Case Studies/Kerman) Reduction of redundant connections in the network can be explored further through explorations into the nature of the node connections at the boundary condition and through emphasis of node connections between open public spaces, which demonstrate high levels of connectivity to building plots.

Experiment 1 (Remapped Values) Fitness Criteria

Population 1

Population 2

Population 5

Population 6

Population 8

Nodes (N)

0.75

0.95

1.00

0.82

0.90

Road Segments (RS)

-0.84

-0.97

-1.00

-0.77

-0.84

Connection Length -2.00 (CL)

-1.95

-1.50

-1.73

-1.36

Network Length (NL)

1.19

1.29

1.50

1.33

1.48

Total Evaluation

-0.89

-0.68

0.00

-0.35

0.18

Ranking

5

4

2

3

1

Highlighted Value

Top 1

Top50%

Table*4.18: Remapped evaluation

!Experiments!101


Cannecting Logic Figure*4.94: Network Development

Starting Points

Connecting Path

Base Grid

Minimum Spanning Tree of connected points

All possible roads

Connecting Path + Base Grid

Resulting Network

Topological relationship to define the network

Shortest Walk following the Connecting Path

Possible Connections

Social Spaces

Connecting the starting points and the social spaces

Experiment 2 Expanding from â&#x20AC;&#x2DC;Experiment 1â&#x20AC;&#x2122;, development of a network is explored through utilisation of more refined criteria for extracting and connecting nodes from the parcel. Focusing exclusively on nodes extracted from the centre of open public spaces, and from eight boundary condition points, a Minimum Spanning Tree is developed through evaluation of all possible node configurations. The connections of the resulting topological path are then analysed with consideration of the base grid to develop the Shortest Path (Reference: Chapter 3/Methods/Graph Theory/Shortest Walk) required to connect all nodes throughout the parcel. The reduced amount of initial nodes will help develop a fully spanning network with less redundant connections, offering more defined transitions through the multiple levels of privacy. Table*4.19: Experiment Evaluation

102!Collective Ecology

Variables: -Nodes: Extracted from the end and mid-points of each edge of the parcel, and the centre points of the open public spaces. -Connecting Logic: Initiated from nodes extracted from the boundary condition, connections are made with the nearest nodes extracted from the open public spaces, and proceed to connect with all other nodes extracted from the open public spaces on the site.

-Connecting Path: Minimum Spanning Tree through extracted nodes -Base Grid to Evaluate: The edge components of all building plots within the boundary condition of the parcel

Experiment 2 Fitness Criteria

Population 1

Population 2

Population 5

Population 6

Population 8

Nodes (N)

54.00

60.00

62.00

56.00

63.00

Road Segments (RS)

29.00

37.00

28.00

27.00

25.00

Connection Length 36.00 (CL)

21.00

19.50

26.00

30.00

Network Length (NL)

636.12

635.47

602.41

619.95

599.95


Experiment 2 / Population 1

Experiment 2 / Population 2

Experiment 2 / Population 5 Figure*4.95: Connecting Logic and Shortest Walk

N: 0.86

RS:-0.78

CL: -2.00

NL: 1.41

N: 0.95

RS: -1.00

CL: -1.17

NL: 1.50

N: 0.98

Experiment 2 / Population 6

N: 0.89

RS: -0.73

Observations: Evaluation of the resulting values demonstrates a decline in the quantity of nodes and overall length of the network, an outcome expected due to the refinement of nodes extracted for use in developing the network. With nearly half the quantity of nodes extracted as in ‘Experiment 1’, the quantity of ‘Road Segments’ and the maximum ‘Connection Length’ values both improved. (Table 4.20) Focusing on the cumulative result among all four evaluation criteria in the experiment, the resulting average values scored lower than ‘Experiment 1’, however, it can be seen in the Resulting Network (Fig 4.95) that the organisational outcome has less redundancy throughout the network, while still maintaining full connectivity throughout the site. Analysis: Through altering the Nodes to be used and adjusting their Connecting Logic (Fig 4.94), a much more concise, and direct network is generated. The reduced nodes around the boarder condition of the parcel allow for a well distributed set of entry points into the site, while maintaining a threshold that transitions from areas outside the boundary of the parcel into the more remote and private network of the parcel. The results developed through this organisational method reduce redundancy while maintaining a well distributed network throughout the

CL: -1.44

RS: -0.76

CL: -1.08

NL: 1.50

Experiment 2 / Population 8

NL: 1.42

N: 1.00

RS: -0.68

CL: -1.67

NL: 1.46

parcel. Transitions through the multiple levels of privacy are successfully achieved through a network focused on connecting open public spaces in a non-linear, yet most direct fashion, as seen in the traditional case studies.

Experiment 2 (Remapped Values) Fitness Criteria

Population 1

Population 2

Population 5

Population 6

Population 8

Nodes (N)

0.86

0.95

0.98

0.89

1.00

Road Segments (RS)

-0.78

-1.00

-0.76

-0.73

-0.68

Connection Length -2.00 (CL)

-1.17

-1.08

-1.44

-1.67

Network Length (NL)

1.41

1.50

1.50

1.42

1.46

Total Evaluation

-0.51

0.29

0.64

0.14

0.12

Ranking

5

2

1

3

4

Highlighted Value

Top 1

Top50%

Table*4.20: Remapped Values

!Experiments!103


Cannecting Logic Figure*4.96: Network Development

Starting Points

Connecting Path

Base Grid

Minimum Spanning Tree of connected points

Resulting network from Experiment 1 (MST)

Connecting Path + Exp. 1 Base Grid

Resulting Network

Topological relationship to define the network

Shortest Walk following the Connecting Path

Possible Connections

Social Spaces

Connecting the starting points and the social spaces

Experiment 3 Continuing from ‘Experiment 2’, the same methodology will be explored, however the base grid utilised to run the Shortest Walk algorithm will be the resultant network developed from the Minimum Spanning Tree in ‘Experiment 1’. This will investigate networks driven by the placement of the refined topological graph within the context of the minimal network connecting all nodes within the parcel. Integrating the refined network from ‘Experiment 1’ will explore the development of a fully spanning network within the context of a base grid that has optimised connections to all nodes within the parcel. This step will attempt to further optimise connections and better define privacy transitions throughout the network.

Table*4.21: Experiment Evaluation

104!Collective Ecology

Variables: -Nodes: Extracted from the end and mid-points of each edge of the parcel, and the centre points of the open public spaces. -Connecting Logic: Initiated from nodes extracted from the boundary condition, connections are made with the nearest nodes extracted from the open public spaces, and proceed to connect with all other nodes extracted from the open public spaces on the site. -Connecting Path: Minimum Spanning Tree through extracted nodes

-Base Grid to Evaluate: The resulting network developed from the Minimum Spanning Tree in ‘Experiment 1’

Experiment 3 Fitness Criteria

Population 1

Population 2

Population 5

Population 6

Population 8

Nodes (N)

54.00

60.00

64.00

56.00

63.00

Road Segments (RS)

29.00

32.00

30.00

29.00

26.00

Connection Length 36.00 (CL)

27.00

31.00

44.00

30.00

Network Length (NL)

658.86

680.02

675.97

667.12

658.67


Experiment 3 / Population 1

Experiment 3 / Population 2

Experiment 3 / Population 5 Figure*4.97: Connecting Logic and Shortest Walk

N: 0.84

RS: -0.91

CL: -1.64

NL: 1.45

N: 0.94

RS: -1.00

CL: -1.23

NL: 1.45

N: 1.00

Experiment 3 / Population 6

N: 0.88

RS: -0.91

CL: -2.00

RS: -0.94

CL: -1.41

NL: 1.50

Experiment 3 / Population 8

NL: 1.49

N: 0.98

RS: -0.81

CL: -1.36

NL: 1.47

Observations: Examining the tested criterion reveals values similar to those found in ‘Experiment 2’, with the quantity of Nodes and Road Segments averaging nearly the exact same values. Results deviated however when examining the lengths of the longest connecting paths, demonstrating an average increase throughout the tested networks, negatively impacting their Total Evaluation value. The average Network Length also increased, however, positively impacting the Total Evaluation value. (Table 4.22) Analysis: Utilising the network developed from the Minimum Spanning Tree in ‘Experiment 1’ as the base grid, the resulting path developed from the Shortest Walk through the patches demonstrated similar characteristics as found in ‘Experiment 2’. Limiting the paths accessible for the network to pass through forced more in-direct connections and successfully improved the average cumulative length of the network. This also however forced routes of the Shortest Walk algorithm that may not be the optimised condition if all of the base grid paths were available, causing the average Connection Length to increase. Although the Network Length shows larger average values, the increased average values of the Connection Length produces longer linear corridors throughout the parcel, affecting the development of privacy hierarchies throughout the site.

Experiment 3 (Remapped Values) Fitness Criteria

Population 1

Population 2

Population 5

Population 6

Population 8

Nodes (N)

0.84

0.94

1.00

0.88

0.98

Road Segments (RS)

-0.91

-1.00

-0.94

-0.91

-0.81

Connection Length -1.64 (CL)

-1.23

-1.41

-2.00

-1.36

Network Length (NL)

1.45

1.45

1.50

1.49

1.47

Total Evaluation

-0.25

0.16

0.15

-0.54

0.28

Ranking

4

2

3

5

1

Highlighted Value

Top 1

Top50%

Table*4.22: Remapped Values

!Experiments!105


Ranking nº 1

Ranking nº 1

Experiment 2 / Population 5

Experiment 3 / Population 8

Figure*4.98: Network Development

CR: 0.98

PR: -0.76

PA: -1.08

FR: 1.50

Total Evaluation: 0.64

Network Strategies Overall Conclusion: Exploration into these multiple methods of network generation and refinement has led to the development of a system capable of fully connecting the parcel while successfully corresponding to the hierarchies of privacy important in Arab culture. Driven by the characteristics and strategies found within traditional architectures of the region, the tessellated networks that were developed had variable success, and were comparatively analysed to help choose a system to utilise within the parcels of our site. ‘Experiment One’ successfully created a network to provide the minimum length possible to connect all possible intersections throughout the parcel and at its boarder condition. This approach revealed multiple redundant connections within the network, which impeded the development of privacy hierarchies throughout the site. ‘Experiment Two’ successfully addressed this issue with a refined network of nodes and connection conditions at the boarder of the parcel. It maintained connectivity to all building plots throughout the parcel, and created meandering branches of the network throughout the parcel to establish multiple interstitial spaces and hierarchies of privacy. ‘Experiment Three’ combined these two strategies with the goad of utilising the Minimal Network of ‘Experiment One’ with the Node refinement and Shortest Path procedure of ‘Experiment Two’ to develop a further developed system for 106!Collective Ecology

CR: 0.98

PR: -0.81

PA: -1.36

FR: 1.47

Total Evaluation: 0.28

developing the network. This approach, however, did not demonstrate an improvement in its resulting values. Comparing the remapped values of the three experiments together (Table 4.16) prompts a critical evaluation of the data results, requiring concurrent visual analysis of the graphs and resulting networks to help choose the most efficient network system. Although the Average Comparison chart rates ‘Experiment 1’ as the top ranking network, the impact of its Network Length to its cumulative Total Evaluation was so great that it skewed the ability to compare all the experiments together. The data values suggest that it is most desirable for achieving networks appropriate for our desired results, however, it did not take into consideration redundancies in the graph and network, which when analysed reveal that this approach is not appropriate for our system. Given this consideration, ‘Experiment Two’ is clearly a more efficient and acceptable system, with ‘Experiment Three’ also showing more favourable results. Both will be considered and evaluated further throughout our subsequent design proposal to further test the results of each and choose a final approach for our network development system.


!Experiments!107


Building Morphologies Incorporating natural water treatment systems and considering social, cultural and climatic aspects. One Household

Figure*4.99: Initial state of wetland impact considered for the experiments, One house hold

10m

10m

12m

3 Persons Household size: 100m2 Wetland per person: 40m2 Wetland per household: 120m2

Wetlands Impact Overview Through investigating the necessary requirements of localised natural water treatment processes throughout the system, several implementation methodologies will explore the potential viabilities of the system. Parameters -Average household size: 3 -Constructed wetland required per capita: 40 sqm -Constructed wetlands required per household: 120 sqm Experiment 1 Exploring the relationships between built area and constructed wetlands will develop an understanding of the impacts natural water treatment systems have at ground level and within buildings as they expand vertically. Wetland impacts are initially explored through their placement next to and on top of the built areas, identifying the spatial requirements and treatment possibilities of a single unit. (Fig. 4.100) To enable increases in density, integration of constructed wetlands within clusters of built areas explores the possibilities of vertical stacking and patterning of multiple household units. (Fig. 4.101) This experiment will formulate the spatial requirements necessary for constructed wetlands per household unit, and will establish the ratios for necessary wetland requirements in relation to household units. Impact of Wetland on Ground: One household is initially explored through placement of a built unit coupled with the necessary constructed wetlands required to sustain it at the ground level. As household units begin to be added in the Z-axis, the wetland area 108!Collective Ecology

increases in the X and Y axes expanding outwards at the ground level. (Fig. 4.100) Impact of Wetland on Roof: Expanding the surface area of the roof is explored as a viable option to accommodate the space necessary for wetlands. As the number of household units increase in the z-axis, the roof area expands its coverage to accommodate the treatment needs of the households. (Fig. 4.100) Impact of Coupling Wetlands and Households: Through clustering units in a three by three aggregation, eight household units surround a centre core to establish a base. As additional floors are added, household units are removed and replaced by constructed wetland units as required to sustain the density and treatment production of the building. (Fig. 4.101) Analysis Through integration of wetlands located at the ground level or through roof surfaces, vertical stacking is constrained to handle a maximum of five household units before reaching its water treatment capacities, restricting density levels to only 15 people per 700 sqm. Although not architecturally viable for a highly dense urban system, these two options establish ratios between the building morphology and the constructed wetlands. Through coupling household units and water treatment units as ratios of one another as they expand vertically, the possibility arises for their relationships to be weighted for either higher densities levels or higher levels of water treatment. With this variability, a wide range of building options becomes available, based on the desired density levels or amount water treatment necessary to sustain of the system.


Impact of Wetlands On the Ground

Impact of Wetlands On the Roof

15 Persons Number of households: 5 Number of Floors: 5 Wetland required: 600m2 z x

15 Persons Number of households: 5 Number of Floors: 5 Wetland required: 600m2 z

y

Initial State

x

Figure*4.100: Wetland impact on ground and roof.

y

One Floor

24 Persons Number of households: 8 Wetland required: 960m2 Actual Wetland: 900m2 Spare Wetland: -60m2

21 Persons Number of households: 7 Wetland required: 840m2 Actual Wetland: 1000m2 Spare Wetland: 160m2

Figure*4.101: Integration of wetlands within a building

Three Floors

Two Floors

48 Persons Number of households: 15 Wetland required: 1800m2 Actual Wetland: 1800m2 Spare Wetland: 0m2

33 Persons Number of households: 11 Wetland required: 1320m2 Actual Wetland: 1400m2 Spare Wetland: 80m2

Four Floors

Five Floors

57 Persons Number of households: 19 Wetland required: 2280m2 Actual Wetland: 2200m2 Spare Wetland: -80m2

69 Persons Number of households: 23 Wetland required: 2640m2 Actual Wetland: 2700m2 Spare Wetland: 60m2

!Experiments!109


Integration of wetlands within the building

Initial State Case 016

Figure*4.102: Resulting buildings considering the integration of wetlands within buildings using computational methods

Second Generation Case 016_Rule 011

Figure*4.103: Example of an Initial state and next 2 generation applied

Build Volume: 29700.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 79 People: 237 Wetland surface area needed: 9480 m2 Actual wetland in the building: 8100.0 m2 Wetland difference: 1380.0 m2

Third Generation Case 016_Rule 011

Rule 011

Experiment 2 Continued from Experiment 1, the logic of constructed wetland integration within the building is further explored through a systematic approach to their organisation. Utilisation of cellular automaton will establish ratios between built (households units) and unbuilt (wetland units) cells through rules for patterning their aggregation. The rule set to drive a cellular automaton can be read from the bottom three cells, comprised of the configuration of states for the centre cell, and its two immediate neighbour cells. There are 23 = 8 possible configurations a cell can take in relation to its neighbours. The fourth cell above this configuration specifies the state the centre cell will take in the next generation, resulting in 28 = 256 possible cellular automata rules. Each time the experiment is ran it starts in one of a possible 28 = 256 initial states, with each subsequent generation stacked on top of the previous. (Fig. 4.103 Each of these 256 initial states were tested with each set of 256 cellular automata rules. The resulting 65,536 possible aggregation outcomes were evaluated and graphed to develop a catalogue of potential building options. A small percentage (0.037%) of which are represented from the results of â&#x20AC;&#x2DC;Initial State 16â&#x20AC;&#x2122; on the following pages. (Fig. 4.110) 110!Collective Ecology

Solar exposure levels of typical aggregation configurations were then investigated to explore the amount of light penetration within the areas reserved for wetlands. These studies are tested within multiple configurations and throughout the seasons of the year in the following pages. Qualities Measured -Ratios of wetland area/built area -Production of water -Density -Solar exposure


Twentieth Generation

Flat Cellular Automata Figure*4.104: Twentieth Generation:

Figure*4.105: Flat Cellular Automaton:

Throughout the experiments generations were kept constant at 20 generations to represent a mid-high rise building.

