RE{CODE} - Urban Simulation platform for Socio-Economic Wellbeing

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Urban Urban simulation simulation platform platform for for socio-economic socio-economic wellbeing wellbeing

Thesis Dissertation Submission for the Master Degree 02 from the Institute of Advanced Architecture of Catalunya

Author : Nihar

Mehta

Thesis Advisors : Andrea Graziano & Alessio Erioli Program: MAA02 2019-2021, Barcelona

RE{CODE} Urban simulation platform for socio-economic wellbeing Author Nihar Mehta Thesis Advisors: Andrea Graziano & Alessio Erioli Teaching Assistant: Eugenio Bettucchi

Developed at

Barcelona September, 2021

MAA02

Master in Advanced Architecture Thesis presented to obtain the qualification of Master Degree from the Institute of Advanced Architecture of Catalonia

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

4

RE{CODE}

VIII. Framework

Abstract

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Traditional conventions of planning cause fragmentation of urban spaces and functions, imposing a degree of inflexible and brittle order, increasing the risk of segregation, gentrification and dispersion. This impacts the social and economic balance of the city, which is a salient aspect for better urban health and sustainable development.

The research aims to modify planning conventions by creating a framework to measure socio-economic imbalances and a protocol to design proposals for wellbeing. G.I.S. mapping and statistical methods of box-plotting and normal distribution are used to measure performances and identify deficient sites with higher inequality. The research develops a matrix of programmatic interventions in relation to deficiencies in neighborhoods. These interventions are determined based on their potential to improve the social value and are appended to neighborhoods with respective deficiencies. Blocks within the neighborhoods are analyzed, to select specific sites, based on their redevelopment potential. This is measured as a factor of underutilization in terms of available and existing facilities, amenities and population density against the factor of high cost of operation, in terms of building age and cost of maintenance. Further, impact analysis is performed using diffusion algorithm to determine the change in vulnerability index. This is done on both current and projected data. The framework is lastly adapted into a digital platform for stakeholders to create development and planning policies. The research’s strategy allows for proposing opportunities that can decrease imbalances and inequalities, and improve the social and economic value, leading to better wellbeing of societies.

IX. Protocol | X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection

Keywords: Socio-economic performance, urban analysis, digital platform, planning protocol.

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Abstract

5

Preface

Classical urban models most commonly illustrated the use of urban spaces by circles and other geometrical shapes in discrete zones.

Gideon Sjoberg (1960) for example, argued that in preindustrial cities, in the city core, close to the main official buildings and the squares was where rich people lived with their servants. People with fewer resources, who could not afford such expensive housing, thus settled in more remote quarters. Whereas the central area was dominated by merchants with their shops.

On the other hand, the socio- geographic pattern in industrial cities was the opposite. (Burgess 1925) Meaning, the poorest people lived close to the city centre, and the better off inhabitants had their homes in villas in the outskirts of the city. The city center became the central business district (CBD) which included shops of all kinds, restaurants, cafés, offices, banks, theatres, cinema and much more. Outside this central space formed the area for manufacturing and wholesaling activities.

Successively, land outward to these spaces formed the housing. Here the social status of the inhabitants was proportional to the distance from the city centre.

Finally, the city was surrounded by a commuter zone.

These two classical models over the years have been modified in various ways. Cities not only grew in hypothetical circles, but also in sectors and segments. These often follow transport routes of public transport lines, main roads, metro stations, and railway lines. Each zone thus has its own characteristics. Circles can be crossed by industrial areas, and activities that are normally found in the outskirts. Density of housing, type of demography occupying these houses changed according to the activities, rather than the distance from a center. The city essentially became multi-central.

The industrial era drastically changed the traditional planning of cities that had been observed before it for centuries. This encourages a new school of urban planning and projections of urban cities in the future. Le Corbusier’s project “Ville Radieuse” envisioned a city of the future which would contain effective means of transportation, abundance of green space and sunlight. He perceived this model to provide residents with a better lifestyle and contribute to creating a better society. Although evidently being a totalitarian, symmetrical and standardised in its nature, Le Corbusier’s principles had extensive influence on modern urban design and planning.

This urban model of stark structural and special segregation has become an antithesis in this age of increasing social, economical and cultural diversities found in 21st century cities. Understanding this multidimensional urban landscape not through the perspective of physical distinction, but one where the city is an interdependent metabolism of its component becomes important. Architects, urban planners, city council and urban technologies must adhere to the changing trends, and shift from the traditional conventions of urban design and planning.

With the advent of information technology and ever increasing interconnected world, both physically and virtually, has enabled the use of digital tools to not only visualise intangible parameters but also quantify them. The image of Le Courbusier’s hand directing the design of the future city, almost personified as “hand of god”, stands in contrast to the process digital tools and information technology enables.

Use of digital tools increases the accessibility and availability of urban level information to multiple stakeholder hitherto not involved and not considered as part of urban planning processes.

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

6

RE{CODE}

VIII. Framework

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IX. Protocol

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X. Stakeholders

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XI. Platform

Ville Radieuse, Le Corbusier

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XIII. Global outlook

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XIV. Reflection

Le Corbusier’s “Hand of God”

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7

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

Spacemaker

Delve

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RE{CODE}

VIII. Framework

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IX. Protocol

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X. Stakeholders

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XI. Platform

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........... | RE{CODE} |..........

XIII. Global outlook

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XIV. Reflection

Urban Footprint

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Examples like UrbanFootprint, Delve and Spacemaker show the potential of methodologies involving digital tools for design and planning.

Spacemaker leverages artificial intelligence to empower architects, real-estate developers and other stakeholders in the AEC industry to make datadriven decisions from the inception of the project, and to repeatedly iterate on designs and explore the boundaries of what is possible on a site.

Delve is a generative design tool which can be used from the pre-feasibility phases to having a permitted master plan. Utilities and infrastructure are important features in urban design. Which are thus integrated in the tool along with feasibility study with financial outlook. Generative design approach allows for a comparative filtering and selection of a potential design based on stakeholder inputs.

UrbanFootprint is an urban intelligence platform to assess risk, understand markets, and make better decisions with the help of urban, climate, and community resilience data. By bridging the gap between information and design the platform drives effective and equitable decision making capabilities.

Furthermore, this was enhanced into a digital platform that allows city planners, architects ad property developers to understand the different parameters influencing the social and economic balance, determine necessary interventions to then devise proposals to benefit the citizens and the city’s wellbeing. The platform, ultimately, enables actionable strategies to be developed. The thesis, thus, is a combination of theoretical approach and applied research to combine a holistic vision for the research to be part of a broader scenario of urban planning and design.

The goal of the thesis is to understand the city through the lens of the layers that influence how people live and experience the city. Additionally, the aim is also to create a methodology to use this understanding into furthering the way in which urban planning and urban design is conventionally conducted. This methodology is further elaborated in the book. In its current form, as presented to a jury panel at the end of masters discourse, the research was resolved into a framework to : analyse socio-economic vulnerabilities of people in the city, design proposals for wellbeing and social value improvement.

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

10

RE{CODE}

VIII. Framework

Acknowledgments Acknowledgments

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I want to thank my thesis advisors Andrea Graziano andAlessio Erioli for their guidance, support and patience throughout the course of the project.

Their expertise and knowledge in computational work flows truly pushed the possibilities of the project. And their critical thinking mindset enabled the sound development and necessary refinements to the methodology.

Additionally, I am incredibly grateful for the continual advice and theoretical assistance by Mathilde Marengo. Her insights in navigating urban issues allowed for the project to achieve a multi-dimensional outcome.

I also want to extend my thanks to my friend and colleague, Kushal Saraiya, for his unconditional help and support throughout the process.

Finally I am immensely grateful to our teaching assistant, Eugenio Bettucchi, all the IAAC staff, my classmates and my parents.

IX. Protocol | X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

11

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Preface

XIV. Reflection

Mateusz Zwierzyck Nikol Kirov Dhwanil Mehta

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Lillet Ricaurte Vatsal Kapadia Deepika Raghu Matin Derabi

XIII. Global outlook

Special thanks:

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Nihar Mehta

VII. Methodology

Index 1_Introduction 1_Introduction

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Context - Urban behavior and resource consumption 1.1_Global resource demands - Land and Energy

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V. State of Art

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VI. How?

1.2 _Observable trends and underlying structures of influence - Gentrification 17 and Socio-Economic equality 1.3_Scientific Interest

20

1.4_Hypothesis

20

1.5_Comparison study of land use against urban behavior

20

2_Strategy 2_Strategy 2.1_State of art

23

IV. Scientific Interest |

2.1.1 Reports and frameworks to understand and mitigate socio-economic 23 inequality 2.1.2 Current approaches to understand and mitigate socio-economic 24 inequality.

III. Barcelona Context |

2.2_Aims and Objectives

27

II. Global Context

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3_Methodology 3_Methodology 3.1_Methodology to design proposals for socio-economic equality

31

3.2_Analysis Stage

33

3.2.1_Constructing SEVI - A measure for evaluating performance

49

3.2.2_Statistical analysis of data

51

3.3_ Protocol Stage

54

3.3.1_ Determining blocks in the city for redevelopment

56

I. What?

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Case study : Neighbourhood 73, La Verneda i La Pau

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66

3.3.2_Calculating value improvement in opportunities and proposals

72

3.3.3_Proposal report for planning policy

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VIII. Framework

3.5_Refinement and testing of the diffusion algorithm

77

3.6_Future projection

79

IX. Protocol

73

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3.4_Impact analysis of interventions

4_Results 3_Results and and application application 4.2_Opportunities for wellbeing

93

4_Conclusions 4_Conclusions and and discussions discussions

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5.1_RE{CODE} methodology in the context of simulation platforms and policy 96 tools

X. Stakeholders

88

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4.1_Digital simulation platform stage

5.2_Global platform

98

5.3_Next steps and Future application

99

XI. Platform

6.1_Referencing Barcelona’s vulnerability matrix - Ajuntament de Barcelona102

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Appendix Appendix 104

7.2_Stakeholder assessment

105

8.1_Research Cosmogram

106

9.1_Alternate Thesis direction - Proposal through aggregation algorithm

110 114

XIII. Global outlook

Bibliography Bibliography

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7.1_Iceberg model

| XIV. Reflection |

Index

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Introdu 1.1 Global resource demands - Land and Energy

1.2 Observable trends and underlying structures of influence - Gentrification and Socio-Economic equality

1.3 Scientific Interest 1.4 Hypothesis 1.5 Comparison study of land use against urban behavior

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

|

Nihar Mehta

14

RE{CODE}

VIII. Framework | IX. Protocol | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection

2.2 Aims and Objectives

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2.1.2 Current approaches to understand and mitigate socio-economic inequality.

XI. Platform

2.1.1 Reports and frameworks to understand and mitigate socio-economic inequality

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2.1 State of Art

X. Stakeholders

uction

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15

1.1

Global resource demands - Land and been the steady increase in demand of land (Figure 3), material, services and energy (Figure Energy

4) (Vandecasteele I. et al. 2019). Land and energy

Today, 55% of the world’s population lives demand have risen the most over the past 15 years, in urban areas (Figure 1), a proportion that in the about 26% and about 1200 % respectively (Global next 30 years will be about 68% (Figure 2) (UN Change Data Lab 2021).

DESA 2018). The unprecedented expansion of cities Figure 1. has presented many challenges, both global and territorial. Not mitigating these challenges hinders Share of people living in urban areas, 2017 sustainable development and aggravates stresses on (Max Roser. 2013. “Future Population Growth”. Published online at OurWorldInData.org. vulnerable communities. A prominent bottleneck has Retrieved from: https://ourworldindata.org/future-population-growth)

II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

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Figure 2.

Population projection by the UN, World, 1950 to 2100

I. What?

(Max Roser. 2013. “Future Population Growth”.

Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/futurepopulation-growth)

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RE{CODE}

VIII. Framework

Figure 3.

Land uptake in OECD countries, 2015

| IX. Protocol

(Dr Ernest I. Hennig, Professor Jochen A. G. Jaeger, Tomáš Soukup, Erika Orlitová, Christian

Schwick, Die Geographen, Professor Felix Kienast. Urban sprawl in Europe.

Luxembourg: Publications Office of the European Union, 2016. https://www.eea.europa.eu/ publications/urban-sprawl-in-europe)

| X. Stakeholders | XI. Platform

Figure 4.

Energy Use per capita, World, 1965 to 2019

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(Hannah Ritchie and Max Roser.

2020.“Energy”. Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/energy)

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1.2

Observable trends and underlying structures of influence - Gentrification

Figure 5.

Inverted “U” Curve

| XIV. Reflection

Development of Society

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Introduction

XIII. Global outlook

Growing inequality is often assumed to be an inevitable cost of the development process. Kuznets (1955) posited that imbalance is low at the initial phases of development, when social orders are for the most part agrarian, and as industry develops, nations urbanize and economies grow quicker, and disparities increase. As nations develop further, increased disposable wealth enables broad-based education and social protection. The developing political force of the urban lower-income groups would prompt protective and supporting legislation, largely aimed at countering the effects of rapid urbanization and industrialization. Thus, as societies develop inequality would follow an inverted “U” curve (Figure 5).

Inequality in Society

and Socio-Economic equality

17

Cities are at the front line of managing change and the driving force for action to reduce the use of resources by taking an integrated approach and planning. They are the engines of the economy and the home of their citizens, and municipalities also supply and control various public services to residents and businesses that influence the majority of resource use, energy consumption and harmful emissions.

The demand for resources, compounded with traditional conventions of zoning and planning, is influenced by, and influences, gentrification of vulnerable communities, dispersion of less resilient populations and segregation within different strata of the society. Although this polarized distribution is the observed trend, the underlying cause is socioeconomic equality or inequality, which are the imbalances of social, economic and spatial dynamics between groups of populations.

III. Barcelona Context |

Mental & Physical livelihood

Community resilience

Financial growth

• Cost of living

• Displacement stress

• Productivity

• Social security

• Disaster risks

• Innovation and opportunities

• Access to amenities

• Crime frequencies

• Access to education

• Urban segregation Figure 6.

Impacts of social and economic inequality

| II. Global Context | I. What?

Imbalances in these urban dynamics result in exclusion of populations from adequate opportunities of growth and wellbeing, threatening sustainable urban development. High and rising inequality hinders progress towards meeting the Sustainable Development Goals. Highly unequal societies grow more slowly than those with low income inequalities and are less successful in sustaining economic growth.

Socio-Economic balance also impacts many characteristics of city life (Figure 6), like mental and physical livelihood of people, dictating access to social services and amenities; resilience of communities against displacement, crime and disasters, and lastly influencing people’s financial growth in terms of access to education and job opportunities (UN DESA 2020). These impacts are in a circular system of direct and indirect causes and effects between many urban dynamics (Figure 7), even the ones seemingly exclusive.

Social and Economic inequality

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

Developing proposals that impact such indicators and benefits communities would bring about propagating positive change. Thus, the research proposes to evaluate and plan for the city based on its operation and socio-economic performance instead of land use and zoning. The aim of the research is to design a framework of performance based redevelopment which can be applied to multiple cities. Although the city of Barcelona is considered as a case study to develop and resolve the framework. The studies conducted are, thus, within the context of Barcelona.

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VIII. Framework | IX. Protocol X. Stakeholders

Gentrification

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Figure 7.