Building Core

Seen as a sequence of generations from top to bottom, CA’s can begin to develop patterns based on their CA rule

The Facade Wraps Around the Core. Figure*4.106: Building Core:

Figure*4.107: The facades wrap around the core:

To ensure viability of the design, each tower includes a consistent ‘Building Core’ allowing for basic elements such as stairwells and utilities to be included which are necessary for a functional tower.

The Facade Wraps Around the Core.

Transitioning from the 2D structure of wetlands and households into a 3D tower is achieved by wrapping its morphology around the building core until both ends connect together.

The Facade Wraps Around the Core. Figure*4.108: The facades wrap around the core.

Figure*4.109: The facades wrap around the core.

!Experiments!111


Experiments Sample

Figure*4.110: Some of the resulting buildings considering the integration of wetlands..

Rule 000 Build Volume: 6300.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 1 People: 3 Wetland surface area needed: 120 m2 Actual wetland in the building: 15900.0 m2 Wetland difference: -15780.0 m2

Rule 007 Build Volume: 30000.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 80 People: 240 Wetland surface area needed: 9600 m2 Actual wetland in the building: 8000.0 m2 Wetland difference: 1600.0 m2

Rule 013 Build Volume: 29100.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 77 People: 231 Wetland surface area needed: 9240 m2 Actual wetland in the building: 8300.0 m2 Wetland difference: 940.0 m2

Rule 037 Build Volume: 24300.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 61 People: 183 Wetland surface area needed: 7320 m2 Actual wetland in the building: 9900.0 m2 Wetland difference: -2580.0 m2

112!Collective Ecology

Rule 006 Build Volume: 15000.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 30 People: 90 Wetland surface area needed: 3600 m2 Actual wetland in the building: 13000.0 m2 Wetland difference: -9400.0 m2

Rule 011 Build Volume: 29700.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 79 People: 237 Wetland surface area needed: 9480 m2 Actual wetland in the building: 8100.0 m2 Wetland difference: 1380.0 m2

Rule 014 Build Volume: 17700.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 39 People: 117 Wetland surface area needed: 4680 m2 Actual wetland in the building: 12100.0 m2 Wetland difference: -7420.0 m2

Rule 058 Build Volume: 28200.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 74 People: 222 Wetland surface area needed: 8880 m2 Actual wetland in the building: 8600.0 m2 Wetland difference: 280.0 m2


Rule 062 Build Volume: 33600.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 92 People: 276 Wetland surface area needed: 11040 m2 Actual wetland in the building: 6800.0 m2 Wetland difference: 4240.0 m2

Rule 087 Build Volume: 30300.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 81 People: 243 Wetland surface area needed: 9720 m2 Actual wetland in the building: 7900.0 m2 Wetland difference: 1820.0 m2

Rule 101 Build Volume: 30000.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 80 People: 240 Wetland surface area needed: 9600 m2 Actual wetland in the building: 8000.0 m2 Wetland difference: 1600.0 m2

Rule 155 Build Volume: 43500.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 125 People: 375 Wetland surface area needed: 15000 m2 Actual wetland in the building: 3500.0 m2 Wetland difference: 11500.0 m2

Rule 075 Build Volume: 29100.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 77 People: 231 Wetland surface area needed: 9240 m2 Actual wetland in the building: 8300.0 m2 Wetland difference: 940.0 m2

Rule 094 Build Volume: 29400.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 78 People: 234 Wetland surface area needed: 9360 m2 Actual wetland in the building: 8200.0 m2 Wetland difference: 1160.0 m2

Rule 124 Build Volume: 31500.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 85 People: 255 Wetland surface area needed: 10200 m2 Actual wetland in the building: 7500.0 m2 Wetland difference: 2700.0 m2

Rule 161 Build Volume: 25800.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 66 People: 198 Wetland surface area needed: 7920 m2 Actual wetland in the building: 9400.0 m2 Wetland difference: -1480.0 m2

!Experiments!113


Sun Exposure Studies Corner Pockets

Autumn

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring Autumn and winter demonstrate high levels of solar access, with the remainder of the year supplying moderate levels of light into the space.

Interior Pockets

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Autumn

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring Interior Pockets drastically reduce the solar levels that penetrate into the building. There is almost no direct sunlight into the north openings. This is the worst position for wetlands out of the tested cases.

114!Collective Ecology

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours


East-West Strips

Autumn

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring East-west strips provide the best scenario for solar access, especially during the winter months, with more than 10 hours of direct sunlight.

North-South Strips

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Autumn

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring Similar to the east-west strips, there is an overall high solar gain that is fairly constant throughout the year, with direct sun exposure between 4 and 8 hours a day.

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

!Experiments!115


Corner Pockets (2 Stories)

Autumn

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring An increase in ceiling height allows for a substantial elevation in solar gain throughout all four seasons.

Interior Pockets (2 Stories)

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Autumn

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring Even though the ceiling height has doubled, its impact is fairly minimal, as this scenario still demonstrates poor sun exposure throughout the majority of the year.

116!Collective Ecology

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours


East-West Strips (2 Stories)

Autumn

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring With extended ceiling height, the eastwest strip continues to demonstrate the best performance out of all the cases, allowing for deep penetration into the openings.

North-South Strips (2 Stories)

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Autumn

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Winter 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Spring Similar to the east-west strips, doubling the ceiling heights allows for increased solar exposure, with near continual exposure levels throughout the year.

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Summer 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

!Experiments!117


Initial State 44 150 135

Initial State 170

Initial State 255

64

90% - 100%

80% - 90%

70% - 80%

60% - 70%

50% - 60%

30 15 40% - 50%

90% - 100%

80% - 90%

70% - 80%

60% - 70%

50% - 60%

40% - 50%

30% - 40%

20% - 30%

10% - 20%

16

64

60 45

30% - 40%

30 15

90 75

20% - 30%

48

120 105

10% - 20%

48

15

148

0% - 10%

90 75

Number of Buildings

150 135

13

90% - 100%

17

80% - 90%

60% - 70%

28

70% - 80%

32

50% - 60%

13

26

40% - 50%

90% - 100%

15

17

Percentage of Wetland Area (m2)

120 105

60 45

30 15

30% - 40%

80% - 90%

70% - 80%

60% - 70%

15

144

0% - 10%

Number of Buildings

18

60 45

Percentage of Wetland Area (m2)

150 135

118!Collective Ecology

23

50% - 60%

40% - 50%

25

30% - 40%

13

23

20% - 30%

14

10% - 20%

30 15

36

31

20% - 30%

58

60 45

80

90 75

10% - 20%

90 75

120 105

0% - 10%

120 105

Number of Buildings

150 135

0% - 10%

Number of Buildings

Figure*4.111: Graphs, analysis of the resulting buildings versus the number of buildings with different initial states.

Initial State 43

Percentage of Wetland Area (m2)

Percentage of Wetland Area (m2)

Analysis: To better analyse the dispersal of buildings throughout the spectrum of results, 256 graphs were developed, each representing an initial start state for the cellular automaton. Within each graph of an initial state, the resulting buildings created from each of the 256 cellular automata rules were plotted. Their dispersals were analysed to present the number of buildings developed within each of the ten percentage ranges of wetland area. There were four common types of dispersals found within the graphs. (Figs. 4.111) ‘Initial State 44’ represents the graphs with a dispersed bell curve, indicating the percentage of wetland was distributed fairly evenly throughout each of the 10 ranges evaluated. ‘Initial State 43’ represents the graphs which had a tighter distribution of buildings located primarily within the centre of the 10 ranges evaluated. ‘Initial State 170’ illustrates graphs with clustered dispersals primarily in mid-range ratios and the two extremes of the graph, with little in-between. Lastly, the graph of ‘Initial State 255’ exhibits graph types with

distribution predominantly in the low level of integration within buildings. Analysis of the solar conditions demonstrated high levels of exposure for wetlands located on the southern portions of the building, with early no direct sunlight exposure within areas on the northern end. The wetlands demonstrated longer hours of exposure during the winter months, due to the reduced angle of the sun penetrating light deeper into the building openings. Opening locations on corners of the building also allowed for additional solar exposure compared to openings in the centre of the building due to access to light penetration coming from the east and west of the building. Doubling the height of the openings improved the solar penetration for all the aggregation organisations.


Rule 222 Build Volume: 39000.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 110 People: 330 Wetland surface area needed: 13200 m2 Actual wetland in the building: 5000.0 m2 Wetland difference: 8200.0 m2

Rule 15 Build Volume: 44100.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 127 People: 381 Wetland surface area needed: 15240 m2 Actual wetland in the building: 3300.0 m2 Wetland difference: 11940.0 m2

Rule 195 Build Volume: 30000.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 80 People: 240 Wetland surface area needed: 9600 m2 Actual wetland in the building: 8000.0 m2 Wetland difference: 1600.0 m2

Conclusion: Through evaluation of the 65,536 buildings created by cellular automaton, the resulting aggregations were analysed and catalogued based on their ratios of built to unbuilt area. Several distinct aggregation categories were identified from these results. On one spectrum of the results, buildings were capable of treating more wastewater than what was necessary to produce to sustain their inhabitants. These buildings have the disadvantage of being low in density, but they have the potential to assist in the water treatment demands of adjacent buildings incapable of fulfilling their own needs. On the opposite spectrum of the buildings analysed, were aggregations comprised of more built cells than unbuilt cells. Those within this spectrum are high in density, but lack the constructed wetlands necessary to support the treatment of waste water to sustain their own needs. Between these two extremes, the remaining buildings demonstrated a capacity of treating just above or below the amount of wastewater they required to bear their own needs.

Rule 246 Build Volume: 44400.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 128 People: 384 Wetland surface area needed: 15360 m2 Actual wetland in the building: 3200.0 m2 Wetland difference: 12160.0 m2

Figure*4.112: Building morphologies selection

Rule 109 Build Volume: 39600.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 112 People: 336 Wetland surface area needed: 13440 m2 Actual wetland in the building: 4800.0 m2 Wetland difference: 8640.0 m2

Rule 188 Build Volume: 30000.0 m3 Volume Envolope: 18000 m3 Number of floors: 20 Houses: 80 People: 240 Wetland surface area needed: 9600 m2 Actual wetland in the building: 8000.0 m2 Wetland difference: 1600.0 m2

The solar exposure studies were essential to establish whether these organisational layouts were viable for wetland growth. Although diffused light also enters into the space, the analysis indicates that due to low light access deep within the building openings, issues may arise with viability of plant growth. Taking also into consideration the probable solar exposure loss due to shading conditions of the neighbouring building morphologies, this system of developing building morphologies was no longer pursued.

!Experiments!119


Case 00 - Single Storey

Case 00 - Single Storey

Case 00 - Single Storey

Figure*4.113: Evaluation of typical single and double storey low-rise buildings. FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

0.51 123.36 8.53 4.44

Case 00 - Double Storey

40 30 20 10 0

Privacy Ratio: 1.00

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

Surface Area (m2)

50

Amount of Sunlight Hours

Case 00 - Double Storey

Case 00 - Dobule Storey

0.98 237.84 8.01 4.44

40 30 20 10 0

Privacy Ratio: 0.77

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours

Courtyard Studies Overview Examination of vernacular low rise buildings will establish a catalogue of the environmental and privacy conditions that result from the organisational relationships of building geometries and private courtyards. The initial experiment explores how placement of the building geometry within the site affects the number of sunlight hours the courtyard area is subject to receive. This solar analysis is then evaluated to map out the optimal placement of plant types based their level of solar tolerance. Each case is then evaluated to measure the level of privacy the courtyards offer within each of organisational layouts. Study 1: Courtyard Sunlight Exposure & Vegetation Integration A series of parcels measuring 15x15 meters were evaluated to examine the environmental conditions resulting from vernacular building geometries and private courtyard spaces as found in the region. Each case is analysed as a single storey and double storey building, establishing ten case conditions to evaluate. Solar exposure levels examine the number of hours of sunlight measured in the courtyard. Privacy Ratios are quantified through measurement of the surfaces exposed at eye level from outside the parcel. The variations in placement, shape, height and orientation of 120!Collective Ecology

the buildings will demonstrate the varying impacts building geometry has on levels of privacy as well as measure the total amount of sunlight hours exposed within the private courtyard. Qualities Measured: -Built Area -Open Surface Area -Solar Exposure Hours -Privacy


Case 01 - Single Storey

Case 01 - Single Storey

Case 01 - Single Storey

0.51 123.36 8.42 3.66

Case 01 - Double Storey

40

Figure*4.114: Evaluation of typical single and double storey low-rise buildings.

30 20 10 0

Privacy Ratio: 1.00

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours

Case 01 - Double Storey

Case 01 - Double Storey

0.97 236.34 7.88 3.63

40 30 20 10 0

Privacy Ratio: 0.75

Observations: -Single Storey Solar Analysis Analysing the organisation of building geometries and courtyard spaces as single stories provides a range of observations that can be abstracted from the data. The placement of building geometry on the site did little to vary the average sunlight hours during the summer months, with most cases averaging over 8 hours of exposure. (Case 00, 01, 02, 03) However, it did affect the average sunlight hours during the winter months more dramatically, with the location of the building geometry in the centre of the site demonstrating the least hours of exposure. (Case 02) The placement of building geometry at the north and south ends of the site exhibit similar solar conditions in both summer and winter due to their large surface areas of open courtyard space. (Case 00, 03) A courtyard space enclosed on at least three sides within the building geometry dramatically reduces the average sunlight hours during the summer months, while still maintaining several hours of solar exposure during the winter months. (Case 01, 04) The corresponding bar graph demonstrates this organisational strategy provides the most evenly dispersed hours of sunlight over the surface area, primarily in a range 3-5 hours, with only a few areas experiencing lower or higher amounts of solar exposure. (Case 04)

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours

-Double Storey Solar Analysis Maintaining the same organisation of building geometries and courtyard spaces, the building heights are increased to double stories and further analysed. All five cases demonstrated increased shadow coverage from the heightened geometries, decreasing their levels of solar exposure within the courtyards. The two most extreme single storey cases continued to characterise the highest levels of average sunlight hours, with only minimal change to the amount of solar exposure their courtyards were subject to. (Case 00, 03) Courtyards enclosed on at least three sides by the building demonstrated dramatic differences in the average sunlight hours in comparison to their accompanying main open courtyard space. (Case 01, 04)

!Experiments!121


Case 02 - Single Storey

Case 02 - Single Storey

Case 02 - Single Storey

Figure*4.115: Evaluation of typical single and double storey low-rise buildings. FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

0.51 123.36 8.04 1.87

Case 02 - Double Storey

40 30 20 10 0

Privacy Ratio: 1.00

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

Surface Area (m2)

50

Amount of Sunlight Hours

Case 02 - Double Storey

Case 02 - Double Storey

0.96 233.16 7.35 1.73

Case 03 - Single Storey

40 30 20 10 0

Privacy Ratio: 0.72

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours

Case 03 - Single Storey

Case 03 - Single Storey

0.45 108.36 8.80 4.53

30 20 10 0

Privacy Ratio: 1.00

Privacy Ratios It can be observed that as single stories, all cases resulted in the highest possible privacy ratio. However, when the cases changed from a single storey to a double storey, their privacy ratios diminished. The geometries with lower amounts of vertical surface area and without inlets or centre open spaces were more exposed and resulted in lower privacy ratios. (Case 02, Case 03) The highest performing double storey case contained a completely enclosed centre space ensuring high levels of privacy. (Case 04)

122!Collective Ecology

40

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours


Case 03 - Double Storey

Case 03 - Double Storey

Case 03 - Double Storey

0.85 206.94 8.45 2.19

Case 04 - Single Storey

40

Figure*4.116: Evaluation of typical single and double storey low-rise buildings.