Circular and propagating system of urban dynamics

+

+

-

Mortality

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Urban Sprawl

Pregnancies

-

-

Land consumption demand

+

+

Intercity travel time

+

+

GHG emissions

Eviction rates

+

XI. Platform

Local businesses

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+

Birth rate

+ +

+ +

Percent of migrant population

Health

+ Housing demand

Percent of employable population

Percent of unemployment

+

+

Accessible education facility

+ +

-

+

Amenities Green space allocation

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Green spaces available -

+

Household density

-

+

House area

+

+

+

+

Population density % + Commercial Area

Dilapidated buildings

+

+

+ +

Workspace area

+

+

XIV. Reflection

-

+

-

+

- + Percent of inactive spaces

% Residential area

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Public communal spaces

Socio-Economic Performace

+

XIII. Global outlook

-

-

Available family income

........... | RE{CODE} |..........

+

+

+

+

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-

Introduction

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Direct impact Indirect impact Increase factor Decrease factor

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1.3

Scientific Interest

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VII. Methodology

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Nihar Mehta

How can we alter urban planning conventions through performative

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

recomposition within urban fabric to improve the socio-economic balance?

1.4

Hypothesis

In current times where data is created constantly, it has become an important and irrefutable commodity in global economies.

But more importantly, it is a resourceful tool to decipher patterns in which people consume and affect the interconnected world. Urban data plays a crucial part in providing information to measure and assess intangible parameters which can help determine patterns and relationships of different aspects of city life. This data is collected and curated in many forms as spatial data, census data, personal data from social media and quantified self data, and sensor data (transport, surveillance, CCTV, Internet of Things devices, etc.)

The most prominent among these forms of urban data is Geographic Information System (GIS hereafter), which connects data to a map, integrating location data with all types of descriptive information. Understanding of census and personal data with GIS can help make informed decisions over urban interventions. Operating over this data using tools of computational design, machine learning techniques and statistical methods will force the rethinking of urban planning conventions. This will also prove to shift the urban design process from a linear model to a cyclic model where interdependent nodes with feedback loops can provide a greater scope in experimentation and simulation within different aspects of the system to refine the output.

20

1.5

Comparison study of land use against urban behavior

The hypothesis, to decouple urban planning from a linear model, based on zoning and land use, to one based on evaluation of urban performance and deficiencies, is reinforced through a preliminarily comparative study. This is carried by mapping the city as zones of behavior against a map of land use (Figure 8). Population density, percentage of migrant population, area of residential spaces, available education facilities, area of commercial spaces, available transportation facilities, tourism and hospitality facilities, social welfare amenities, value of cadastral and green space per capita are used as parameters to construct the zones of behavior and imbalances.

Higher values of population density, percentage of migrant population, area of commercial spaces, tourism and hospitality facilities and value of cadastral; and lower values of area of residential spaces, available education facilities, transport facilities, amenities and green space per capita indicate higher imbalances.

These parameters are mapped using machine learning method of K-means clustering with 5 (Figure 9), 6 (Figure 10) and 7 clusters (Figure 11).

RE{CODE}

VIII. Framework | IX. Protocol | X. Stakeholders XI. Platform

(Municipal Institute of Informatics. “Land uses of the city of Barcelona”. V1. 2016. Distributed

by Open Data BCN)

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Figure 8.

Barcelona Land use plan

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XIII. Global outlook | XIV. Reflection

Figure 9.

K-Means clustering of parameters 5 clusters

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Introduction

21

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

IV. Scientific Interest |

Figure 10.

K-Means clustering of parameters 6 clusters

I. What?

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II. Global Context

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III. Barcelona Context |

Figure 11.

K-Means clustering of parameters - 7 clusters

The maps show the city as a completely different fabric as compared to its land use counterpart. They showcase a highly segregated city of communities and areas with widely different socio-economic inequalities.

This indicates a high level of stress to the wellbeing that vulnerable communities may face against external conditions.

22

RE{CODE}

| IX. Protocol

the circular model and the role of compactness in urban resource efficiency (EEA 2015). It also State of Art highlights certain theoretical frameworks that

As cities are multidimensional systems. facilitate societal change based on dialogue between addressing a certain issue will have systemic private and public actors, and overcome the repercussions. The approach taken must also limitations of policy instruments that are insufficient be a system where perspectives of legislation, to deal with the complexity of urban challenges. policymaking, theoretical frameworks, technical Figure 12. reports and studies, community platforms and World Social Report 2020 - Inequality in rapidly changing world digital portals are integrated. Hence, projects and documents studied as state of art are multi-scalar (United Nations. (n.d.). (rep.). INEQUALITY IN A RAPIDLY CHANGING WORLD (20th ed., Vol. 1, Ser. World Social Report). United Nations publications. ISBN 978-92-1-130392-6) and multidisciplinary.

VIII. Framework

2.1

| X. Stakeholders

2.1.1

Reports and frameworks to understand and mitigate socio-economic inequality

|

The research closely follows the World social report by the United Nations department of economic and social affairs, Urban sustainability issues technical report by the European environment agency and Governance of land use in OECD countries by The Organization for Economic Cooperation and Development (OECD).

XI. Platform |

World social report – Inequality in rapidly changing world documents deep divides within and across countries in the era of extraordinary economic growth and widespread improvements in standards of living. The report also highlights how gender, ethnicity, race,residence and socioeconomic status continue to shape the chances people have in life (UN DESA 2020)(Figure 12). Furthermore, it also stipulates policies to ensure equal opportunity and reduce inequalities of circumstantial outcomes.

........... | RE{CODE} |..........

XIII. Global outlook |

European Environment Agency’s technical reports of Urban sustainability issues – What is a resource-efficient city? Resource-efficient cities: good practice and Enabling resourceefficient cities elaborates on identifying patterns of resource consumption in cities (Figure 13). The reports present the concepts of urban metabolism,

XIV. Reflection

Figure 13.

Urban Sustainability Issues (Urban sustainability issues. Rep. 2015th ed. Luxembourg: Publications, n.d. )

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Introduction

23

Governance of land use in OECD countries – Policy analysis and recommendations is a report by OECD (Figure 14) that analyses the impacts of spatial development, land use and zoning on economic, environmental and social adequacy in cities. It also describes policy and planning actions of adaptable and flexible zoning, sustainable densification.

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

|

Nihar Mehta

Figure 14.

The Governance of Land Use in OECD Countries (OECD (2017), The Governance of Land Use in OECD Countries: Policy Analysis and Recommendations, OECD Publishing, Paris, https://doi.org/10.1787/9789264268609-en)

2.1.2

with residents, stakeholders and elected officials.

The overarching strategies taken were to alter the land use by promoting growth of livable areas, increase the available stock of housing, promote economic activities to foster jobs and increase opportunities and lastly to support the infrastructure and invest in services. The proposal developed was to allow for new affordable and mixed-income housing and community facilities, encourage new commercial development, and provide requirements for street-level activity on commercial corridors.

Altering of land use and increasing housing stock also included provisions for inclusivatory areas, financing rehabilitation of existing tenants and evaluating the quality of existing houses. Increasing job opportunities included providing job training by setting up career centers, supporting commercial revitalization projects and providing business training courses. Investment in services included building new schools and community parks.

Superilla Barcelona, 2030 (Figure 16) mitigates gentrification and congestion in neighborhoods by modifying streets to public spaces and providing support to small and medium scale enterprises. The proposal presents the 21st-century street model which represents a paradigm shift with regard to the way the urbanization of public space has been conceived up to now. This involves reversing priorities and uses, and moving away from streets designed for cars to streets designed for people.

To face the housing emergency, Barcelona Current approaches to understand and City Council is working to help people through mitigate socio-economic inequality. grants and specific services, and also by increasing

the Public Housing Stock of the city. Co-Housing Barcelona, 2018 (Figure 17) looks at an alternative to traditional consumption of housing as a new accessibility model that also addresses consumption of energy in low density households. Co-Housing is a way of getting access to housing which allows a

East New York rezoning, 2016 (Figure 15) community of people to live in a building without promoted affordable housing preservation and being the owners or tenants at below-market prices, development, encouraged economic development, for a long period of time. increased walkable streets and investment in community resources to support long-term growth and sustainability. This was developed through a community planning process, a close collaboration

East New York rezoning, Co-Housing Barcelona and Superilla Barcelona address urban issues through legislative and policy tools. Whereas Archistar, Metricmonkey and Morphocode addresses issues through digital tools.

24

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VIII. Framework | IX. Protocol

Figure 15.

East New York neighborhood plan

|

(“East New York Neighborhood Plan.”

East New York Community Planning Plan - DCP. Accessed September 18, 2021. https://www1.nyc.gov/site/ planning/plans/east-new-york/east-newyork-1.page. )

X. Stakeholders | XI. Platform |

Figure 16.

Spain’s plan to create car-free ‘superblocks’ is facing protests

........... | RE{CODE} |..........

(Garfield, Leanna. Spain’s Plan to Create Car-Free ‘Superblocks’ Is Facing Protests. January 24, 2017. https://www. businessinsider.in/spains-plan-to-createcar-free-superblocks-is-facing-protests/ articleshow/56765990.cms.)

XIII. Global outlook | XIV. Reflection

Figure 17.

Co-Housing Barcelona (“CoHousing Barcelona.” Co-Housing Barcelona, November 22, 2019. https:// cohousingbarcelona.cat/en/co-housingbarcelona/. )

|

Introduction

25

Metricmonkey (Figure 18) is a digital platform that helps in design for better buildings by augmenting your existing work-flow with advanced digital design techniques. It works with organizations to develop strategies, processes, structural changes, and skill sets, to enable digital design and innovation to flourish.

Archistar (Figure 18.1) is a digital platform that facilitates the generation of design and evaluation of its execution feasibility for stakeholders at all scales, from city councils to inhabitants.

Morphocode (Figure 18.2) is a design and development tool that uses data to visualize urban dynamics and provide location insights. With a data driven dashboard, the platform covers the process from design to development, and allows the exploration of historical layers and cultural heritage to analyze pedestrian activity, business performance, and air pollution.

V. State of Art

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VI. How?

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VII. Methodology

|

Nihar Mehta

Figure 18.

Metric Monkey

III. Barcelona Context |

IV. Scientific Interest |

(Parametric Moneky. “Home.” MetricMonkey, August 22, 2021. https://metricmonkey.io/. )

Figure 18.1.

Archistar

|

II. Global Context

|

(Waititi-Parata, Hayden, Harj Uppal, and Michael Norman. “Property Development Software: Property Feasibility Software.”

Archistar, August 3, 2021. https:// archistar.ai/. )

Figure 18.2.

I. What?

Morphocode (“Morphocode.” MORPHOCODE, May 18, 2020. https://morphocode.com/.)

26

RE{CODE}

VIII. Framework

2.2

Aims and Objectives

| IX. Protocol

The aim of the thesis subsequently is to create a framework to design

proposals that reduce the inequalities within and among neighborhoods, and develop a simulation platform for stakeholders to make policies and visualize

| X. Stakeholders

the impact of design solutions.

| XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Introduction

27

3.2 Analysis Stage

3.1 Methodology to design proposals for socio-economic equality

3.2.1 Constructing SEVI - A measure for evaluating performance

|

II. Global Context

IV. Scientific Interest |

V. State of Art

| III. Barcelona Context |

Method

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

3.2.2 Statistical analysis of data 3.3 Protocol Stage

I. What?

3.3.1 Determining blocks in the city for redevelopment Case study : Neighbourhood 73, La Verneda i La Pau 28

RE{CODE}

VIII. Framework | IX. Protocol | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook

3.3.3 Proposal report for planning policy

|

3.3.2 Calculating value improvement in opportunities and proposals

X. Stakeholders

dology 3.4 Impact analysis of interventions

|

3.5 Refinement and testing of the diffusion algorithm

XIV. Reflection

3.6 Future projection

|

29

I. What?

|

II. Global Context

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

30

RE{CODE}

Policy tools of working with enterprises, Methodology to design proposals for planning tools of infrastructure decision making and socio-economic equality encouraging development for vulnerable people.

| X. Stakeholders

Strategies

IX. Protocol

The research also leverages analytical evaluation of social and economic indicators along with design tools of refurbishment and urban compactness.

The aims of the thesis to create a framework and platform are curated in a methodology of performance based urban planning and development.

This comprises multiple objectives in a cyclic model of feedback and involves four larger stages.

1] Carrying analysis; 2] Determining protocols of actions; 3] Creating a digital simulation platform; 4] Developing the execution strategy

|

The instances from state of the art and the comparative study hint towards multiple strategies that can be considered in conjunction and arrive at a potential methodology. The studies, frameworks and projects reinforce the multidisciplinary and multiscalar approach towards determining and designing proposals in any urban landscape, mirroring the cyclic model. The thesis and research RE{CODE}, thus, incorporates strategies at regional, territorial and intervention (Figure 19) for a comprehensive urban development process.

VIII. Framework

3.1

|

Design - Intervention scale

• Employment and social policy

• Infrastructure decisions

• Urban compactness

• Public procurement guidelines

• Land use heterogeneity

• Refurbishment

• Subsidies for sustainable redevelopment

• Land use connectivity

• Density, size and form

• Working with enterprises

• Mobility

• Quality and age

|

Planning - Territorial scale

........... | RE{CODE} |..........

XIII. Global outlook

• Tools and indicators of evaluation

|

• Development for vulnerable people

XIV. Reflection

Figure 19.

|

Social and Economic inequality mitigation strategies

Methodology

XI. Platform

Policy - Regional scale

31

|

Nihar Mehta

VII. Methodology

RE{CODE} METHODOLOGY 1] Analysis stage comprises of the following steps:

|

VI. How?

|

//Determining socio-economic parameters

-This step involves categorization, selection and mapping of relevant indicators with which to understand the characteristics of different urban areas.

//Determining performance thresholds

-This step involves use of statistical methods to determine the range of values that would be categorized as vulnerable in the datasets of the parameters.

//Evaluation of urban areas

-This step involves creating a composite index with the values of the parameters.

V. State of Art

2]Protocol stage comprises of the following steps:

III. Barcelona Context |

IV. Scientific Interest |

//Determining vulnerable areas

-This step involves using the composite index and statistical analysis to filter areas with different performances.

//Determining necessary social value improvements

-Creating a catalogue of activities to improve social values in relation to specific vulnerabilities.

//Determining opportunities and interventions for positive impact

-This step involves determining relevant programs and functions that would improve social value of the urban area.

//Evaluating impact of interventions on socio-economic vulnerability index and social value.

-This step involves simulating and calculating the social and economic impact to the specific vulnerabilities of the interventions on urban areas.

3] Platform stage comprises of the following steps:

II. Global Context

|

//Determining stakeholder outcomes

-This step involves creating a catalogue of potential stakeholders to expand the possibilities of the methodology. Further specifying the degree of contributions of the stakeholders.

//Creating interface with select inputs and outputs

-This step involves developing a digital platform for stakeholders to use the methodology and design intervention proposals.

4] Execution strategy stage comprises of the following steps:

I. What?

|

//Generating report for policy making

-Documenting the analysis, proposal specifications, impact and social value calculations for the city council to create development policies.

32

RE{CODE}

Analysis Stage

X. Stakeholders | XI. Platform |

to income, services, urban quality and well-being for all citizens. Besides,it aims to address the negative effects that are derived from the concentration of the levels of lower incomes in those neighborhoods that suffer most pronounced urban deficits, and where the quality of housing is lower. With these criterias, it focuses on four areas of strategic action: urban ecology, economic activity, social rights and education.