30 20 10 0

Privacy Ratio: 0.71

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours

Case 04 - Single Storey

Case 04 - Single Storey

0.47 114.36 6.56 2.56

Case 04 - Double Storey

40 30 20 10 0

Privacy Ratio: 1.00

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours

Case 04 - Double Storey

Case 04 - Double Storey

0.89 217.05 5.75 2.09

40 30 20 10 0

Privacy Ratio: 0.80

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

FAR: Built Area: Summer Avg Sunlight Hours: Winter Avg Sunlight Hours:

Surface Area (m2)

50

Amount of Sunlight Hours

!Experiments!123


Case 00 - Single Storey (Entire Year)

Case 00 - Double Storey (Entire Year)

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Figure*4.117: Solar analysis and wetland viabilitiy tests.

N

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Mean hours: 6.56

Case 00 - Single Storey (Entire Year)

N

Standard Deviation: 1.98

Case 01 - Single Storey (Entire Year) >10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Mean hours: 6.37

Case 00 - Double Storey (Entire Year)

Mean hours: 6.13

Case 01 - Single Storey (Entire Year)

>8

>8

>8

5-8

5-8

5-8

<5

<5

<5

Hours

Hours

Hours

N

Standard Deviation: 1.98

Conclusions These case studies, as both single and double storey buildings demonstrate the importance building placement and enclosure has in defining average sunlight hours on courtyard surfaces. Height helps mediate these levels, but only at a minimal amount compared to the impact placement and orientation of building geometry has on solar exposure. This varying level of solar exposure, however, offers the opportunity to explore further the placement of plant species dependent on their ability to handling sunlight. Building geometries also had impacts on privacy ratios, demonstrating that height can diminish privacy by exposing more vertical surface area; however, this loss can be offset with enclosed, highly private centre spaces. Understanding and cataloguing these results will help inform organisational strategies and building geometries within further experiments for the development of our urban system. â&#x20AC;&#x2DC;Study 1â&#x20AC;&#x2122; included 10 cases, each case tested in all cardinal directions, with the most optimal orientations presented in this chapter. Full results can be referenced in the Appendix (Appendix/Experiments/Building Morphologies/Low Rise: Courtyard Studies)

N

Standard Deviation: 2.37

Case 03 - Single Storey (Entire Year) >10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Mean hours: 6.74

Case 03 - Single Storey (Entire Year) >8 5-8

<5 Hours

N 124!Collective Ecology

Standard Deviation: 2.16


Case 01 - Double Storey (Entire Year)

Case 02 - Single Storey (Entire Year)

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Mean hours: 5.87

Case 01 - Double Storey (Entire Year)

N

Mean hours: 4.46

Case 02 - Double Storey (Entire Year)

>8

>8

>8

5-8

5-8

5-8

<5

<5

<5

Hours

Hours

Hours

N

Standard Deviation: 2.61

N

Standard Deviation: 1.57

Case 04 - Single Storey (Entire Year)

Standard Deviation: 1.62

Case 04 - Double Storey (Entire Year)

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Mean hours: 5.43

Case 03 - Double Storey (Entire Year)

Standard Deviation: 2.12

N

Mean hours: 5.05

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Case 02 - Single Storey (Entire Year)

Case 03 - Double Storey (Entire Year)

N

Case 02 - Double Storey (Entire Year)

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Mean hours: 4.63

Case 04 - Single Storey (Entire Year)

Mean hours: 3.98

Case 04 - Double Storey (Entire Year)

>8

>8

>8

5-8

5-8

5-8

<5

<5

<5

Hours

Hours

Hours

N

Standard Deviation: 1.54

N

Standard Deviation: 1.92

!Experiments!125


Design Development Site Cells Public Spaces Urban Wetland Integration Network Development Test Patch Building Morphologies Low Rise Low-Rise Sections High Rise High-Rise Sections Conclusions

p.130 p.132 p.140 p.146 p.150 p.152 p.164 p.166 p.186 p.196 p.216 p.235


Design Development Overview The culmination of the previously explored methods and experiments leads to the development of a design proposal to address the issues presented within Doha, Qatar. Through implantation of these strategies on a chosen site, population densities are dispersed, networks are developed and optimised, and building typologies are positioned to create a system aimed at demonstrating the viability of the urban system.


Site Doha, Qatar Photograph(5.46: Doha skyline Source: <http://www. qia-qatar.com/content/ doha-city-tour>

Photograph(5.47: Contrast between the West-End building morphology (High rises) and the rest of the city. Source: Deep Ghosh <https://www.flickr. com/groups/1599245@ N24/>

The chosen site to test the urban system is located along the Qatari coastline of the Arabian Gulf, allowing for access to seaside winds and a relatively flat and empty patch of desert. Covering an area of 13.62 ksqm, this patch will be designed to support 150,000 people at an average density of 109.8 per hectare, similar to rates found within central Doha. Located midway between two major Qatari cities, Doha and Al Khor, the site is subject to their continued outward expansion, leading to its increasing and density development in the near future.

130!Collective Ecology


Figure&5.118: Aerial view of the site in relation to Doha Source: Google Earth

Site

Photograph&5.48: Aerial view of the site Source: A. Ahmed <http://static. panoramio.com

Figure&5.119: Site with measurements

2.96km

30 km

2.82km 13.62km2

3.98km 0

1

2 km

Doha

!Design Development!131


Cells Cells as containers of information Figure'5.120: 3 different cell types with different ratios and water treatment.

Residential

Mixed

Residential

Mixed 26%

34%

Residential

Mixed 32%

31%

29%

56% Office

10%

43%

Office

Cell A

39%

Office

Cell B

Wetlands

Urban

Cell C

Wetlands

Urban 35%

40%

Wetlands

Urban

22%

65% 78%

60%

Wetland Required

Cell density Population: Water usage per capita: Total water consumption: Wetlands surface area:

100 p/ha 2160 430 L/day 928800 L/day 86400 m2

Wetland Required

Cell density Population: Water usage per capita: Total water consumption: Wetlands surface area:

Cell Types With the site chosen and an overall desired density established, a systematic approach for dispersing population levels is developed, informing the resulting amount of wetland required to sustain them. Within a series of cells types, each cell represents a collection of information in regards to density, ratios of function type, and their consequential water consumption levels. (Fig 5.72) Analysis of tissue samples from Frankfurt, Manhattan and Amsterdam informed the characteristics of each of the cell types. Leading ‘Cell A’ to be primarily residential based, ‘Cell B’ to be mostly office based, and ‘Cell C’ to be comprised of evenly split function types. The distribution of cells on the site will allow for a variation of density levels and wetland dispersal within the urban system.

132!Collective Ecology

162 p/ha 3500 430 L/day 1505000 L/day 140000 m2

Wetland Required

Cell density Population: Water usage per capita: Total water consumption: Wetlands surface area:

55 p/ha 1188 430 L/day 510840 L/day 47520 m2

Growth Strategy The growth strategy begins with the placement of a cell on the site from which to start from. Its cell type is randomly chosen and additional cells begin to connect based on preferential attachment rules. This procedure disperses population densities based levels found in surrounding neighbours, allowing for a gradual transition between areas of low and high density. The probability of the selection of a cell type for placement on the site is controlled through a weighted probability algorithm, adjusting the ratios between cell types on the site in order to achieve an overall desired population level.


Cell A

A B C

Cell B

C A B

Cell C

B A C

Cell A

A B C

Cell B

C A B

Cell C

B A C

Cell A

A B C

Cell B

C A B

Cell C

B A C

Preferrential Attachment Rules

Preferrential Attachment Rules

Figure&5.121: Rules defining preferential attachment

Preferrential Attachment Rules

Figure&5.122: Steps for cells aggregation process.

Step 02

Step 04

Step 06

Step 07

Step 08

Step 09

Step 11

Step 12

Step 13

Step 20

Step 30

Step 40

!Design Development!133


Figure&5.123: Three different starting points for the cell aggregation process.

Study 00

Starting points

)0

Population : 150,916 Number of Cell types Cell A : 28 Cell B : 16 Cell C : 29

)2

)0

)1 Table&5.23: Possible cells distributions, highlighting the three selected ones to run the experiments using different starting points

Population Cell A

Cell B

Cell C

Standard Deviation

149577

29

15

29

6.60

149706

22

18

33

6.34

149815

34

13

26

8.65

149835

15

21

37

9.29

149944

27

16

30

6.02

150053

39

11

23

11.47

150073

20

19

34

6.85

150182

32

14

27

7.59

150202

13

22

38

10.34

150311

25

17

31

5.73

150420

37

12

24

10.21

150440

18

20

35

7.59

150549

30

15

28

6.65

150569

11

23

39

11.47

150678

23

18

32

5.79

150787

35

13

25

8.99

150807

16

21

36

8.50

150916

28

16

29

5.91

151025

40

11

22

11.95

151045

21

19

33

6.18

151154

33

14

26

7.85

151174

14

22

37

9.53

Cell Ratios Each cell covers an area of 21 hectares, permitting a total 73 cells to fit on the site. This allows for a population range from 86,724 up to 255,500 depending on the ratios of the differing cells. All combinations of cell types were calculated and their resulting population levels were assessed to find all arrangements that come within 1% of the target population range. (Table 5.5) Of these, three combinations are further evaluated, two that are closest to the targeted population goal, and one which had the lowest standard deviation between the ratios of cell types throughout the site.

134!Collective Ecology

Study 03 Population : 150,916 Number of Cell types Cell A : 28 Cell B : 16 Cell C : 29

)1

Study 06

Population : 150,916 Number of Cell types Cell A : 28 Cell B : 16 Cell C : 29

)2

Aggregation and Starting Points Three starting points were established to test the behaviour of the aggregation of cells throughout the site. Locating points in the centre, Northwest and Southeast corners, these points examine how the relationship between the starting point and the border condition has an effect in the organisation of cell types. (Fig 5.75) Each of starting positions develops a unique pattern of dispersal, either flowing along the border condition or through the middle of the site. Variations in the ratios of cell types allow for some patters to emerge, with some studies having more defined clustering of types, and others developing a more mixed distribution. (Fig 5.76)


Population : 150,569 Number of Cell types Cell A : 11 Cell B : 23 Cell C : 39

Study 01

Study 02

)0

)0

Population : 150,053 Number of Cell types Cell A : 39 Cell B : 11 Cell C : 29

Study 04

Figure&5.124: Experiments based on the three selected distribution running from three different starting points

Study 05

Population : 150,569 Number of Cell types Cell A : 11 Cell B : 23 Cell C : 39

Population : 150,053 Number of Cell types Cell A : 39 Cell B : 11 Cell C : 29

)1

)1

Study 07

Population : 150,569 Number of Cell types Cell A : 11 Cell B : 23 Cell C : 39

Study 08

)2

Population : 150,053 Number of Cell types Cell A : 39 Cell B : 11 Cell C : 29

)2

!Design Development!135


Example 1 Figure&5.125: Three different examples for clustering

Example 2

!7 )8

)1 )0

)4

)3 )7

)5 )6

#2 #8

!8 @1

$6

$3 $4

%8

^6

%4

%1

^0 ^4

^7

#7

^9

!6

#8 #2

@2 !1

!0

#6 @7

!2 )7

)6 )5

!4

&0

#4

@1

)9

!5 )8 !3

)4 )3

)2 )1

&1

)0

&2

Study 00

Study 01

Study 02

Study 03

Study 04

Study 05

Study 06

Study 07

Study 08

Figure&5.126: Clustering studies

136!Collective Ecology

&0 %0

#1

@0

!7

@9

$4

@6

!8

^9 $9

@8

$1

!9

@3

#9

$2

@4

^8 ^1

$3

@5

#0 #3

%2 %5

%9

%3 $0

$6 $5

%2

%4

#5

%6

%8

%6

%7 ^1

%1 #9

$2

#1 #6

$9

@9

$7

^5 %3

#7

#0 @8

@7 #4

%0

!6

$8

%9

$8 $1

@6

^2

^7 ^0

%7

^4 ^3

^8 ^3

$0 @5

!5

&1

^2 $7

@4

^5

&2

$5 #5

@0

!3

!2 @2

%5

!0

!1

!4

!9 )9

)2

^6

#3 @3


Clustering Cells are coupled together to develop regional clusters that can collectively sustain themselves, permitting autonomy from other clusters in the treatment, storage and reintegration of water. Each cluster allows for the urban systems within them to collectively accommodate fluctuations in population or function within the cells of the cluster. Cells cluster with their neighbours in the order they were placed on the site until they reach a collective population of 7000-9000 people. (Fig. 5.77) In most studies, a range of 3-8 cells are required to meet this population level within a cluster.(Fig 5.78) Clusters with the most similar amounts of cells within them also have comparable surface areas. (Fig.5.79) Analysis of the clustering studies shows that Study 05 demonstrates the most similarity in cluster types, which will maintain density variations, allowing it to develop a heterogeneous urban landscape.

Example 3

^2

#8 #4 $1

!5 )7

$2

)3 )0

)9

$3 %7

)6

@5

^1

!9

$0 %4 %3

^7 ^6

@7 #3

#0 #1 %5 %1 %2

#6 %6

$9

&0 $6

@2 #2

%0

^9 #9

!7 !4

#5

#7

!2

)5

&1 ^5

@9

)4

@0

$4

@8 !1

)2

^4 $8

@6 !0

)8 )1

@4

^8

!3

^3 $7

!8

@3 @1

$5

!6

%8 %9

^0 &2

Study 00

Study 01

10 8 6 4 2 0

1

2

3

4

5

6

7

8

10 8 6 4 2 0

9

12

Number of Clusters

12

Number of Clusters

Number of Clusters

12

Number of clustered cells

1

2

6

7

8

8 6 4 2 1

2

3

4

5

6

7

8

6 4 2 1

2

3

4

5

6

7

8

Number of clustered cells

9

6 4 2 1

2

3

4

5

6

7

8

3

4

5

6

7

8

9

10 8 6 4 2 0

9

1

2

3

4

5

6

7

8

Number of clustered cells

Number of clustered cells

Study 07

Study 08

9

12

10 8 6 4 2 0

2

12

Number of Clusters

Number of Clusters

8

1

Study 05

12

10

2

Study 04

Study 06 12

6 4

Number of clustered cells

8

0

9

8

0

9

10

Number of clustered cells

Number of Clusters

5

Number of Clusters

10

0

4

12

Number of Clusters

Number of Clusters

12

3

Figure&5.127: Analysis of clustering studies.

10

Number of clustered cells

Study 03

0

Study 02

2

3

4

5

6

7

8

Number of clustered cells

9

10 8 6 4 2 0

1

2

3

4

5

6

7

8

9

Number of clustered cells !Design Development!137


Clustered Site Through this systematic distribution of population across the site, the information that is coupled with the resulting clusters informs parameters which will drive the manner in which the site will develop. Factors such as population, density, surface area, buildable area and wetland impact will continually be referenced from these studies throughout the development of the urban system.

Study _ 05

!6

!8

!2

!3

!7 )9

)7

!5 !0 )4 )8 )6 )2 !4 !1 )3 )5 )1 )0

125 Population 9008

75

Water Consumption 3873440 L

50 25 0

Available Wetland Area Needed

Cell A : 2 Cell B : 1 Cell C : 1

125 100

Population 9828 Water Consumption 4226040 L

25 0

Available Wetland Area Needed

Cell A : 4 Cell B : 0 Cell C : 1

Surface Area (hectares)

Surface Area (hectares) 138!Collective Ecology

125

50

Population 8640

75

Water Consumption 3715200 L

50 25 0

Available Wetland Area Needed

Cell A : 4 Cell B : 0 Cell C : 0

125 Population 8036

75

Water Consumption 3455480 L

50 25 0

Available Wetland Area Needed

Water Consumption 4226040 L

50 25 0

Available Wetland Area Needed

Cell A : 4 Cell B : 0 Cell C : 1

150 125 100

Population 8036

75

Water Consumption 3455480 L

50 25 0

Available Wetland Area Needed

Cell A : 1 Cell B : 1 Cell C : 2

Cluster 6

150

100

Population 7668

75

Cluster 5

150

75

Surface Area (hectares)

150

Cluster 4

100

125 100

Cluster 03 Surface Area (hectares)

150

100

150

Cluster 02 Surface Area (hectares)

Cluster 01 Surface Area (hectares)

Figure&5.129: Analysis of clustered cells informs future development of the parcels on the site.

Cluster 00

Cell A : 1 Cell B : 1 Cell C : 2

Surface Area (hectares)

Figure&5.128: Selected clustering Cluster Analysis 73 individual cells grouped together into 19 clusters.