Its main purpose is to reduce social and territorial inequalities,while boosting access to income, services, urban quality and well-being for all citizens.

|

Development in different neighborhoods of Barcelona over time has been asymmetrical leading to inevitable inequalities. As a result the government formulated the Pla De Barris (Figure 19) proposals for different neighborhoods to work towards reducing inequalities and improving living conditions of the city. They focused on fundamental areas of housing, public space, education, health and creation of conditions for the improvement of economic activity. Its main purpose is to reduce social and territorial inequalities,while boosting access

IX. Protocol

Pla De Barris

|

The most vulnerable people are likely those whose needs are not sufficiently considered in the planning. This work builds on research that examines vulnerability as a social condition or a measure of the resilience of population groups when confronted by disaster (Cutter et al. 2003).

Vulnerability factors often occur in combination of multiple indicators (Morrow 1999).

Determining the most effective factors requires an intensive method of simulation and evaluation over

multiple test cases with varying degrees of success within an acceptable margin of error. This would ultimately constitute a discrete thesis, even multiple thesis, in the determination alone. Although for Barcelona, multiple historic frameworks, policies and projects can be considered as case studies to select the most relevant indicators and factors with which the city can be evaluated and possible patterns can be understood. The cases considered are Pla De Barris, Índex Sintètic de Desenvolupament / Vulnerabilitat Social de Barcelona and Área Metropolitana de Barcelona : Desarrollo Socioeconomico

VIII. Framework

3.2

........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection

Figure 20.

Methodology

|

Pla De Barris

33

I. What?

|

II. Global Context

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

Índex Sintètic de Desenvolupament / Vulnerabilitat Social de Barcelona

The Social vulnerability index of Barcelona, is a support tool to measure, assess and monitor inequalities in different areas of the city. This index summarizes the status of each analyzed geographical area combining three dimensions of development and vulnerability: Health, Education and Economics. Área Metropolitana de Barcelona : Desarrollo Socioeconomico

The index is constructed at neighborhood level as they are commonly used to collect and analyze data for policy and planning in city council.

Block level scale is used to refine the understanding of blocks with high potential of development. There are 73 neighbourhoods, of varying sizes, in the metropolitan area of Barcelona.

These indicators, mapped using the GIS system, are further analysed using statistical methods The socio-economic development of box plotting with data retrieved from the 2020 department of the Area of metropolitan Barcelona database of Open Data BCN. (AMB hereafter) provides different social services to citizens along with creating and monitoring

A boxplot, also called a box and whisker socio-economic policies. The department deals plot, is a way to show the spread and centers of a with areas of real estate, economic reactivation, data set (Figure 1A). Measures of spread include energy poverty, circular economy, food services and the interquartile range and the mean of the data set. investment. It also focuses on policies of residential Measures of the center include the mean or average vulnerabilities, housing, social care, coexistence and and median. Following aspects are included in the security, vocational training, and labour market. plot:

To determine the performance and 1: The minimum, the smallest number in the data inequalities, the thesis draws hints from states of set, is shown at the far left of the chart, at the end of the art, current and historic projects and policies the left “whisker.” of Barcelona, and develops a Socio-Economic 2: First quartile, Q1, is the far left of the box, or the Vulnerability Index (SEVI, hereafter). This index far right of the left whisker, is the median of the data is used as a tool to measure and compare all points to the left of the median. . neighbourhoods within the city. The domains that 3: The median is shown as a line in the center of the form the basis of RE{CODE}’s SEVI are social box. indicators of population density (Figure 21), 4: Third quartile, Q3, shown at the far right of the percentage of migrant population (Figure 22), average box (at the far left of the right whisker) is the median life expectancy (Figure 23), green space per capita of the data points to the right of the median..

(Figure 24), available education facilities (Figure 5: The maximum, the largest number in the data set, 25), ratio of commercial to residential activities shown at the far right of the box.

(Figure 26), household area per capita (Figure 27) and economic indicators of available amenities per capita (Figure 28), percentage population with higher education (Figure 29), percentage of inactive spaces (Figure 30), percentage of employable demography (Figure 31) , unemployment percentage (Figure 32) and available family income per capita (Figure 33).

When determining the location of vulnerable population groups, the use of a geographic scale sufficient to discern demographic differences is important (CDC 2011). For Barcelona, two scales Figure 1A. are used, neighborhood level and block level.

Box plot diagram

34

RE{CODE}

| XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection

Maximum Q3 + (1.5*IQR)

|

Outliers Methodology

X. Stakeholders

Q3

|

Interquartile Range (IQR)

Median

IX. Protocol

Q1

Dataset over the map is classified with different methods to distinguish areas with the same discrete value or areas with values in a particular range. The methods used to distribute data points for the abovementioned 13 indicators are equal quantiles and equal counts Equal quantiles is a method where a certain number of categories with an equal number of units in each category. Equal counts is a method where the bounds of the dataset are divided with a certain number, and the data points are categorized within the divided ranges.

Equal counts are typically used when the ranges are determined based on the distribution of values giving an understanding of a weighted distribution. For example, in an equal count distribution of 4 ranges for population density in buildings with 100 data points and every quantile with 25 points, the first range can be 20-60 and the second can be 60-65. This means the probability of a building with density 60-65 is higher than ones with density 20-60.

Equal quantile method is used when data values within specific ranges are to be analysed.

For example, in an equal quantile distribution of 4 ranges, between the bounds of 0 to 20, for the Air Quality Index dataset (AQI) of 100 cities, there can be 50 cities with AQI range 0-5 as compared to 10 cities with AQI range 5-10. This means the probability of the cities with AQI of 0-5 is much higher than cities with AQI of 5-10.

|

Minimum Q1-(1.5*IQR)

VIII. Framework

Box plot of the datasets helps understand extremes, averages and outliers of the distributed values. An outlier is a data point that differs significantly from other observations. Based on the type of data, outliers and extremes can either signify ideal or vulnerable values. For example, higher extremes in a dataset that measures the number of reported crimes would signify vulnerability of those data points. As opposed to a dataset that measures average crop yield, higher extremes and outliers would signify ideal values.

Outliers

35

Population density (People per square hectare of land area)

Population density is midyear population divided by land area in square hectare. Population is based on the ‘de facto’ definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country’s total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones.

In most cases the definition of inland water bodies includes major rivers and lakes.

The data is classified in 5 equal count ranges (Figure 34). The maximum density is in the range of 900 to 1371 inhabitants per hectare, and minimum is in the range of 19-331 inhabitants per hectare. The average density calculated is 650 inhabitants per hectare. In case of population density, vulnerable areas tend to be values higher than the average.

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

V. State of Art

Figure 34.

1417

1371

901

718

557

41 19

IV. Scientific Interest |

Population Density box plot

Figure 21.

|

II. Global Context

|

III. Barcelona Context |

Population Density GIS map

I. What?

Average Density

36

19-331 / ha 331-633 / ha 633-752 / ha 752-900 / ha 900 - 1371 / ha

RE{CODE}

IX. Protocol

The data is classified in 5 equal count ranges (Figure 35). The maximum percentage is in the range of 7-14 % ,and minimum is in the range of 25-62% migrant population. In the case of the percentage of migrant population, less inclusive demography tends to be higher than 25%.

| |

Migrant population is the ratio of the number of people born in a country against the ones born other than that in which they live. It also includes refugees. The data used to estimate the migrant stock at a particular time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population, people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign populations, that is, people who are citizens of a country other than the country in which they reside, are used as estimates.

VIII. Framework

Percentage of migrant population

X. Stakeholders

Figure 35.

|

Percentage of migrant population box plot

XI. Platform

61.92

36.85

24.1

17.9

15.6

6.17

2.85

Figure 22. | ........... | RE{CODE} |..........

Percentage of migrant population GIS map

XIII. Global outlook | |

Methodology

XIV. Reflection

Less Inclusive demography

7-14 % 14-16 % 16-21 % 21-25 % 25-62 %

37

Average life expectancy Life expectancy indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

The data is classified in 5 equal quantiles (Figure 36). The maximum ratio is in the range of 79-81 ,and minimum is in the range of 87-89 years.

In the case of the average life expectancy, lower ratios tend to be less than 83 years.

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

V. State of Art

Figure 36.

88.8

87.3 85.5 84.7 83.3

80

0

IV. Scientific Interest |

Average life expectancy box plot

Figure 23.

|

II. Global Context

|

III. Barcelona Context |

Average life expectancy GIS map

I. What?

Low Ratio

38

N/A 80-81 81-83 83-85 85-87 87-88

RE{CODE}

IX. Protocol

The data is classified in 5 equal count ranges (Figure 37). The minimum is in the range of 0 - 0.003 sqm, and maximum is in the range of 0.315 sqm. In the case of area per capita, European standards specify an ideal number of 9 sqm. Hence neighbourhoods with a ratio below 9 sqm per inhabitant are more vulnerable.

| |

Urban green space is open-space areas reserved for parks and other green spaces, including plant life, water features and other kinds of natural environment. Most urban open spaces are green spaces, but occasionally include other kinds of open areas. The landscape of urban open spaces can range from playing fields to highly maintained environments to relatively natural landscapes.

Green space per capita is the ratio of aggregated area in square meters of above mentioned urban green spaces to number of inhabitants.

VIII. Framework

Green space per capita

X. Stakeholders

Figure 37.

|

Green space per capita box plot

XI. Platform

355.93

35.4

15.9 6.1 2.9 0.29

-16.6

Figure 24. | ........... | RE{CODE} |..........

Green space per capita GIS map

XIII. Global outlook | XIV. Reflection

Methodology

|

0-0.003 m2 0.003-0.014 m2 0.014-0.05 m2 0.05-0.3 m2 Low Ratio 0.3-15 m2

39

|

Education facilities include primary, secondary and tertiary level schools, higher education universities and training schools. The ratio is taken as the number of education facilities per 100 inhabitants, between the ages of 0 to 30.

The data is classified in 5 equal count ranges (Figure 38). The minimum is in the range of 2-6, and maximum is in the range of 31-60 education facilities per 100 inhabitants. In this case, 9 facilities per 100 inhabitants is considered insufficient.

VI. How?

|

Available education facilities per capita

VII. Methodology

Nihar Mehta

|

Figure 38.

59.6

5.45

2.6 1.3 0.7 0.19

-2.15

IV. Scientific Interest |

V. State of Art

Available education facilities per 100 inhabitants box plot

Figure 25.

|

II. Global Context

|

III. Barcelona Context |

Available education facilities per 100 inhabitants GIS map

I. What?

Low Ratio

40

2-6 6-10 10-17 17-31 31-60

RE{CODE}

IX. Protocol

The data is classified in 5 equal quantiles (Figure 39). The minimum ratio is in the range of multiplication factor 0-3 number of commercial activities to residential, and the maximum is in the range of 12-63 times. In this case lower than the multiplication factor 3 means deficiency in commercial activities, and factor of more than 12 means disproportionate concentration of commercial activities.

|

Commercial activities include offices, small and medium businesses, shopping facilities and other places of business. Residential activities include temporary and permanent housing facilities.

The ratio is measured as the number of commercial facilities to the number of residential facilities.

VIII. Framework

Ratio of commercial to residential activities

| X. Stakeholders

Figure 39.

|

Ratio of commercial to residential spaces box plot

XI. Platform

58.37 57.05

35.3

27.6

20.8

8.51

-0.95

Figure 26. | ........... | RE{CODE} |..........

Ratio of commercial to residential spaces GIS map

XIII. Global outlook |

Methodology

0-3 X 3-6 X 6-9 X 9-12 X 12-63 X

|

Low Ratio

XIV. Reflection

High Ratio

41

|

Household area is the net usable floor area covered by inside and outside areas, including bathrooms, kitchen, terraces and balconies. The ratio is calculated as household area to number of inhabitants.

The data is classified in 5 equal count ranges (Figure 40). The minimum household area per inhabitant is in the range of 20-25 sqm, and maximum is in the range of 34-50 sqm. In this case, the European Environmental Agency specifies 25 sqm of area per inhabitant as being ideal.

|

VI. How?

|

Household area per capita

VII. Methodology

Nihar Mehta

V. State of Art

Figure 40.

50.4

42.35

32.6

29.4

26.1

20.3

16.35

IV. Scientific Interest |

Household area per capita box plot

Figure 27.

I. What?

|

II. Global Context

|

III. Barcelona Context |

Household area per capita GIS map

Higher than EEA mandate

42

20-25 m2 25-29 m2 29-31 m2 31-34 m2 34-50 m2

RE{CODE}

IX. Protocol

The data is classified in 5 equal count ranges (Figure 41). The minimum number of amenities per 1000 inhabitants is in the range of 2-20, and maximum is in the range of 1450-1900. In this case, 20 units of amenities per 1000 inhabitants is the measured low extreme and highlights its deficiency for the neighbourhood’s inhabitants.

|

Urban amenities means urban facilities such as parks, playgrounds, parking facilities, public bus transport, libraries, affordable hospitals, cultural centres, recreation centres, stadiums, sports complexes, restaurants and cafes, and department stores.

VIII. Framework

Available amenities per 1000 capita

| X. Stakeholders

Figure 40.

|

Available amenities per 1000 inhabitants box plot

XI. Platform

9.02

4.85

3.2

2.6

2.1

0.79

0.45

Figure 28. | ........... | RE{CODE} |..........

Available amenities per 1000 inhabitants GIS map

XIII. Global outlook | |

Methodology

XIV. Reflection

Low Ratio

2-20 20-440 440-730 730-1450 1450-1900

43

Percentage population with higher education

Population with higher education is defined as those having completed the highest level, at least tertiary level, of education, by age group.

This includes both theoretical programs leading to advanced research or high skill professions such as medicine and more vocational programs leading to the labour market. The measure is the percentage of the same age population, also available by gender.

The data is classified in 5 equal quantiles (Figure 42). The minimum percentage of the population with higher education is in the range 8-12.5%, and the maximum is in the range of 27 31.5%. In this case lower than 21 % highlights the low skill level of the population hinting towards a deficiency.

VI. How?

|

VII. Methodology

|

Nihar Mehta

|

Figure 42.

38.85

30.27

25.2

20.8

15.1

8.02

2.45

IV. Scientific Interest |

V. State of Art

Percentage population with higher education box plot

Figure 29.

|

II. Global Context

|

III. Barcelona Context |

Percentage population with higher education GIS map

I. What?

Low percentage

44

8 - 12.5 % 12.5 - 17 % 17 - 21.5 % 21.5 - 27 % 27 - 31.5 %

RE{CODE}

IX. Protocol

The data is classified in 5 equal quantiles (Figure 43). The minimum percentage of inactive spaces is in the range of 9-18 % ,and maximum is in the range of 48-58 %. In the case of the percentage of inactive spaces, 25% or above is a high rate of economic inactivity and inefficient use of resources.

|

Number of commercial spaces that have reported financial loss for over a period of 12 months and/or have been non-operational for over a period of 6 months are considered as inactive spaces. This data is collected as part of the census survey.

VIII. Framework

Percentage of Inactive spaces

| X. Stakeholders

Figure 43.

|

Percentage of inactive spaces box plot

XI. Platform

58.37 57.05

35.3

27.6

20.8

8.51

-0.95

Figure 30. | ........... | RE{CODE} |..........