150 125 100

Population 9008

75

Water Consumption 3873440 L

50 25 0

Available Wetland Area Needed

Cell A : 2 Cell B : 1 Cell C : 1


150 125 Population 8640 Water Consumption 3715200 L

25 0

Available Wetland Area Needed

Cell A : 4 Cell B : 0 Cell C : 0

25 0

Population 9828 Water Consumption 4226040 L

25 0

Available Wetland Area Needed

Cell A : 4 Cell B : 0 Cell C : 1

Surface Area (hectares)

Surface Area (hectares)

125

50

Population 7668 Water Consumption 3297240 L

25 0

Available Wetland Area Needed

Cell A : 3 Cell B : 0 Cell C : 1

Surface Area (hectares)

Surface Area (hectares)

125

50

Population 3348 Water Consumption 1439640 L

25 0

Available Wetland Area Needed

Cell A : 1 Cell B : 0 Cell C : 1

Surface Area (hectares)

Surface Area (hectares)

125

50

Water Consumption 4449640 L

50 25 0

Available Wetland Area Needed

Cell A : 1 Cell B : 2 Cell C : 1

25 0

125 Population 1188

75

Water Consumption 510840 L

50 25 0

Available Wetland Area Needed

Cell A : 0 Cell B : 0 Cell C : 1

125 Population 9008

75

Water Consumption 3873440 L

50 25 0

Available Wetland Area Needed

Cell A : 1 Cell B : 2 Cell C : 2

150 125 100

Population 7668

75

Water Consumption 3297240 L

50 25 0

Available Wetland Area Needed

Cell A : 3 Cell B : 0 Cell C : 1

150 125 100

Population 8036

75

Water Consumption 3455480 L

50 25 0

Available Wetland Area Needed

Cell A : 1 Cell B : 1 Cell C : 2

Cluster 18

150

100

Available Wetland Area Needed

Cluster 15

150

100

Water Consumption 4960480 L

50

Cluster 17

150

75

Population 10348

75

Population 11536

75

Cluster 12

125 100

Cluster 16

100

125 100

Cluster 14

150

75

Cell A : 1 Cell B : 1 Cell C : 2

150

Cluster 13

100

Available Wetland Area Needed

Figure&5.130: Cluster analysis of selected clustering.

150

Cluster 11

150

75

Water Consumption 3455480 L

50

Cluster 10

100

Population 8036

75

Surface Area (hectares)

50

125 100

Surface Area (hectares)

75

150

Cell A : 2 Cell B : 1 Cell C : 1

Surface Area (hectares)

100

Cluster 09 Surface Area (hectares)

Cluster 08 Surface Area (hectares)

Surface Area (hectares)

Cluster 07

150 125 100

Population 2376

75

Water Consumption 1021680 L

50 25 0

Available Wetland Area Needed

Cell A : 0 Cell B : 0 Cell C : 2

!Design Development!139


Public Spaces Figure&5.131: Wetlands as a public park based on the most integrated nodes. The park is defining the subdivision as an attractor and density distribution

Low-rise

High-rise

Mid-rise

Park Wetlands

Public Park as Main Public Wetland Figure&5.132: Wetlands required for the site according to density.

Figure&5.133: Public wetlands percentage defining the public park.

140!Collective Ecology

Overview Given that 40 sqm of constructed wetland is required to treat the average daily grey water output per person, the amount of surface area to sustain 150,000 people would necessitate 36% of the entire site. Of this amount of required wetland, 60% will be integrated within buildings and courtyards, and the remaining 40% will be located at ground level as public wetlands. These public wetlands are usable parks that also act as water purification systems for neighbouring built morphologies. Therefore, their incorporation within the urban system should be within the most integrated parts of the site to minimise water flow distances throughout the system. Initial Parcellation Through the clustering of cells throughout the site, each cluster has individual water storage. In order to allow for shifts in density distribution and fluctuation of water demand, these storage points need to be interconnected to help each other overcome these variations. Storage points are connected through the application of Delaunay Triangulation which returns a triangular subdivision on the site through all of the points. (Fig. 5.134) These points are then further refined to more evenly and fully connect the site.

Surface Area of Wetland Required

36% Required

Surface Area of Park Wetlands

36% Required 21% Integrated 15% Park


Water Route and Storage Points

Integration Analysis (Betweenness Centrality)

Re-adjustment of nodes to be part of the site boundary

Relocation of Storage Points

Connection of most Integrated Nodes (Minimum Spaning Tree)

Park Wetlands cover the most integrated part of the site

Figure&5.134: Work flow steps to define the public wetland and building density distribution.

15%

Height of building distribution in relation to the park

Low-rise

High-rise

Mid-rise

Park Wetlands

Public Wetland From this connected network, betweenness centrality is applied to identify the most integrated and traversed nodes within the site. (Fig. 5.134) These nodes, primarily located in the centre of the site and along the coastline, are then connected with a minimum spanning tree to establish the shortest possible network that connects all points in a set. (Fig 5.134) This connected network is what defines the location of our public wetland, ensuring high connectivity within the site. (Fig 5.134) Parcel Subdivision With the public wetland located within the site, a recursive subdivision algorithm was applied to diversify parcel size

Modification of building height distribution based on most integrated nodes of the site

Low-rise

High-rise

Mid-rise

Park Wetlands

throughout the site. Two approaches were considered and tested for determining block size. The initial approach established more connection points and resulted in smaller parcel sizes that were closer to the public wetlands. This method allowed for inhabitants of low rise typologies to more easily access the public wetlands. However, this decreased over all connection to the park, given that larger parcels which housed higher densities of inhabitants were located farther away from the public wetlands. (Fig. 5.131) The second method attempted to approach this condition by locating the high rise parcels nearest the most connected nodes within the site, with lower density parcels in less integrated areas. This provides the highest number of people to the most accessible areas of the site. !Design Development!141


Figure&5.135: Public squares based on Closeness Centrality,

km

Low-rise

High-rise

Mid-rise

Park Wetlands

Locating Public Squares Figure&5.136: Clustering used to analyse the integration

142!Collective Ecology

Once the public wetland and initial subdivisions are established on the site, closeness centrality analysis of each cluster establishes the areas of highest connectivity (Fig. 5.137) for placement of public squares. With the locations of the public squares, each cluster is analysed further for additional subdivision, in a similar manner as the public wetlands. (Fig. 5.137) Parcel subdivisions within each cluster position the high rise parcels nearest the most connected nodes within the site, with lower density parcels in less integrated areas, generally further away from the public square. This allows for the highest number of people within the cluster to have access to their local public space.

Cluster Target

7000 People


Closeness Centrality Analysis per Cluster

Location of Public Squares Figure&5.137: Clustering analysis of integration with the public squares to define subdivision and density distribution.

km

low

high

Evaluation of Public Square within a cluster based on Closeness Centrality Cluster to Evaluate

low

Evaluation of the subdivided cluster based on Closeness Centrality Cluster to Evaluate

high

low

high

!Design Development!143


Final Subdivision, Public Squares and Park Figure&5.138: Merging public wetlands and pubic squares layer

Low-rise

5.4 km2

Mid-rise

1.8 km2

High-rise

4.6 km2

Park Wetlands

1.8 km2

Total Surface Area 13.6 km2

144!Collective Ecology


!Design Development!145


Urban Wetland Integration Examining Spatial Qualities Figure'5.139: Three viable plant options.

80%

66%

100%

3-4m 2-3m

1m

Phragmites Australis Exposure Tolerance: Average Height: Flowers: Characteristics:

Nearly Full Sun 2m Yes Highly Invasive

Typha Domingensis Exposure Tolerance: Average Height: Flowers: Characteristics:

Development of the park wetland was driven through the ecological requirements of plant species, coupled with the impacts of their physical characteristics on the site. Diverse plant species can handle differing solar exposure levels, and provide variations in shading abilities, density, and visibility. The park wetland running though the centre of the site meets with multiple road network types, prompting strategic placement of plant species to allow or discourage views throughout these areas. These variations in plant characteristic allow for the development of differentiated spatial and microclimatic conditions within the park wetland to establish a heterogeneous landscape throughout the site. Park Wetland Production 1,786,502 (m2) area of the park wetlands on site 17,865,020 (L) total treated water from the main park

146!Collective Ecology

Full Sun 3m Yes Dense & Dominant

Juncus Rigidus Exposure Tolerance: Average Height: Flowers: Characteristics:

Full or Partial Sun 0.5 - 1 m No Aridity Tolerance


Figure&5.140: Ground and elevated condition.

4.5m Shaded

Grey water

Grey water

1m Typha Domingensis 0.6m

3.5m Juncus Rigidus Purified water output

Figure&5.141: Dual elevated condition.

Shaded 4.5m Grey water

Grey water 0.6m Typha Domingensis

Phragmites Australis

3.5m

Purified water output

3m

Figure&5.142: Dual ground condition.

Shaded

Grey water

Grey water 0.6m

3.5m Typha Domingensis

Phragmites Australis Purified water output

!Design Development!147


Figure&5.143: Belowground vehicle integration.

Grey water

Pedestrian road

Phragmites Australis

Purified water output

Figure&5.144: Above ground vehicle integration.

Grey water

Pedestrian road

Purified water output

148!Collective Ecology

Typha Domingensis


Grey water

Grey water

Juncus Rigidus

Pedestrian road

4m

20-22m Primary road

Purified water output

3m

Grey water

Grey water

Pedestrian road 6.5m Phragmites Australis Purified water output

Secondary road

Wetlands barrier to car roads

!Design Development!149


Network Development Differentiation of System Hierarchies Photograph(5.49: Branching network of a tree Source: David Hulme <http://www.flickr. com/photos/davidhulme/5269425862/>

tworks, p.11

The construction and the structure of graphs or networks is the key to understanding the complex world around us.”126

Figure(5.145: Betweenness centrality analysis

Three network typologies are established through evaluation of the parcel subdivisions across the site. The primary road network is informed through betweenness centrality analysis of the network, which finds the paths with the highest probability of being utilised when traversing from one node to another. This resulting network contains the most integrated paths, with a width of 22m. The secondary road network is based on the initial subdivision established through the Delaunay Triangulation, creating localized road connections that are 11.5m wide. Lastly a pedestrian network aims to have as many uninterrupted paths as possible while minimising intersections. These pathways are located along road networks and as dedicated pedestrian pathways 3.5m wide, to minimise sun exposure and promote use.

Albert-László Barabási

Betweenness Centrality Analysis on First Level of Subdivision

low

150!Collective Ecology

high


Figure&5.146: Roads network based on integration analysis.

Low-rise

High-rise

Mid-rise

Park Wetlands

Sections Sections Sections ofofof Roads Roads Roads and and and Streets Streets Streets Based Based Based on onon Downtown Downtown Downtown Doha Doha Doha Primary Primary Primary

Secondary Secondary Secondary

Pedestrian Pedestrian Pedestrian

2.50

3.25

building pedestrian

3.50

building

building

3.25

pedestrian

lane 1

3.25

lane 2

pedestrian

2.00 22.00

building

building 3.25

pedestrian

lane 2

3.25

lane 4

lane 1

3.50

lane 3

pedestrian

building

Figure&5.147: Sections of roads and streets based on downtown Doha

3.25

2.50

3.50

11.50

!Design Development!151


Test Patch System development

Figure&5.148: Outcome of network development, indicating the specific patch for further development

Park Wetlands and Public Spaces

Relationship of network integration and building height With each clusterâ&#x20AC;&#x2122;s subdivision established, information of the floor area and the wetland required for its population is utilised to develop each clusterâ&#x20AC;&#x2122;s urban morphology. (Fig 5.149) Each building typology is dispersed based on the parcel subdivisions throughout the clusters within the site. The level of integration each parcel subdivision has within the network is analysed and informs the extruded building height. This relationship between network integration and building heights allows for increased floor areas and higher densities within the most well-connected areas of the site. (Fig 5.150) 152!Collective Ecology


Available Wetland Area In Buildings

Floor Area

Available Wetland Area In Buildings

Floor Area

450 400 350 300 250 200 150 100 50 0

Available Wetland Area In Buildings

Roads Integration Analysis low

Available Wetland Area In Buildings

high

Floor Area

450 400 350 300 250 200 150 100 50 0

Available Wetland Area In Buildings

Figure&5.149: Cluster analysis

Available Wetland Area In Buildings

Floor Area

Cluster 09

Floor Area

450 400 350 300 250 200 150 100 50 0

Cluster 12 Surface Area (hectares)

Surface Area (hectares)

Cluster 10 450 400 350 300 250 200 150 100 50 0

Floor Area

Surface Area (hectares)

450 400 350 300 250 200 150 100 50 0

Available Wetland Area In Buildings

450 400 350 300 250 200 150 100 50 0

Cluster 08 Surface Area (hectares)

Surface Area (hectares)

Cluster 07

Surface Area (hectares)

450 400 350 300 250 200 150 100 50 0

Cluster 06

Available Wetland Area In Buildings

Floor Area

Cluster 13 Surface Area (hectares)

450 400 350 300 250 200 150 100 50 0

Cluster 04 Surface Area (hectares)

Surface Area (hectares)

Cluster 02

Floor Area

Most Integrated Road Defining Height

450 400 350 300 250 200 150 100 50 0

Available Wetland Area In Buildings

Floor Area

Buildings Height Based on Roads Integration

Figure&5.150: Buildings height based on integration analysis of surrounding roads.

!Design Development!153


Figure&5.151: Heights gradients per typology

154!Collective Ecology


Building Heights

20

Number of floors

18 16 14 12 10 8 6 4 2 0 Low Rise

Courtyards

High Rise

!Design Development!155


Figure&5.152: Outcome of building heights

Study A Study B

Evaluation of Pedestrian Network From the developed network and morphology, two tissue samples within the patch were evaluated to analyse the amount influence building heights and street widths have on the resulting sun exposure levels within the site. Focusing on Study A, high density typologies arenâ&#x20AC;&#x2122;t provided enough access to sun in both the summer and winter because of very narrow adjacent roads. However Study B, comprised of primarily low rise typologies, demonstrates a more balanced exposure level, allowing some solar access in the winter season, yet still providing some shadows during the summer. (Fig. 5.153)

156!Collective Ecology


Autumn

Figure&5.153: Network sun exposure studies

Autumn 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

!Design Development!157


Figure&5.154: Close-up view of the patch

Optimisation of the Initial Network Figure&5.155: Street analysis of Shibam in different seasons

To better optimize solar exposure levels within the road Autumn networks, a small study examines the solar conditions at ground level based on street width and orientation (Fig. 5.156) Four test patches are developed and are evaluated for both winter and summer solar exposure. These eight tests contain a set of buildings 12 stories high, with street widths ranging from 6-24m. Analysis of these studies indicates how to develop a system of varying street widths to better control solar exposure and promote pedestrian use within a network.

13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

Autumn 13.7 12.4 11.0 9.6 8.2 6.9 5.5 4.1 2.7 1.4 0.0 Hours

158!Collective Ecology


Study 01 Street widths that are 6m wide provide shade throughout the year, with some solar access for 4-6 hours a day during the summer months.

Figure&5.156: Pedestrian roads solar analysis in different seasons with a width variation

Study 02 Doubling the width to 12m wide, the streets develop two distinct characteristics, almost completely shading the north-south streets for the entire year, with solar access only to streets running east-west during the summer months.

Study 03 At 18m wide, the street demonstrates very similar solar access to what occurs at a width of 12m. This allows for further expansion of widths on along the north-south streets, providing shade while allowing for additional wind flow.

Study 04 Widening the streets even further, to 24m, we find that the shading condition still remains along the north-south axis for a majority of the day, only being exposed for four hours a day

!Design Development!159


Figure'5.157: Patch network and tissue samples

Figure'5.158: Pedestrian roads optimisation logic according to orientation

Re-evaluated Network

Pedestrian Street Widths based on orientation

From the previous tests and analysis, east-west pedestrian streets demonstrate high levels of solar exposure, limiting their widths to expand to a maximum of 6m. North-south pedestrian streets however are able to increase up to 18m wide, while still providing sufficient shading conditions. Given the non-linearity of the streets throughout the site, their orientation varies to a great degree, alternating shading conditions throughout the lengths of many streets. (Fig. 5.159) An algorithm evaluates street orientation within the network and adjusts their widths accordingly to better accommodate or discourage solar access. These changes allow for both increased public space and better environmental conditions throughout the entire urban system. (Fig. 5.158)

3m 6.75 m

10.5 m

14.25 m

18 m

N 160!Collective Ecology


Tissue Sample A Before

Tissue Sample A After

Figure&5.159: Pedestrian street tissue sample before and after optimisation

Tissue Sample B Before

Tissue Sample B After

!Design Development!161


Building Morphologies Development of strategies considering social, cultural and climatic aspects.