Percentage of inactive spaces GIS map

XIII. Global outlook | |

Methodology

XIV. Reflection

High Percentage

9-18 % 18-28 % 28-38 % 38-48 % 48-58 %

45

Percentage of employable demography

The employable demography is the working age population, defined as those aged 16 to 64. This indicator measures the share of the working age population in total population.

The data is classified in 5 equal count ranges (Figure 44). The minimum percentage of employable demography is 59-70%, and maximum is in the range of 79-94%. In this case, lower than 70 % signifies a higher number of old and unemployable demography.

VI. How?

|

VII. Methodology

|

Nihar Mehta

|

Figure 44.

76.2

72.6

66.3 64.7

62.1

58.18 55.8

IV. Scientific Interest |

V. State of Art

Percentage of employable demography (age 16-64) box plot

Figure 31.

|

II. Global Context

|

III. Barcelona Context |

Percentage of employable demography (age 16-64) GIS map

I. What?

Low Percentage

46

59-70 % 70-72 % 72-74 % 74-79 % 79-94 %

RE{CODE}

IX. Protocol

The data is classified in 5 equal quantiles (Figure 45). The minimum percentage of unemployment is in the range 3-5 %, and the maximum is in the range of 8-13%. In this case higher than 10 % unemployment in the neighbourhood highlights the population in need of economic support.

| | X. Stakeholders

The unemployed are people of working age who are without work, are available for work, and have taken specific steps to find work. The uniform application of this definition results in estimates of unemployment rates that are more internationally comparable than estimates based on national definitions of unemployment. This indicator is measured in numbers of unemployed people as a percentage of the labour force and it is seasonally adjusted. The labour force is defined as the total number of unemployed people plus those in employment. Data are based on labour force surveys (LFS).

VIII. Framework

Unemployment percentage

Figure 45.

|

Unemployment percentage box plot

XI. Platform

12.55

11.2

7.9

6.5

5.7

2.87

2.4

Figure 32. | ........... | RE{CODE} |..........

Unemployment percentage GIS map

XIII. Global outlook | |

Methodology

XIV. Reflection

High Percentage

3-5 % 5-6 % 6-7 % 7-8 % 8-13 %

47

Available family income per capita

Disposable income is closest to the concept of income as generally understood in economics.

Household disposable income measures the income of households (wages and salaries, self-employed income, income from unincorporated enterprises, social benefits, etc.), after taking into account net interest and dividends received and the payment of taxes and social contributions. Net signifies that depreciation costs have been subtracted from the income presented. Household gross adjusted disposable income is the income adjusted for transfers in kind received by households, such as health or

education provided for free or at reduced prices by government and NPISHs (Non Profit Institutions Serving Households). All OECD countries compile their data according to the 2008 System of National Accounts (SNA 2008).

The data is classified in 5 equal quantiles (Figure 46). The minimum disposable income is in the range 35-60 Euros per person, and the maximum is in the range of 150-250 Euros per person. In this case lower than 80 Euros of disposable income per person is considered financially unsustainable.

Figure 46.

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

255

165

108

87

64

38

IV. Scientific Interest |

V. State of Art

Available family income per capita box plot

Figure 33.

|

II. Global Context

|

III. Barcelona Context |

Available family income per capita GIS map

I. What?

Low Ratio

48

35 - 60 60 - 80 80 - 100 100 - 150 150 - 250

RE{CODE}

To construct the SEVI, the 13 indicators are Constructing SEVI - A measure for ranked for across all neighborhoods in Barcelona evaluating performance

XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Methodology

|

There are different ways to construct an index. In general, there are 2 main classifications: Simple and Weighted. The simple method is classified into simple aggregative and simple relative. Similarly, the weighted method is classified into weighted aggregative and weighted average or relative. Indexes calculated in different fields, such as economics, engineering, physical sciences, biochemistry and many others, have been calculated differently based on the type of data and use case.

For this thesis, the percentile ranking method developed by Centers for Disease Control and Prevention (CDC) is adopted. This method was used

by CDC to create their Social Vulnerability index (SVI) to indicate the relative vulnerability of every U.S. Census tract, and help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event. The thesis follows the steps and mathematical operations used in the percentile rank method.

where N = the total number of data points, and all sequences of ties are assigned the smallest of the corresponding ranks (CDC 2019). SEVI of a city is calculated as the average of neighborhoods SEVI. Table xx shows the calculated SEVI of the neighborhoods.

The map (Figure 47) and the table (Table 1) shows the calculated SEVI of neighborhoods in relation to each other, helping the identification of deficient areas. 0 signifies areas with relatively low vulnerability and 1 signifies areas with relatively high vulnerability.

X. Stakeholders

Constructing the SEVI

|

Percentile Rank = (Rank-1) / (N-1)

IX. Protocol

with a non-zero population (N = 73). Average life expectancy, green space per capita, available education facilities, household area per capita, available amenities per capita, percentage population with higher education and available family income per capita are ranked from lowest to highest as higher value indicates lesser vulnerability. Population density, percentage of migrant population, ratio of commercial to residential activities, percentage of inactive spaces and unemployment percentage are ranked from highest to lowest because, unlike the other variables, a lower value indicates lesser vulnerability.

A percentile rank is then calculated for each neighborhood over each of these variables. A percentile rank is defined as the proportion of scores in a distribution that a specific score is greater than or equal to. Percentile ranks were calculated by using the formula :

|

Observing the maps and box plots of the indicators gives us information and hints towards deficiencies of different urban areas for those parameters separately. Although the same area or neighbourhood can be vulnerable for one, or some parameter, but not for others. Similarly, there will be datasets with different bounds (Data as varied ranges of number of a particular unit, percentages and ratios) and different value extremes (high value being undesirable for some, whereas low value being undesirable for others). Measuring the areas and dataset in one unified range of numbers helps correlate the different indicators. This thesis develops a Socio-Economic Vulnerability Index (SEVI), as from the indicators analysed. An index is a composite statistic, a measure of changes in a representative group of individual data points, or in other words, a compound measure that aggregates and ranks multiple indicators.

VIII. Framework

3.2.1

49

| VII. Methodology | VI. How? | V. State of Art IV. Scientific Interest | III. Barcelona Context |

low vulnerability{0}

|

high vulnerability{1}

II. Global Context

Figure 48.

|

Socio-Economic Vulnerability index GIS mapping

I. What?

Table 1.

Socio-Economic Vulnerability index values

50

N.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

3 0.65 4 0.69 5 0.60 6 0.33 Nihar7 Mehta 0.38 8 0.00 9 0.25 10 0.52 11 0.73 12 1.00 13 0.14 14 0.93 15 0.26 16 0.44 17 0.19 18 0.58 19 0.57 20 0.56 21 0.65 22 0.57 23 0.40 24 0.40 25 0.72 26 0.43 27 0.36 28 0.69 29 0.58 30 0.66 31 0.10 32 0.06 33 0.23 34 0.82 35 0.48 SEVI N.No 36 SEVI 0.26 0.56 1 0.74 0.56 37 0.54 2 0.54 38 0.41 0.65 3 0.65 39 0.61 0.69 4 0.69 40 0.64 0.60 5 0.60 41 0.46 0.33 6 0.33 42 0.85 0.38 7 0.38 43 0.59 0.00 8 0.00 44 0.34 0.25 9 0.25 45 0.93 0.52 10 46 0.52 0.70 0.73 11 47 0.73 0.57 1.00 12 48 1.00 0.63 0.14 13 49 0.14 0.85 0.93 14 50 0.93 0.97 0.26 15 51 0.26 0.55 0.44 16 52 0.44 0.65 0.19 17 53 0.19 0.98 0.58 18 54 0.58 0.74 0.57 19 55 0.57 0.43 0.56 20 56 0.56 0.40 0.65 21 57 0.65 0.81 0.57 22 58 0.57 0.87 0.40 23 59 0.40 0.69 0.40 24 60 0.40 0.50 0.72 25 61 0.72 0.76 0.43 26 62 0.43 0.32 0.36 27 63 0.36 0.59 0.69 28 64 0.69 0.73 0.58 29 65 0.58 0.60 0.66 30 66 0.66 0.69 0.10 31 67 0.10 0.54 0.06 32 68 0.06 0.12 0.23 33 69 0.23 0.40 0.82 34 70 0.82 0.78 0.48 35 71 0.48 0.54 36 0.26 72 0.26 0.71 37 0.74 73 0.74 0.66 38 0.41 0.41 39 0.61 0.61 40 0.64 0.64 41 0.46 0.46 RE{CODE} 42 0.85 0.85 43 0.59 0.59 44 0.34 0.34

Statistical analysis of data

| X. Stakeholders | XI. Platform

Standard deviation for indicators of population density, green space per capita, average family income available, available amenities, household area per capita and available education facilities (Figure 49, Figure 50, Figure 51, Figure 52, Figure 53, Figure 54) were used to measure the magnitude. Values and standards specified by city council were used as measures for the remaining indicators (Table 2).