Figure&5.160: Distribution of building typologies

Reliance on Park Wetlands High

Low

Proximity to Park Wetland

Figure&5.161: Percentages of required public wetland

164!Collective Ecology

To ensure viability of higher population densities within the most integrated areas of the site, a systematic approach for determining the water treatment capacities within building morphologies is established. Based on their proximity to the park wetlands, parcels located closer distances are capable of placing a higher reliance on the park wetlands to assist in their water treatment needs. This will require a smaller portion of their building morphologies to be devoted to the integration of constructed natural wetlands, and allow for a higher population of inhabitants within the building morphology. Those located further distances away require a larger portion of their morphologies to be devoted to constructed wetland systems, and capable of meeting the needs of smaller populations. Through feedbacks regulating the proximities and relationships between elements within the site, a system emerges that drives the morphology of the buildings and the capacities of their metabolic processes in relation to the collective ecology.

Integrated and Public Wetlands

36% Required 21% Integrated 15% Park


Figure&5.162: Park wetland.

X=Distance to Park Wetland 2X= Distance to Park Wetland 3X= Distance to Park Wetland 4X= Distance to Park Wetland

X%

2X% Figure&5.163: Percentage of wetland integrated within buildings.

3X%

4X%

!Design Development!165


Low Rise Development of strategies considering social, cultural and climatic aspects.

Figure'5.164: Location within patch.

Overview Novel low-rise building morphologies and network organisations will be generated throughout the parcels, driven by the requirements of the ecological processes, climatic conditions, and sociocultural modalities abstracted from sample tissues of case studies. Building off traditional courtyard typologies and urban networks, the metrics and relationships of their geometries will be extracted to establish a design methodology capable of developing a more culturally specific approach for the system. Further incorporation of ecological processes will explore the spatial impacts of constructed natural wetland systems within low-rise morphologies and will demonstrate their role as drivers of public space with different hierarchies. These factors are further shaped by the local climatic conditions of the region, moulding and organising the development of morphologies for optimised levels of solar exposure or shading as desired throughout the system. These arrangements will produce highly performative novel low-rise building morphologies and network organisations, capable of negotiating environmental conditions, managing hydrological flows, arranging infrastructural networks and creating complex spatial and microclimatic environments.

166!Collective Ecology


!Design Development!167


Plot Aggregation Outcome Figure&5.165: Application of plots on site.

36

35 12

37

31

6

16

27

4

37

29

25

26

28 39 38

31

Open Public Space 15

24

22

20

19

11

12 27

3

3 6

2 1

21

16

23

17

14

7

5

18 19

4 5

7

12

0

0

4

9

1

13

Aggregation Boundary Condition Emergent Semi-public Space

8

8

10

Plot Aggregation Sequence

14

2 29

Low rise Block

16

10

28

18 15

17

13

30

25

30

40

35

26

10

26

27

22 24

14

41

23

20

29

17

21

23

34

24

32

11

40

17

20

25

11

28

33

15

13

8

9

19

32

8 22

12

5

18

16

19 36

14

10

3 7

30

1

0

2

4

34

33 13

3

5

7

38

2 1

18

21

39

0

6

9

15

6 9

Closest Semi-public Space

11

Plot Distribution Table&5.24: Ana;ysis of plot distribution.

168!Collective Ecology

Plot Distribution Low-rise plot distribution is informed through methods developed from the plot experiments, and employed for use throughout areas of the system. (Reference: Chapter 4/ Experiments/Plot Distribution) Exploring the development of one parcel on the site, the chosen process for low-rise aggregation is presented. Informed by the metrics and relationships extracted from the Kerman case study, this process demonstrates a systematic dispersal of geometries that best address the cultural modalities of the region with through emergence of semi-public spaces throughout the parcel. The process initially quarters the parcel, allowing for extensions of the park wetlands to penetrate into the area as open public space, establishing four distinct blocks for the plot aggregation to be applied to. Informed by strategies developed in the plot distribution experiment, dispersal begins along the longest edge of each boundary condition and aggregates accordingly until the entirety of the space is occupied, with the emergence of interstitial semi-public spaces throughout their geometries. This process is conducted for five populations and evaluated with one another to establish the highest ranking population to be used within the parcel.

Plot Distribution on Selected Patch (Remapped Values) Block 1

Block 2

Block 3

Block 4

Fitness Criteria

Pop. 1

Pop. 4

Pop. 4

Pop. 2

Coverage Ratio

0.99

1.00

0.97

0.99

Porosity Ratio

2.00

1.98

2.00

1.98

Proximity Average for Public Space

-0.85

-0.86

-0.72

Frequency Ratio for Public Space

0.87

0.84

0.97

1.00

Total Evaluation

3.01

2.96

3.22

2.97


Selected Patch and Aggregation Boundaries Figure&5.166: Seperation for extended wetlands.

Block 1

Block 2

High-rise Block

Initial Distribution Pattern Block 3

Block 4 Aggregation Boundary Condition

Public Space Generation Lines

Plot Aggregation 12 12 9

2

6 7

12

11

3 6

7

5

7

1

Low-rise Block

0

0

4

9 12

3

2

2

1

Open public space

12

10 11

11 10

10 5

8

9

3 4

8

1

3

4

2 1

5

7 0

0

6

4

8

8

10

Semi-public Space

5

Plot Distribution On Selected Patch (Overall Evaluation)

6

9

Plot Aggregation 24 12

16

19 6

16

9

19 23

5 8

11 17

21

14

10

3

4

7

18

1

0

22

15

12

24

22

19 18

21 23

11 15

16 17

12

6

14

10

4

14 11

10

23

22

15 12

1

1

0

0 7

5

8

8 13

3

2

2

4 7

8

13 3

24

3

9

7

2 1

20

10 20

5

15

13

13 24

17

20

0

6

9

18

21

2

9

17 16

Open Public Space

14 18

Low-rise Block

19

4

5

Semi-public Space

6 11

Block 1

Block 2

Block 3

Block 4

Fitness Criteria

Pop. 1

Pop. 4

Pop. 4

Pop. 2

Coverage Ratio

0.961

0.974

0.932

0.969

Porosity Ratio

1.051

1.031

1.054

1.080

Min. Public Space Proximity

5.850

8

6.456

5.864

Max. Public Space Proximity

29.833

36.641

55.756

87.958

Proximity Average for Public Space

15.919

16.203

22.567

41.809

Number of Plots

41

42

31

20

Number of Public Spaces

9

10

4

1

Frequency Ratio for Public Space

4.556

4.2

7.75

20

Min. Public Space Size Average

1

8.5

3

90.5

Max. Public Space Size Average

193.25

484

284

90.5

Size Average for Public Space

78.25

100.875

77.75

90.5

Closest Semi-public Space

11

Table&5.25: Oversll plot distribution.

Closest Semi-public Space

!Design Development!169


Selected Parcel (Original Grid)

Plot Aggregation 00

Plot Aggregation 01

0

Figure&5.167: Plot aggregation sequence.

0 1 0 1

1 0

0

Plot Aggregation 06 6

6

1 2 0 3 5

2 0 3 4 7 5 8

3 2 6 1 0 4 5

2 0 4

3 1

3 2 6 1 0 4 5 7 8

5

6

Plot Aggregation 18

12 15 0 2 13 9 6 1 3 14 18 7 5 4 16 11 8 10 17

1 14 2 0 16 6 3 10 15 4 5 12 18 7 9 8 11 13 17 15 17 13 12 16 10 3 14 2 1 18 0 7 4 5 8 6 9 11

Plot Aggregation 30

12 15 0 2 13 9 6 1 3 14 21 18 7 5 4 11 19 16 8 1 14 10 17 0 22 20 2 6 10 15 16 24 3 23 27 25 4 5 12 20 26 30 18 7 27 8 11 13 28 29 19 9 17 28 22 21 30 23 24 29 26 25 30 26 27 28 25 24 20 3 2 29 22 19 11 6 1 0 18 15 7 9 4 5 21 16 12 7 8 8 23 17 14 10 13 9

170!Collective Ecology

0 2 1 3 7 5 4 8

15 17 13 12 16 10 3 14 2 1 18 0 19 4 6 11

5

1

Plot Aggregation 10 0 2 9 6 1 3 7 5 4 8 10

6

1

2 0 7 4 8

10

3 1

3 2 6 1 0 9 4 5 7 8 10

5

6

Plot Aggregation 20

12 15 0 2 13 9 6 1 3 14 18 7 5 4 11 19 16 8 10 17 20

1 14 2 0 16 6 3 10 15 4 7 5 12 20 18 8 11 13 19 9 17

20 3 2 19 11 6 1 0 18 15 9 4 5 16 12 7 8 17 14 10 13

1 2 0 3 10 4 7 5 9 8 6

6

4

3 2 11 6 1 0 18 15 9 4 5 16 12 7 8 17 14 10 13

Plot Aggregation 08

0 2 1 3 5 4

0

15 17 13 12 16 10 3 14 2 1 18 0 19

7 4 5 8 6 9 11

Plot Aggregation 32

12 15 0 2 13 32 9 6 1 3 14 21 18 7 5 4 11 19 16 8 1 14 10 22 20 17 2 0 6 24 23 31 27 16 3 10 15 25 4 12 20 5 26 30 18 7 27 28 9 8 11 13 29 32 28 19 17 22 21 30 23 31 24 29 26 25 30 26 27 28 25 24 20 3 2 29 22 19 11 6 1 0 18 15 7 9 4 5 21 16 12 7 8 8 23 17 14 10 13 9

15 17 13 12 16 10 3 14 2 1 18 0 19 4 6 11

5

2 0

3 1

7 4 5 8 6 9

Plot Aggregation 22

1 14 2 0 16 6 3 10 15 4 5 12 20 18 7 8 11 13 19 9 22 21 17

20 3 2 22 19 11 6 1 0 18 15 9 4 5 21 16 12 7 8 17 14 10 13

12 15 0 2 13 9 6 1 3 14 21 18 7 5 4 11 19 16 8 10 22 20 17

15 17 13 12 16 10 3 14 2 1 18 0 19

7 4 5 8 6 9 11

Plot Aggregation 34

12 15 33 34 0 2 13 32 9 6 1 3 14 21 18 7 5 4 11 19 16 8 1 14 10 22 20 17 2 0 6 24 23 31 27 16 3 10 15 25 4 12 20 5 26 30 18 7 27 28 9 8 11 13 29 32 28 19 17 22 21 30 31 33 29 23 24 26 25 34 15 17 13 30 12 16 26 27 10 28 25 24 3 14 20 3 2 2 29 22 19 1 18 11 6 1 0 0 19 18 15 7 4 9 4 5 21 16 5 12 7 8 8 6 23 17 14 10 13 9 11


Plot Aggregation 02

Plot Aggregation 03

0 2 1 1 2 0

2 0

3 2 1 0

1

Plot Aggregation 12

12 0 2 9 6 1 3 7 5 4 11 8 10

1 2 0 3 10 4 7 5 12 9 8 11 6

2 0

3 1

3 2 1 0 4

12 0 2 13 9 6 1 3 14 7 5 4 11 8 10

1 14 2 0 3 10 4 7 5 12 9 8 11 13 6

12 3 2 1 0

3 2 1 0 9 4 5 12 7 8 14 10 13 11 6

7 4 5 8 6 9 11

Plot Aggregation 24

1 14 2 0 16 6 3 10 15 4 7 5 12 20 18 8 11 13 19 9 17 22 23 21 24

24 20 3 2 22 19 11 6 1 0 18 15 9 4 5 21 16 12 7 8 23 17 14 10 13

1 2 0 3

4

Plot Aggregation 14

10

3 2 1 0 9 4 5 12 7 8 10

0 2 1 3 4

1 2 0 3

2 1 0

11 6

Plot Aggregation 04

0 2 1 3

12 15 0 2 13 9 6 1 3 14 21 18 7 5 4 11 19 16 8 10 22 20 17 24 23

15 17 13 12 16 10 3 14 2 1 18 0 19

7 4 5 8 6 9 11

Plot Aggregation 36

35 36 12 15 33 34 0 2 13 32 9 6 1 3 14 21 18 7 5 4 16 11 19 8 10 22 20 17

1 14 36 2 0 6 24 23 31 27 16 3 10 15 25 4 5 12 20 26 30 18 7 27 13 8 28 9 11 29 32 28 19 17 22 21 30 31 33 29 23 24 26 25 34 35 15 17 13 30 12 16 26 27 10 28 25 24 3 14 20 3 2 2 29 22 19 1 18 11 6 1 0 0 19 18 15 7 4 9 4 5 21 16 5 12 7 8 8 6 23 17 14 10 13 9 11

13 12 10 3 14 2 1 0 7 4 5 8 6 9 11

Plot Aggregation 26

1 14 2 0 16 6 3 10 15 4 5 12 20 18 7 8 11 13 19 9 17 22 23 21 24 26 25 26 25 24 20 3 2 22 19 11 6 1 0 18 15 9 4 5 21 16 12 7 8 23 17 14 10 13

12 15 0 2 13 9 6 1 3 14 21 18 7 5 4 11 19 16 8 10 22 20 17 24 23 25 26

15 17 13 12 16 10 3 14 2 1 18 0 19

7 4 5 8 6 9 11

Plot Aggregation 38

35 36 12 15 33 34 0 2 13 32 9 6 1 3 14 21 18 7 5 4 16 11 19 8 10 22 20 17

37 38 1 14 36 2 0 6 24 23 31 27 16 3 10 15 25 4 37 26 5 12 20 30 18 7 27 13 8 28 9 11 29 32 28 19 17 22 21 31 38 30 33 29 23 24 26 25 34 35 15 17 13 30 12 16 26 27 10 28 25 24 3 14 20 3 2 2 29 22 19 1 18 11 6 1 0 0 19 18 15 7 4 9 4 5 21 16 5 12 7 8 8 6 23 17 14 10 13 9 11

2 0

3 1

4

Plot Aggregation 16

12 15 0 2 13 9 6 1 3 14 7 5 4 16 11 8 10

1 14 2 0 16 6 3 10 15 4 7 5 12 9 8 11 13

3 2 1 0 15 9 4 5 16 12 7 8 14 10 13 11 6

15 13 12 16 10 3 14 2 1 0 7 4 5 8 6 9 11

Plot Aggregation 28

1 14 2 0 6 10 15 27 16 4 3 5 12 20 18 7 8 11 13 19 9 17 28 22 23 21 24 26 25 26 27 28 25 24 20 3 2 22 19 11 6 1 0 18 15 9 4 5 21 16 12 7 8 23 17 14 10 13

12 15 0 2 13 9 6 1 3 14 21 18 7 5 4 11 19 16 8 10 22 20 17 24 23 25 26 27 28

15 17 13 12 16 10 3 14 2 1 18 0 19

7 4 5 8 6 9 11

Plot Aggregation 41

35 36 12 15 33 34 0 2 13 32 9 6 1 3 14 39 21 18 7 5 4 11 37 38 16 19 40 8 1 14 36 10 22 20 17 2 0 6 24 23 41 31 27 16 3 10 15 25 4 12 20 37 26 5 30 18 7 27 9 8 11 13 29 39 28 32 28 19 17 22 21 31 38 30 33 29 23 24 40 26 25 34 35 15 17 13 30 12 16 26 27 10 28 25 24 3 14 20 3 2 2 29 22 19 1 18 11 6 1 0 0 19 18 15 7 4 9 4 5 21 16 5 12 7 8 8 6 23 17 14 10 13 9 11

!Design Development!171


Connecting Network Outcome Figure&5.168: Connecting Network

Semi-public spaces

Roads

Network Development Table&5.26: Network Development on Selected Patch Table&5.27: Remapped values

172!Collective Ecology

Network Development Continuing within the same parcel of the site, the methodologies developed in the network development experiments are employed to analyse possible connections and establish a network throughout the low-rise plots. (Reference: Chapter 4/Experiments/Network Development) Informed by the metrics and relationships extracted from the Kerman case study, this process generates the minimum network required for fully connected parcels throughout the site, and produces appropriate transitions through multiple levels of privacy, in accordance with the cultural modalities of the region. The process initially starts by extracting nodes the centre of open public spaces, and from eight boundary condition points, a Minimum Spanning Tree is then developed through evaluation of all possible configurations between nodes. The connections of the resulting topological paths are then analysed with consideration of the base grid to develop the Shortest Path required to connect all nodes throughout the parcel.

Network Development on Selected Patch Block 1

Block 2

Block 3

Block 4

Fitness Criteria

Pop. 1

Pop. 4

Pop. 4

Pop. 2

Number of nodes

37

52

24

16

Number of roads

19

27

16

9

Maximum straight length of roads

18

39.5

44

17

Overall network length

469.814

578.076

376.584

207.049

Network Development on Selected Patch (Remapped Values) Block 1

Block 2

Block 3

Block 4

Fitness Criteria

Pop. 1

Pop. 4

Pop. 4

Pop. 2

Number of nodes

1.00

1.00

1.00

1.00

Number of roads

-1.00

-0.93

-1.00

-0.90

Maximum straight length of roads

-0.67

-2.00

-2.00

-2.00

Overall network length

1.45

1.39

1.50

1.45

Total Evaluation

0.78

-0.55

-0.50

-0.45


Plots and Emergent Social Spaces

Cannecting Logic Figure&5.169: Network system development

Semi-public spaces

Starting points Possible connections

Connecting the starting points and the social spaces

Cannecting Path

Base Grid

Semi-public spaces

Semi-public spaces Connecting path (MST)

Possible roads

Starting points Connecting logic

Minimum Spanning Tree (MST) of connected points

All possible roads

Connecting Path + Base Grid

Resulting Network

Semi-public spaces Possible roads Connecting path (MST)

Semi-public spaces Connecting path (MST) Starting points Roads (Shortest Walk)

Topological relationship to define the network

Shortest Walk following the Connecting Path

!Design Development!173


Axonometric View

Morphology 00

Figure'5.170: Resultant building morphology

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

M

OR

M

OR

M

OR

M

OR

Morphology 01

SunG

SunE FAR00 FAR01 FAR02

1.05

9.46

1.55

2.04

0.73

S.A.

P

322.91

0.1

O_S

O_E

Building Generation Overview The affects geometry and orientation have on local environmental conditions and levels of privacy were explored in a previous experiment (Reference Chapter: 4 Experiments/Courtyard Studies) and catalogued to help drive the development of building morphologies. The results are based on circumstances the of variable characteristics, and categorised into four main types of courtyard building morphologies comprised of one, two, three or four sides, and enclosed by a single storey wall surrounding the parcels perimeter. (Fig 5.171) These four characteristic types are to be explored in a multiobjective genetic algorithm, taking into consideration the optimisation of multiple conflicting evaluation criteria. The resulting populations will provide a series of candidate buildings for each plot on the site, and further collectively assed in-relation to one another.

O_N O_W

D_S

D_E

D_N D_W

Morphology 02

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

Morphology 03

Variables -Morphology Type (M) -Building Orientation (OR) -Building Depth (D_S, D_E, D_N, D_W) -Offset from the Perimeter (O_S, O_E, O_N, O_W) O_S

174!Collective Ecology

O_E

O_N O_W

D_S

D_E

D_N D_W


Orientation

Built Depth

Building Offset

Figure&5.171: Variable characteristic Types

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

M

OR

O_S

O_E

Orientation

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

O_E

O_N O_W

D_S

D_E

D_N D_W

M

OR

O_S

O_E

O_E

O_N O_W

D_S

D_E

D_N D_W

D_E

D_N D_W

M

OR

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

M

OR

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

M

OR

O_S

O_E

OR

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

D_S

D_E

D_N D_W

M

OR

O_N O_W

D_S

D_E

D_N D_W

M

OR

M

OR

M

OR

Building Offset

M

OR

O_S

O_E

Built Depth

M

O_N O_W

Building Offset

Built Depth

Orientation

O_S

D_S

Built Depth

Orientation

O_S

O_N O_W

O_N O_W

D_S

D_E

D_N D_W

Building Offset

M

OR

O_S

O_E

O_N O_W

D_S

D_E

D_N D_W

!Design Development!175


Available Wetland Coverage

Section A - A’

Figure&5.172: Viablility analysis of wetlands.

B’

A

Section B - B’

>8 5-8

SunG

SunE FAR00 FAR01 FAR02

1.05

9.46

1.55

2.04

0.73

S.A.

P

322.91

0.1

Evaluation Criteria Criteria 00: Minimum Sunlight Hours at Ground Level (SunG) To better ensure functional private open space year round, shading conditions at the ground level are evaluated to minimise sunlight hours during the most extreme condition of the Northern Solstice on 21, June each year. (Fig. 5.174)

Figure&5.173: Fitness Criteria

Criteria 01: Maximum Sunlight Hours at Elevated Conditions (SunE) Horizontal building surfaces above the ground plane are contrarily evaluated to maximise sunlight hours during the least extreme condition of the Southern Solstice on 21, December, ensuring optimum solar exposure for integrated constructed wetlands. Criteria 02,03,04: Floor to Area Ratios (FAR00, FAR01, FAR02) Target floor to area ratios (FAR) extracted from those found in Kerman (Reference Chapter: 4 Experiments/ Social Spaces) and the courtyard analysis cases (Reference Chapter: 4 Experiments/ Low Rises: Courtyard Analysis) are utilised to establish a desirable range for each of the building floor areas to be contained within. Candidates of the GA population are rewarded if the FAR falls within this desired target range and penalised if not.

176!Collective Ecology

<5

A’

B

Hours

Criteria 05: Exposed Horizontal Surface Area (S.A.) Building geometries are evaluated to maximise the horizontal surface area (m2) above the ground plane, ensuring optimal arrangements for placement of wetlands throughout the site building. Criteria 06: Privacy (P) To ensure higher levels of privacy, building geometries are evaluated to minimise levels of vertical surface exposure from areas outside of the site, ensuring increased privacy from surrounding buildings.

Fitness 1.05

9.46

1.55

2.04

0.73

322.91

0.1

Genes 1

1

0

3

5.8

5.2

5.4

5.2

2

1

0

1

5.9

5.7

3.0

5.4

3

1

0

1

5.8

5.3

3.7

4.6

3

2

Ground Floor 1

3

First Floor 3

0

Second Floor


Ground Floor (21 of June)

Ground Floor (21 of June) >10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

>8 5-8

<5 Hours

N First Storey (21 of December)

First Storey (21 of December) >10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

>8 5-8

<5 Hours

N Second Storey (21 of December)

Second Storey (21 of December) >10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

>8 5-8

<5 Hours

N Roof (21 of December)

Roof (21 of December) >10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Figure&5.174: Solar analysis and viability of wetlands.

>8 5-8

<5 Hours

N !Design Development!177


Figure'5.175: Highest ranked individuals for each critea.

Evaluation Results -Fitness Evaluation: Criteria 00 (SunG) Analysis of the graph reveals the population geometries optimised to reduce the average amount of sunlight hours at the ground level by one hour. The minimum and maximum range of sunlight hours in the final population significantly reduced from the initial population, having an overall tighter population domain in the final population. -Fitness Evaluation: Criteria 01 (SunE) The fitness evaluation initially demonstrated a near constant increase in sunlight hours for the first 20 generations, helping maximise the hours of sunlight exposure on horizontal surfaces above the ground plane. However, these increases levelled off and showed little fluctuation in the fitness of subsequent generations. -Fitness Evaluation: Criteria 02 (FAR00) The FAR fitness criteria had target values within a desired range rather than targeting minimum or maximum as other criteria, leading it to graphically exhibit a less optimal average improvement from the first and last generation, but overall the values improve the range in which they fell within the chart. Although the average FAR (Red Line) fluctuates throughout Evaluation 02, and subsequently in Evaluation 03 and Evaluation 04, the lower and upper bounds of the generations (Black Lines) appear to stay fairly constant. This signifies that at least one individual in the population consistently ranks low and another that ranks high throughout the course of the evaluation. The graph demonstrates a clear increase of the average FAR for the ground floor over the course of the 50 generations, initially growing at a steep rate and levelling out as the generations matured. They tended to stay close within the target range, with little instances outside the desired ratios, finalising at a ratio nearly triple the initial average. -Fitness Evaluation: Criteria 03 (FAR01) With a similar strategy and goal of Evaluation 02, the average FAR for the centre storey in this evaluation also demonstrates an increased ratio throughout the generations, but at a more gradual rate, levelling out and staying within the desired range after only 15 generations. The average of these levelled generations exhibited a 50% increased FAR compared to the beginning of the evaluation to reach the target ratios.

Individual 06 - Best - Fitness 00

0.0

1.77

1.98

1.92

284.04 0.85

Individual 48 - Best - Fitness 01

0.47

11.0

1.51

2.25

1.91

147.7

0.71

Individual 33 - Best - Fitness 02

0.02

5.97

1.92

1.76

1.94

294.88 0.34

Individual 48 - Best - Fitness 03

0.47

178!Collective Ecology

8.57

11.0

1.51

2.25

1.91

147.7

0.71


Fitness Evaluation: Criteria_00 – Minimum Sunlight Hours – Ground Condition (SunG) Figure&5.176: Fitness evaluation results.

4 3 2 1 0

5

10

15

20

25

30

35

40

45

50

Generation

Fitness Evaluation: Criteria_01 - Maximum Sunlight Hours – Elevated Conditions (SunE)

Sunlight Hours

12

10

8

6

5

10

15

20

25

30

35

40

45

50

Generation

Fitness Evaluation: Criteria_02 - Floor to Area Ratios (FAR00) 3.0 2.5

FAR

2.0 1.5 1.0 0.5 0.0

5

10

15

20

25

30

35

40

45

50

40

45

50

Generation Fitness Evaluation: Criteria_03 - Floor to Area Ratios (FAR01) 3.0 2.5 2.0

FAR

Sunlight Hours

5

1.5 1.0 0.5 0.0

5

10

15

20

25

30

35

Generation !Design Development!179


Figure&5.177: Highest ranked individuals for each critea.

-Fitness Evaluation: Criteria 04 (FAR02) Analysis of the graph reveals that similar to Evaluation 02 and Evaluation 03, the average FAR for the top storey increased over the course of the evaluation, but with more inconsistency, initially increasing for the first half of the evaluation, before decreasing to an average ratio lower than the start of the evaluation, eventually increasing again to a ratio 50% greater than the initial average FAR. -Fitness Evaluation: Criteria 05 (S.A.) The fitness evaluation demonstrates a gradual increase in the exposed horizontal surface area (Red Line), leading to an average of 400m2 for the final generation, 100m2 (33%) higher than the initial average. A gain is also seen in analysis of the individuals establishing the higher bounds of each generation (Top Black Line), resulting in a maximum exposed horizontal surface area of just over 500m2, which is 100m2 (25%) higher than the initial generation. The individuals comprising the lower bounds of each generation (Bottom Black Line) demonstrated fluctuating results throughout the evaluation, but resulted in the greatest overall gain, to end at a minimum exposed horizontal surface area of 300m2, 150m2(100%) higher than the initial generation. -Fitness Evaluation: Criteria 06 (P) Analysis of the graph indicates that the average privacy levels decreased from the initial generation, however, through comparative analysis with the data output from the evaluation, it was discovered that a bug in the recording process for the development of the chart incorrectly averaged the data, producing incorrect results in the chart. The output results prior to this error however indicate a general improvement in privacy levels throughout the 50 generations.

Individual 47- Best - Fitness 04

0.09

1.89

1.71

2.0

311.89

0.67

Individual 26- Best - Fitness 05

0.09

5.18

1.89

0.0

0.0

370.75

1.0

Individual 46- Best - Fitness 06

0.47 0.26

180!Collective Ecology

6.11

9.57 11.0

1.51 1.71

2.08 2.25

1.96 1.91

262.16 147.7

0.71 1.0


Fitness Evaluation: Criteria_04 - Floor to Area Ratios (FAR02) Figure&5.178: Fitness evaluation results.

3.0 2.5

FAR

2.0 1.5 1.0 0.5 0.0

5

10

15

20

25

30

35

40

45

50

Generation

Fitness Evaluation: Criteria_05 - Exposed Horizontal Surface Area (S.A.) Surface Area (m2)

600 500 400 300 200 100

5

10

15

20

25

30

35

40

45

50

Generation

Fitness Evaluation: Criteria_06 - Privacy (P) 1.00

Privacy

0.75 0.50 0.25 0.00

5

10

15

20

25

30

35

40

45

50

Generation !Design Development!181


Individual 39 - Ranking 01

Individual 18 - Ranking 02

Individual 36 - Ranking 03

Figure'5.179: Top 12 ranking individuals.

0.04

8.64

1.68

1.95

0.0

326.12

1.0

0.0

Individual 37 - Ranking 04

0.08

8.66

1.74

2.1

1.88

257.06

9.08

1.72

1.97

287.72

0.73

Individual 11 - Ranking 05

1.0

0.0

7.82

1.73

Analysis The top ranking 12 individuals (Fig. 5.179) demonstrate a variety of different building geometries, with south facing, three sided morphologies dominating the majority of the individualâ&#x20AC;&#x2122;s top stories, ensuring the desired high levels of sunlight hours for wetlands and minimal light penetration at the ground level. Large four sided morphologies tended to occupy the ground level, with larger central open spaces to inhabit. Centre storey morphologies often exhibited a varied number of sides throughout the population. However, they almost always had their geometries offset from the ground storey condition, ensuring increased levels of horizontal surface area. Focusing on the fittest individuals for each criterion, the criteria most important for controlling sunlight exposure hours and horizontal surface area displayed some of the most sizeable average improvements, ensuring better solar comfort at the ground level and maximum integration of wetlands within the building morphology. The top ranking individual for minimal sunlight hours (Fig. 5.175) demonstrated a large open space at ground level with a smaller courtyard opening at the top storey, allowing less solar penetration. Individuals providing optimal solutions for wetland integration established south facing three sided building geometries on the top storey (Fig. 5.175) and higher values for building depth at each story (Fig. 5.175). 182!Collective Ecology

2.06

2.02

0.0

310.42

0.0

7.27

1.89

1.78

0.0

312.01

1.0

Individual 46 - Ranking 06

1.0

0.26

9.57

1.71

2.08

1.96

262.16

1.0

The fittest individuals for the FAR (Fig. 5.175-5.177) demonstrate a dominant increase in building depth for the particular storey being evaluated, but typically erodes when the multiple fitness criteria factors are considered together, as seen in the top 12 ranking individuals (Fig. 5.175). Privacy values typically show an increase in the levels of privacy from areas outside the site. The most fit individual (Fig. 5.177) demonstrated fully enclosed ground and centre stories and a three sided top storey, increasing the amount of unexposed vertical surface area within the building.


Individual 35 - Ranking 07

Individual 40 - Ranking 08

Individual 06 - Ranking 09 Figure&5.180: Top 12 ranking individuals.

0.06

8.03

1.82

1.92

1.97

285.26 0.84

0.03

Individual 30 - Ranking 10

0.04

7.49

1.61

2,15

1.91

290.16

7.16

1.75

1.69

0.0

319.17

1.0

0.0

Individual 43 - Ranking 11

1.0

0.05

8.91

1.72

2.16

1.85

260.24 0.76

8.57

1.77

1.98

1.92

284.04 0.85

Individual 14 - Ranking 12

0.02

5.73

1.92

1.76

1.93

292.44

0.35

Conclusion Overall the evaluation exhibits substantial improvements in the fitness values of the populations by the final generation of the experiment. The majority of the fitness criteria demonstrate significant optimisation, maximising or minimising accordingly within the limiting factors of other fitness criteria, and typically followed alongside trends of the best and worst individuals of the populations. The findings of this evaluation will help ensure low-rise building optimisation throughout the development of our urban system.

!Design Development!183


Wetlands Application on Public Space (Winter and Summer) Figure(5.181: Placement of Public Wetlands.

Public Wetlands Analysis of solar exposure hours at the ground level reveal locations throughout areas of the open public space and semi-public spaces capable of receiving sufficient hours of sun for placement of natural water treatment systems. Examined for both winter and summer, the cumulative yearly analysis reveals their optimised placement within the parcel, and the range of plant species capable of sustaining growth in each location based on the minimum amount of solar hours needed. This defines a spectrum of plant options based on their viability within a location, offering a selection of plant species based on the desired spatial and shading qualities or level of privacy for the specific location.

Solar Exosure And Wetland Viability

184!Collective Ecology

Evaluation Criteria

Open Public and Semi-Public Space

Cumulative Solar Hours

93328

Average Solar Hours

6

Unsuitable Wetland Area (sqm)

3089

Suitable Wetland Area (sqm)

9806

Medium Solar Exposure Area (sqm)

5280

High Solar Exposure Area (sqm)

4526


Public Space and Connecting Network Figure&5.182: Evaluated area and resulting solar exposure hours and wetland viablity tests.

Open Public Space

Semi-public Space

Low-rise Plot

Solar Analysis (Winter)

Optimized Wetland Application (Winter)

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Solar Analysis (Summer)

Thypha Domingensis Phragmites Australis Juncus Rigidus

Wetlands Not Viable

>8 5-8

<5 Hours

Optimized Wetland Application (Summer)

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Thypha Domingensis Phragmites Australis Juncus Rigidus

Wetlands are not viable

>8 5-8

<5 Hours

!Design Development!185


Low-Rise Sections Cumulative Section Overview

186!Collective Ecology


!Design Development!187


Section 01

Juncus Rigidus Location: Purpose: Height: Sun Tolerance:

Juncus Rigidus

Juncus Rigidus

0.6m

Low exposed areas and roofs Generate open views 0.6m 66%

0.6m

Typha

Location Purpose

Height: Sun Tol

Privacy

0.6m Pedestrian Road

Grey water output

Fresh water accumulation tank

188!Collective Ecology


S.1

Location of Detailed Sections

Juncus Rigidus

0.6m

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

a Domingensis

n: e:

High exposed areas Generate shaded conditions Separation of spaces : 3m lerance: 100%

Privacy

3m

Shaded

Typha Domingensis

Juncus Rigidus Grey water output Fresh water output

Black water output

Fresh water accumulation tank

!Design Development!189


Section 02

Juncus Rigidus Location: Purpose: Height: Sun Tolerance:

Juncus Rigidus Juncus Rigidus

190!Collective Ecology

Low exposed areas and roo Generate open views 0.6m 66%


S.2

Location of Detailed Sections

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

ofs

Juncus Rigidus

0.6m

Pe

!Design Development!191


Section 03

Juncus R

Location: Purpose: Height: Sun Tolera

Typha Domingensis Location: Purpose:

High exposed areas Generate shaded conditions Separation of spaces Height: 3m Sun Tolerance: 100%

Juncus Rigidus

Pedestrian Road

192!Collective Ecology

0.6m


S.3

Location of Detailed Sections

Rigidus

Low exposed areas and roofs Generate open views 0.6m ance: 66%

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

Road

!Design Development!193


Section 04

Juncus Rigidus

0.6m

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

Typha Domingensis Location: Purpose:

High exposed areas Generate shaded conditions Separation of spaces Height: 3m Sun Tolerance: 100%

Privacy

3m

Shaded

Typha Domingensis

Juncus Rigidus Grey water output Fresh water output

Black water output

194!Collective Ecology

Fresh water accumulation tank


S.4

Location of Detailed Sections

Juncus Rigidus Location: Purpose: Height: Sun Tolerance:

Low exposed areas and roofs Generate open views 0.6m 66%

0.6m Juncus Rigidus

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

2m

Phragmites Australis

Typha Domingensis Location: Purpose:

High exposed areas Generate shaded conditions Separation of spaces Height: 3m Sun Tolerance: 100%

3m

Typha Domingensis

!Design Development!195


High Rise Development of morphologies considering social, cultural and climatic aspects.

Figure'5.183: Area of parcel

Overview Novel high-rise building morphologies and network organisations will be generated throughout the parcels, driven by the requirements of the ecological processes, climatic conditions, and sociocultural modalities abstracted from sample tissues of case studies. Informed by the courtyard typologies and urban networks found in low-rise experiments, the metrics and relationships of their geometries will be extracted to establish a design methodology capable of developing a vertical morphology capable incorporating these aspects within the system. Further incorporation of ecological processes will explore the spatial implications of stacking building geometries for integration of constructed natural wetland systems as drivers of public space, with different hierarchies throughout high-rise morphologies. Local climatic conditions of the region, will mould and organise the development of morphologies for optimised levels of solar exposure or shading as desired throughout the system. These arrangements will produce highly performative, novel high-rise building morphologies and network organisations, capable of negotiating environmental conditions, managing hydrological flows, arranging infrastructural networks and creating complex spatial and microclimatic environments.

196!Collective Ecology


!Design Development!197


Optimised High-rise Dispersal

Figure'5.184: Solar analysis of exposed high rise facades.

N

Mean hours: 5.05 Number of Towers: 19

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

High Rise Dispersal Overview: To increase the solar exposure for building facades within clusters of high-rise morphologies, an emphasis is placed on their dispersal patterns to decrease shadow casting while, maintaining the highest levels of density possible. A multi-objective genetic algorithm was developed, taking into consideration the optimisation of conflicting evaluation criteria which aim to maximise the amount of towers on site while also maximising the amount of sunlight hours the building envelopes are exposed to. The resulting populations will provide a series of candidate building dispersals for the parcel which will be evaluated to establish the optimal organisation for a cluster of high-rises in the parcel. Evaluation Criteria: Criteria 00: Maximum Number of Towers Criteria 01: Maximum Sunlight Hours (Winter) Criteria 02: Maximum Sunlight Hours (Summer) Analysis: A varied range of possibilities were generated through the multi-objective algorithm, presenting similar quantities of towers and solar exposure hours, while offering dramatically 198!Collective Ecology

different dispersal characteristics within the parcel. (Fig 5.186) There was an increase overall for both summer and winter solar hour averages, with tower dispersal typically inhabiting just over half of the site. The main consistency for tower location was along the east side of the parcel, typically occupying the entirety of the plots available. Conclusion: The varied spatial characterises of the highest ranked candidate organisations offer the ability to choose among several similarly capable solutions for patterning high-rise dispersal within the parcel. Although density does not reach the maximum capacity as if the entire parcel was comprised of high-rise geometries, the ability to incorporate appropriate solar access to facades for constructed natural water treatment systems was achieved while balancing the highest levels of density possible.


Fully Occupied (Winter)

Fully Occupied (Summer)

Figure&5.185: Solar analysis of exposed high rise facades, fully occupied and optimized dispersal.

N

Mean Hours: 3.51 Number of Towers: 25

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Mean Hours: 3.64 Number of Towers: 25

Optimized Dispersal (Winter)

N

Mean Hours: 4.98 Number of Towers: 19

Optimized Dispersal (Summer)

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

N

Fully Occupied

N

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Mean Hours: 5.05 Number of Towers: 19

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Optimized Dispersal

N !Design Development!199


High-rise Dispersal 00

High-rise Dispersal 01

High-rise Dispersal 02

Figure&5.186: Selection of distribution test layouts.

N

Mean Hours (Summer): 4.72 Mean Hours (Winter): 4.59 Number of Towers: 19

N

High-rise Dispersal 03

N

Mean Hours (Summer): 4.79 Mean Hours (Winter): 4.99 Number of Towers: 17

N

N

N 200!Collective Ecology

Mean Hours (Summer): 4.95 Mean Hours (Winter): 5.15 Number of Towers: 16

N

Mean Hours (Summer): 4.98 Mean Hours (Winter): 4.98 Number of Towers: 15

N

N

Mean Hours (Summer): 4.95 Mean Hours (Winter): 5.08 Number of Towers: 16

High-rise Dispersal 08

N

High-rise Dispersal 10

Mean Hours (Summer): 5.17 Mean Hours (Winter): 5.16 Number of Towers: 15

Mean Hours (Summer): 4.97 Mean Hours (Winter): 4.84 Number of Towers: 17

High-rise Dispersal 05

High-rise Dispersal 07

High-rise Dispersal 09

Mean Hours (Summer): 5.02 Mean Hours (Winter): 5.23 Number of Towers: 15

N

High-rise Dispersal 04

High-rise Dispersal 06

Mean Hours (Summer): 5.05 Mean Hours (Winter): 4.98 Number of Towers: 16

Mean Hours (Summer): 5.03 Mean Hours (Winter): 4.78 Number of Towers: 17

Mean Hours (Summer): 5.19 Mean Hours (Winter): 4.70 Number of Towers: 16

High-rise Dispersal 11

N

Mean Hours (Summer): 4.71 Mean Hours (Winter): 5.32 Number of Towers: 15


!Design Development!201


Regaining Density

Figure&5.187: Solar fan limitations to morphologies.

N

Constrained High-Rise Geometry The high-rise dispersal strategy required removal of approximately half the towers within the parcel, dramatically reducing the original density levels. To regain a portion of these inhabitants, the towers were reintegrated and examined with the volume of the solar fan (Reference: Chapter 3/Methods/Solar Analysis/Solar Fan) to refine their geometries. These constrained high-rise morphologies allow for increased densities while ensuring no obstruction of solar access to the wetlands throughout the year.

202!Collective Ecology


Calculation of all the Solar Fans

Calculation of a Solar Fan

Figure&5.188: Process for removal of geometry for optimized solar acces.

N

N Intersection of the Solar Fan with the High Rises

N

Removal of the Obstructing Geometry

N Removal of the Obstructing Geometry

Maximum Buildable Volume in Order to Guarantee the Solar Access

N !Design Development!203


Development of Elevated Platforms and Tower Placement

Figure&5.189: Placement of elvated platforms.

Public space

Elevated Platforms High-rise

Constrained High-rise

Network Development Continuing within the same parcel of the site, the methodologies developed in the network development experiments are employed to analyse possible connections and establish a network throughout the high-rise plots. (Reference: Chapter 4/Experiments/Network Development) Informed by the metrics and relationships extracted from the Kerman case study and adapted for high-rise networks, this process generates the minimum network required to fully connect the parcel, and to produce appropriate transitions through the multiple levels of privacy, in accordance with the cultural modalities of the region. The process initially starts by extracting nodes from the centre of open public spaces, and from eight boundary condition points, a Minimum Spanning Tree is then developed through evaluation of all possible configurations between nodes. The connections of the resulting topological paths are then analysed with consideration of the base grid to develop the Shortest Path required to connect all nodes throughout the parcel.

204!Collective Ecology


Relationship to Adjacent Wetlands

Low-rise Area

Figure&5.190: Development of high-rise networks.

Open Public Space

Initial Distribution Pattern

Public Space Generation Lines

High-rise (Tower Dispersal)

Identification of Public Spaces and Connecting Logic

Connecting Path

Public space

Connecting path (MST)

Starting points

Starting points

Public space

Public space

Connecting logic

Possible connections

Connecting the starting points and public spaces

Minimum Spanning Tree (MST) of connected points

Connecting Path + Base Grid

Resulting Network

Connecting path (MST)

Starting points Public space

Possible roads

Topological relationship to define the network

Connecting path (MST)

Starting points Public space

Roads (Shortest Walk)

Shortest Walk following the Connecting Path

!Design Development!205


Development of Building Pedestals and Tower Placement

Figure'5.191: Elevated platform extrusion and development.

Pedestals and Public Wetlands Overview Within the low-rise morphologies, building plot dispersal and network organisation strategies established distinct public, semi-public and private spaces dispersed throughout the parcel. In order to generate similar privacy hierarchies within the development of high rise morphologies, a space will be developed for transitioning from the open public spaces of the extended wetlands, to the privacy found within the building morphologies. A building platform extruded from the plot boundary will address this issue, generating an elevated semi-public space surrounding the high-rise footprint that establishes a hierarchical threshold between the extended wetlands and high-rise morphology. Elevated Platform Connections Elevated platforms will be connected with the ground condition by adjusting a portion of the elevated geometry to establish stepped access from one privacy hierarchy to another, differentiating the spatial qualities for each connection. Elevated platforms will also be connected to one another through development of a secondary elevated network, established through a similar logic found within the network development experiments. (Reference: Chapter 4/Experiments/Network Strategies/Experiment 2) The methodology utilised considers all possible connections 206!Collective Ecology

between the centroids of the open public spaces and those of the building platforms to develop a Minimum Spanning Tree and define the connecting path. This topological relationship identifies which platforms will be connected to one another in addition to their connections to the open public spaces at the ground condition. Platform Development Elevated platforms throughout the parcel will be evaluated with solar fan analysis (Reference: Chapter 3/Methods/Solar Analysis/Solar Fan) to remove any volume of the platforms that obstruct direct solar access to the extended wetlands within an 8 hour period. Through exploring a series of systems for developing the geometry of the elevated platform, each scenario will be evaluated and analysed to achieve the highest level of solar access to the extended wetlands.


Extened Wetlands and Elevated Platforms

Evaluation of Solar Fan Volume

Figure&5.192: Elevated platform extrusion and development

N

N Removal of the Obstructing Geometry

Removal of Intersecting Solar Fan Volume

N

N Platfrom Accessibility with Ground Level

Connection Between Platforms and Opem Public Space

N

N !Design Development!207


Wetland Application within Open Public Spaces and Building Pedestals

Figure'5.193: Wetland apliction within the parcel.

Open Public Space

Building Pedestal High-rise

Mid-rise

Figure'5.194: Secondary elevated network development.

Variables -System 1: This system acts as a control for comparison to other evaluated systems. It considers a 10.5m platform extrusion without application of the solar fan. (Fig 5.192) -System 2: Develops the same extrusion as System 1, with removal of platform geometry based solar obstructions established through evaluation of the solar fan. (Fig 5.192) -System 3: Develops the same process as System 2, minimising the platform extrusion height to 7m. (Fig 5.192) -System 4: Develops the same process as System 3, with the accompanying high rise tower elevated two stories above the elevated platform to establish a void for additional wetland integration. (Fig 5.192)

Examining the viability of wetlands within semi-public platforms as well as the open public space increases the coverage area that is suitable for wetlands by nearly 200%. Similarly, the semi-public platforms offer substantially higher amounts of area of capable of receiving medium and high levels of solar exposure, at rates of nearly 200% and 300% respectively. (Table 5.29) Development of the Elevated Network

15

11

Evaluation Criteria -Criteria 00: Cumulative Solar Hours (sqm) -Criteria 01: Average Solar Hours (sqm) -Criteria 02: Unsuitable Wetland Area (sqm) -Criteria 03: Suitable Wetland Area (sqm) -Criteria 04: Medium Solar Exposure Area (6-8 Hours/Day) -Criteria 05: High Solar Exposure Area (8-12 Hours/Day)

6

16

12

9 4

13

Connecting path (MST)

3

8

14

1

10

Connecting Logic

5

Analysis Evaluation of the four systems developed presents incremental improvements for all values tested. The largest improvements are seen in System 4, which increased the area suitable for wetlands by nearly 20% and improved areas of high solar exposure by 38%. (Table 5.28) 208!Collective Ecology

Public space

0

2 7

Building pedestals


System 1 / Solar Analysis

System 1 / Optimized Wetland Application

Figure%5.195: High-rise solar analysis andwetland application

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

System 4 / Solar Analysis

Thypha Phragmites Juncus

Wetlands not viable

>8 5-8

<5 Hours

System 4 / Optimized Wetland Application

>10 9-10 8-9 7-8 6-7 5-6 4-5 3-4 2-3 1-2 <1 Hours

Conclusion Overall, the evaluation of elevated platforms among the high-rise morphologies exhibit substantial improvements in the availability of viable wetland space within the parcels, while also establishing semi-public transitions between the extended wetlands and the high-rise towers. Development of a secondary elevated network between platforms generates additional levels of connectivity among the semi-public spaces and establishes relationships between high-rise neighbouring morphologies similar to those found within the case study of Kerman. Establishing a system for developing the geometry of the platforms generated substantial improvements in solar access to the extended wetlands. Elevating the high rise towers two stories above the platforms established substantially large amounts of area suitable for wetland integration, increasing the overall treatment capacity of the high-rise morphologies. (Fig 5.195)

Thypha Phragmites Juncus

Wetlands not viable

>8 5-8

<5 Hours

Evaluation of Platform Development Evaluation Criteria

Sys. 1

Sys. 2

Sys. 3

Sys. 4

Cumulative Solar Hours

34358

36596

37870

39318

Average Solar Hours

5.18

5.65

5.87

6.11

Unsuitable Wetland Area

3058

2623

2406

2286

Suitable Wetland Area

3158

3593

3810

3930

Medium Solar Exposure

2494

2786

2922

2861

High Solar Exposure

663

807

888

1069

Table%5.28: Evaluation of wetland implementation between systems.

Solar Exosure And Wetland Viability Evaluation Criteria

Open Public Space

Semi-Public Platform

Cumulative Solar Hours

39318

122685

Average Solar Hours

6.11

3.51

Unsuitable Wetland Area

2286

26200

Suitable Wetland Area

3930

7987

Medium Solar Exposure

2861

4560

High Solar Exposure

1069

3426

Table%5.29: Comparison of wetland implementation within publi spaces and the semi-public platform.

!Design Development!209


Figure'5.196: High-rise solar optimization.

Tower Design

Building Generation Figure'5.197: Conflicting solar criteria for solar access and shading comfort.

Overview Through the development of low-rise morphologies, the affects geometry and orientation have on local environmental conditions and levels of privacy were explored and catalogued to help drive their development. A similar approach is explored to incorporate privacy hierarchies and courtyard spaces throughout the development of high-rise morphologies. A multiobjective genetic algorithm, takes into consideration the optimisation of multiple conflicting evaluation criteria to better incorporate the cultural modalities of the region, while maintaining high population densities and wetland treatment integration capabilities. Initial Organisation The high-rise geometries are evaluated as a series of stacked slabs, organising themselves to best achieve shading conditions for use as outdoor spaces, while also retaining surface areas of high solar exposure capable of integrating wetland systems.

210!Collective Ecology


Figure&5.198: A series of individual morphologies.

!Design Development!211


Figure&5.199: Removal of volume for additional areas of solar penetration and shading.

Figure&5.200: Removal of slabs for inhabitation.

212!Collective Ecology

Tower Design

Morphological Refinement With slab geometries optimised for both shaded and exposed solar conditions, a methodology for generating additional areas for solar exposure was established through removal of slab volumes throughout the building morphology. Each slab attempts to maintain the highest volume possible while also trying to establish additional surface area exposure of the slab below for additional placement of wetlands. These conflicting evaluation criteria offered a multitude of equally optimised individuals, each capable of sustaining its specified population density and wetland productions.


Figure&5.201: A series of individual morphologies.

!Design Development!213


Tower Design

Figure&5.202: Building volume.

High-rise Accessibility and Integration To establish a gradual transition between privacy hierarchies, the semi-public spaces of the elevated platforms extend up within the towerâ&#x20AC;&#x2122;s geometry through a series of steps and ramps entwined throughout the base to establish additional semi-public spaces within the high-rise. As the spaces rise in elevation they transition from semi-public spaces to private outdoor spaces for each floor. This system of transition allows for gentle transitions from one privacy hierarchy to another as found in the case studies of Kerman and utilised within other morphologies of the urban system. Conclusion Evaluation of the developed morphologies present high-rise geometries capable of incorporating wetlands with sufficient levels of solar access, while maintain areas of shade for the development of private outdoor spaces. They successfully incorporate privacy hierarchies as found within the lowrise morphologies but in a vertical fashion, transitioning between each threshold through a series of morphological differentiations within the building,

214!Collective Ecology


Vertical Access

Vertical Access

Figure&5.203: Vertical access and integration of semi-public space. Vertical access for private courtyards.

Horizontal Access

Private Access

Exterior Spaces

Exterior Spaces

!Design Development!215


High-Rise Sections Cumulative Section Overview

216!Collective Ecology


S.3

S.2

S.4

S.1

S.5

Location of Detailed Sections

!Design Development!217


+21.00

Section 01

Tower 01 +20.00

+17.50 Tower 01 Juncus Rigidus

+16.50

Location: Purpose: Height: Sun Tolerance:

+14.00 Tower 01 +13.00

+7.00 Tower 00

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

+3.50 Pedestal 01

2m +-0.00 Phragmites Australis

218!Collective Ecology

Phragmites Australis

Low exposed areas and roofs Generate open views 0.6m 66%


Fresh water accumulation tank

!Design Development!219


+58.50

Section 02 +56.00 Tower 01 +55.00

+52.50 Tower 01 +51.50

Typha Domingensis Location: Purpose:

High exposed areas Generate shaded conditions Separation of spaces Height: 3m Sun Tolerance: 100%

+49.00 Tower 01 +48.00

+45.50 Typha Domingensis

Tower 01 +44.50 Juncus Rigidus

+42.00

Location: Purpose: Height: Sun Tolerance:

Low exposed areas and roofs Generate open views 0.6m 66%

Tower 01 +41.00

+38.50 Tower 01 +37.50

+35.00 Tower 01 +34.00

220!Collective Ecology

Juncus Rigidus


Phragmites Australis

Juncus Rigidus

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

Juncus Rigidus

High exposed areas Generate privacy 3m 80%

Phragmites Australis

!Design Development!221


Section 03 +87.50 Tower 01 +86.50

+84.00 Tower 01 +83.00

Typha Domingensis Location: Purpose:

High exposed areas Generate shaded conditions Separation of spaces Height: 3m Sun Tolerance: 100%

+80.50 Tower 01 +79.50

+77.00 Tower 01 +76.00

Juncus Rigidus Location: Purpose: Height: Sun Tolerance:

Typha Domingensis

Low exposed areas and roofs Generate open views 0.6m 66%

+73.50 Tower 01 +72.50

+70.00 Tower 01 +69.00

+66.50 Tower 01 +65.50

222!Collective Ecology

Juncus Rigidus


!Design Development!223


Section 04

Juncus Rigidus Location: Purpose: Height: Sun Tolerance:

Juncus Rigidus

224!Collective Ecology

0.6m

Juncus Rigidus

Low exposed areas and roofs Generate open views 0.6m 66%

Jun


ncus Rigidus

Typha Domingensis Location: Purpose:

High exposed areas Generate shaded conditions Separation of spaces Height: 3m Sun Tolerance: 100%

3m

3m

0.6m 0.6m

Typha Domingensis

Juncus Rigidus

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

2m

Juncus Rigidus

Phr

!Design Development!225


Section 05

Phragmites Australis Location: Purpose: Height: Sun Tolerance:

High exposed areas Generate privacy 3m 80%

0.6m 0.6m

Phragmites Australis

Juncus Rigidus

Juncus Rigidus Juncus Rigidus Location: Low exposed areas and roofs Purpose: Generate open views Sun Tolerance: 66%

Fresh water accumulation tank

226!Collective Ecology


Typha Domingensis Location: Purpose:

High exposed areas Generate shaded conditions Separation of spaces Height: 3m Sun Tolerance: 100%

3m

0.6m

Typha Domingensis

Juncus Rigidus

3m

Typha Domingensis

!Design Development!227


228!Collective Ecology


!Design Development!229


230!Collective Ecology


!Design Development!231


232!Collective Ecology


!Design Development!233


Conclusions Overview COLLECTIVE ECOLOGY has presented a systems based urban model that acts as an agent for its own productivity, in which its metabolic exchanges develop symbiotically with the dynamic growth of its morphology. The feedbacks and critical thresholds of its ecological processes, climatic conditions and cultural modalities have driven the emergence of novel morphologies, social organizations and metabolic processes within a larger collective system. It explores the potential to minimise metabolic flow in and out of the system through integration of localised natural water treatment processes, extending hydrological retention within the system through multiple cycles of use and treatment. The resulting heterogeneous landscape of emergent interactions has presented a more homeostatic environment, in which the dynamic qualities of an urban system can better adapt to intensifying metabolic demands of a growing population.


System Overview

Cell Distribution on Site

!7 )8

)1 )0

Cell Distribution on Site To meet an overall desired population, a systematic approach to develop variable density dispersal and appropriate water storage distribution across the system was established. Factors such as population, density, buildable area, and wetland requirements of established clusters were continually referenced from these studies throughout the development of the urban system.

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Distribution of Park Wetland

Distribution of Park Wetland Ensuring viability of the system, 40% of the constructed natural water treatment system requirements are located within the landscape of the site as a public park. Its distribution within the most integrated parts of the site allows for high levels of access and the ability to support the water treatment requirements of the areas with the highest densities of the population.

Low-rise

High-rise

Mid-rise

Park Wetlands

Subdivision to Block Size

Site Subdivision and Distribution of Public Squares Initial divisions of the site are made between water storage locations, and subdivided further with Delaunay Triangulation to establish primary parcels. These edge conditions were then evaluated to identify the most integrated areas within each cluster for placement of public squares throughout the site.

Low-rise

236!Collective Ecology

High-rise

Mid-rise

Public Square


Betweenness Centrality Analysis on First Level of Subdivision

Network Development and Building Heights Additional subdivisions throughout the parcels were defined by their relationship to the Park Wetlands and Public Squares and Coastline. Evaluation of these parcels revealed their level of integration, establishing a hierarchy of network typologies, containing primary, secondary and pedestrian networks and identifying dispersal of building typologies across the site. Building heights were then established based on the highest level of network integration for each parcel. low

high

Constructed Natural Water Treatment Systems and Building Production Ratios

Constructed Natural Water Treatment Systems and Building Production Ratios Based on a parcelâ&#x20AC;&#x2122;s proximity to the Park Wetland, the percentage of water treatment required of its building morphologies is established. Buildings in parcels closer to the park require less integration of constructed natural water treatment systems, while those further distances are more self-sustaining.

Reliance on Park Wetlands High

Low

Plot Distribution 35 12

38

37

Low-rise Distribution A range of plot sizes and associative patterns were extracted from case studies and used as drivers for building dispersal throughout the parcel. Compact aggregations of building plots were driven and developed from these criteria, while generating well connected emergent interstitial spaces throughout the urban system.

31 30 32

28

33

8

9

19 23

29

25

24 19

18 21 23

20 11

15 16

17

29

22 24

25

3

6

12 14

1

7 10

1

0 7

5

8

8 13

3

2

29

0

4

9

12

10

28 2

9

38

15

13

30 27

1

5

7 8

2

40

33

13 3

4

36 34

32

14 11

10

41 26

28

39

31

40

17

23 37

27

13

20

24

25

20

35 26

22

17

21 26

34

11

22

15

12

5

16

19

36

14

10

3

4

7

18

0

2

6

16

27

1

18

21

39

0

6

9

15

30

17 16

14 18

19

4

5

6 11

!Design Development!237


Low-Rise Network

Low-rise Network A system was developed to generate the minimum network required for fully connected parcels throughout the site. Organised through the building plots and intestinal public spaces, it produces appropriate transitions through the multiple levels of privacy found within the system.

Low-Rise Morphology

Low-rise Morphology Within this building typology, four characteristic types were explored in a multi-objective genetic algorithm, taking into consideration the optimisation of seven conflicting evaluation criteria. The resulting populations provided a series of candidate buildings for each plot on the site, assessed further through a collective evaluation in-relation to one another.

High-Rise Distribution

High-rise Distribution To increase the solar exposure for building facades within clusters of high-rise morphologies, an emphasis is placed on their dispersal patterns to decrease shadow casting while, maintaining the highest levels of density possible.

N 238!Collective Ecology


Platform Development

Platform Development A building platform was extruded from the plot, generating an elevated semi-public space surrounding the high-rise footprint that establishes a hierarchical threshold between the extended wetlands and high-rise morphology.

High-Rise Network

High-Rise Network A system was developed to establish secondary connections throughout the high-rise plots, generating the minimum network required to fully connect the parcel. These connections produce appropriate transitions through the multiple levels of privacy within a vertical morphology, in accordance with the cultural modalities of the region.

Connecting path (MST)

Starting points Public space

Connecting logic

High-Rise Morphology

High-rise Morphology Influenced by the organisation of low-rise morphologies, a system was established to incorporate privacy hierarchies and courtyard spaces throughout the development of high-rise morphologies. It takes into consideration the optimisation of multiple conflicting criteria to better incorporate the cultural modalities of the region, while maintaining necessary population densities and water treatment capabilities.

!Design Development!239


System Relationships

Figure'5.204: Develomental process of incorporating feedback parameters rather than linear flow.

Ecological Processes

Sociocultural Modalities

Climatic Conditions

Ecological Processes Research into natural water treatment systems for use within an extremely arid and hot region such as Qatar established sets of data outlining treated water production capabilities, and the spatial implications of their morphological characteristics and coverage requirements. It presented the effects and advantages of dispersed natural water treatment systems within the context of Qatar and influenced strategies for ensuring their viability throughout urban the urban system. Sociocultural Modalities Through examination of regional case studies of Kerman, Iran and Shibam, Yemen, a system of analysis was developed to extract and quantify the cultural values and social parameters of sample tissues within their contexts. It established a catalogue of descriptive metrics and mathematics which expressed the sociocultural modalities of the region, and outlined culturally relevant and socially sensitive approaches for the system. Climatic Conditions The environmental qualities and sensory characteristics of Qatarâ&#x20AC;&#x2122;s climate were catalogued through the analysis of its humidity, temperature and solar exposure levels. These parameters drove aspects of solar accessibility and environmental comfort throughout the system to adapt to the specific challenges presented by its extreme climatic conditions.

240!Collective Ecology

Morphological Development

Social Organisations

Metabolic Performance

Morphology The allometric development of morphologies throughout the collective system were driven in response to the necessities of the ecological processes associated with natural water treatment systems, the sociocultural modalities of Qatari citizens, and the extreme environmental qualities brought forth by its climatic conditions. These parameters of Qatarâ&#x20AC;&#x2122;s specific qualities and requirements collectively influenced and moulded a process of morphogenesis to develop novel architectures and relationships throughout the system. Social Organisations The privacy hierarchies established throughout the system were informed by the sociocultural influences of Qatar, and incorporate ecological environments as extensions of public spaces to establish privacy thresholds and develop comfortable microclimates throughout the system. These multiple factors of influence establish a methodological approach for differentiated social spaces to be organised throughout the system in accordance with the unique modalities of Qatar. Metabolic Performance The metabolic input of the system was dramatically reduced through localised natural water treatment processes within the ecological environment. Their dispersal in accordance with the morphological development of the system synthesised the spatial impacts of their coverage requirements with the parameters of the climatic conditions and the consumption demands of the culture to develop an appropriate methodology for minimising metabolic flow throughout the system.


Analysis and Conclusions

Performance Evaluation Public Space Evaluation Criteria

Patch 1 (Public Space)

Cumulative Solar Hours

Buildings Patch 1 (Pedestals)

Patch 2 (Public Space)

Patch 2 (Pedestals)

Low-Rise

High-Rise

93328

56558

76054

132089

39541

141112

Average Solar Hours

6

7

5

4

11.39

5.49

Unsuitable Wetland Area

3089

3734

7129

22998

1801

22563

Suitable Wetland Area

9806

5166

6932

9447

37739

31117

Medium Solar Exposure

5280

2931

4547

5483

2751

14136

High Solar Exposure

4526

2236

2385

3963

34988

16981

Total Public Surface Area

12896

8900

14061

32445

39540

53680

Unsuitable Wetland Percentage

24%

42%

51%

71%

5%

42%

Suitable Wetland Percentage

76%

58%

49%

29%

95%

58%

Medium Solar Percentage

41%

33%

32%

17%

7%

26%

High Solar Percentage

35%

25%

17%

12%

88%

32%

Performance Analysis The systemâ&#x20AC;&#x2122;s results are encouraging, and offer the potential for further improvement through refinement and continued development. The generated patch established is capable of a relativity high population of over 11,000 inhabitants, with densities of 250 people/hectare, double the desired average of the site. This, however, is due to the proximity of the generated patch to a highly integrated area along the park wetlands, establishing its higher population levels. Analysis of the water treatment capacities for the generated patch reveal it is capable of treating only 25% of the required water input, and places reliance on the neighbouring park wetland to supplying an additional 15% to meet the systemâ&#x20AC;&#x2122;s goal of 40% reintegration. Although this reliance on the neighbouring park wetlands was intended, the system can be refined through adjustments of ratios among its elements to influence their importance within the morphology, such as the relationship of water treatment systems over density. Performance analysis of the developed system demonstrates that the multiple conflicting elements of a generative system aim at reaching equilibrium of its encompassing relationships and morphologies based on set parameters. Through continued examination of these parameters, the system can continue to better develop and further demonstrate its viability. Final Conclusion and Further Potentials Collective Ecology proposes a systems-based model for generating dynamic complex systems, placing emphasis on the interactions and connectivity of the flows through its infrastructures, and on the feedbacks and critical thresholds to drive the emergence of new morphologies social organisations. It is informed by ecological processes, environmental analysis, and sociocultural modalities of

the region to generate a contextually relevant approach to its development. It establishes a platform that can be applied to situations of urban growth around the world, capable of negotiating environmental conditions, managing hydrological flows, arranging infrastructural networks and creating complex spatial environments in response to the specific ecological, climatic and cultural modalities of its context. This platform presents a fundamental change from the current model of urban development, shifting the current water crisis into a water opportunity, dramatically reducing the systems metabolic input and extending the magnitude of its achievable population growth well into the future. Overall Analysis People per Household

3

Required Living Space for One Household (m2)

100

Require Wetlands per Household

120

Required Wetlands per Person (m2)

40

1 m2 can purify (L)

10

Total Water Needed per person (L)

341

Total Water Needs per household (L)

1023

Total Patch Area (m2)

113270

Total Area Capable for Wetland (m2)

90808

Total Liveable Area (M2)

393769

Total Water needs for Patch (L)

4027551

Total Number of Households in Patch

3937

Total Number of People in Patch

11811

Water Treatment Production Capacity (L)

908079

Number of People Capable of Sustaining

2662

Percentage of Water that is Treated

23% !Design Development!241