IX. Protocol

The standard deviation is a measure of the amount of variation or dispersion of a set of values.

It is calculated as the square root of variance by determining each data point’s deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.

Figure 48 shows an example of a bell curve diagram of standard distribution.

Area highlighted in dark pink is one standard deviation on either side of the mean. For the normal distribution, this accounts for 68.27 percent of the set; while two standard deviations from the mean (medium and dark pink) account for 95.45 percent;

|

SEVI gives an overview of vulnerable neighbourhoods, but determining the magnitude of vulnerability of the indicators helps identify extremes, outliers and specific deficiencies. Statistical method of standard deviation was opted for this.

three standard deviations (light, medium, and dark pink) account for 99.73 percent; and four standard deviations account for 99.994 percent. The two points of the curve that are one standard deviation from the mean are also the inflection points; while two standard deviations from the mean (medium and dark pink) account for 95.45 percent; three standard deviations (light, medium, and dark pink) account for 99.73 percent; and four standard deviations account for 99.994 percent. The two points of the curve that are one standard deviation from the mean are also the inflection points.

VIII. Framework

3.2.2

Figure 33. |

Bell Curve Diagram

........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Methodology

51

|

Nihar Mehta

|

VII. Methodology

1371

19

0

9

0

VI. How?

95

69

35

33

|

Figure 49.

Population density

IV. Scientific Interest |

V. State of Art

355.9

0.3

III. Barcelona Context |

.9

17

9

Figure 50.

Greenspace per capita

II. Global Context

|

248.8

38.6

|

.5

93

80

I. What?

Figure 51.

Available disposable family income

52

RE{CODE}

VIII. Framework

9

| IX. Protocol

0.8

9

2.

2

|

Figure 52.

X. Stakeholders

Available amenities per 100 inhabitants

50.4

| XI. Platform

20.2

.7

29

| ........... | RE{CODE} |..........

Figure 53.

Household area per capita 59.6

XIII. Global outlook |

0.2

XIV. Reflection

20

8

2.

Figure 54.

Available education facilities per 100 inhabitants |

Methodology

53

Table 2. Values specified by city council for indicators Indicators

- 2 deviation

+ 2 deviation

1

Percentage of migrant population

50 %

NA

2

Average life expectancy

NA

80

3

Ratio of commercial to residential area

50

5

4

Percentage of inactive spaces

40 %

NA

5

Percentage population with higher education

NA

20 %

6

Percentage of employable demography (age 16-64)

NA

70 %

7

Unemployment percentage

8%

NA

3.3

Protocol stage

performance of neighbourhoods in relation to each other.

Mapping the SEVI with statistical analysis (Figure 55) highlights the vulnerable areas and chronic deficiencies of the most vulnerable neighbourhood, against Barcelona’s average values (Table 3).

The analysis stage comprises GIS mapping, statistical analysis and the vulnerability index proved useful in understanding patterns in the city with different parameters and categorizing the

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

VI. How?

Sr. No.

|

|

VII. Methodology

|

Nihar Mehta

42

21

20

32

63 73

07

II. Global Context

51

26

17

|

48

71

67

|

02

I. What?

Figure 55.

Vulnerable neighborhoods

54

RE{CODE}

VIII. Framework

Having done the analysis in determining the vulnerable neighbourhood, scaling into block level, further analysis is carried out to determine redevelopment potential of blocks.

Table 3. Deficiencies in neighbourhoods

|

N No. Density

BCN 689.4 20.6%

Green space per capita

81.8

2.9

9.2

29.9

20.1%

2.8

28.3%

93.7

64.5%

6.8%

17.9

546

61.9%

81.8

0.2

15.2

30.8

18.8%

9

17.4%

106.1

71.2%

5.6%

4.6

07

382

25.4%

83.7

0.4

2.5

48.1

11.3%

6.6

10%

175.9

66.2%

4.2%

1.1

17

969

21%

85.5

0.6

3.3

28.2

21%

2.8

32.9%

81

64.8%

5.2%

0.3

20

757

13.7%

87.3

0.7

6.2

33.7

15.2%

2.2

22.3%

114.2

60.7%

5.8%

6.6

26

499

14.1%

86

0.9

9.3

50.4

9.3%

4.2

14.7%

192.1

62.1%

3.5%

2.9

67

380

19.4%

87.2

4.4

43.2

35

10.6%

2.8

16.4%

164.2

69%

5.9%

14.6

73

721

14.8%

84.4

59.6

16.8

29.5

26.2%

2.6

28.1%

57

60.9%

8.3%

3.9

|

02

IX. Protocol

% Average Education Commercial House % with Amenities % Family % % unemmigrant life facilities to area higher per 1000 inactive income employ- ployment expectancy per 100 residential per education spaces per able activities capita capita

X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Methodology

55

3.3.1

Determining blocks in the city for cost of maintenance and older buildings have lower economic value and benefits. The calculated index redevelopment

Block level analysis helps filter blocks within the vulnerable neighbourhoods which can be used as potential sites for the urban interventions to improve socio-economic deficiencies. The potential to develop an intervention over any given block is defined in the research as the redevelopment potential. This is calculated into an index of underutilization of a block in terms of available and existing facilities (Figure 56), available and existing amenities (Figure 49) and building population density (Figure 57), against the factor of high cost of operation , in terms of building age (Figure 58) and cost of maintenance (Figure 59).

of potential of blocks, in relation to each other, is mapped over the city (Figure 53) and in the vulnerable neighbourhoods (Figure 54). 0 signifies blocks with relatively low redevelopment potential and 1 signifies blocks with relatively high potential.

The redevelopment index is calculated using the percentile rank method (CDC 2019). Indicators of underutilization of blocks are ranked from highest to lowest. Indicators of high cost of operation are ranked from lowest to highest, because a higher Available facilities

and

existing

This is a measure of the number of facility units in each block. Facilities include clinics, hospitals and other medical facilities, service shops, parking and essential commodity shops.

The data is classified in 3 equal count ranges (Figure 56). The minimum number of facility units per building is in the range 0-2, and the maximum is in the range of 50-350.

|

II. Global Context

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

I. What?

50 - 350 2 - 50 0-2 N/A

56

Figure 56.

Available and existing facilities GIS map RE{CODE}

and

existing

| IX. Protocol | X. Stakeholders

This is a measure of the number of amenity units in each block. Amenities include department stores, grocery markets, daily supply stores, parks, recreation areas and sport facilities.

The data is classified in 3 equal count ranges (Figure 57). The minimum number of amenities units per building is in the range 0-10, and the maximum is in the range of 50-210.

VIII. Framework

Available amenities

|

Figure 57.

XI. Platform

50 - 210 10 - 50 0 - 10 N/A

Available and existing amenities GIS map Building population density

| ........... | RE{CODE} |..........

XIII. Global outlook

This is the density of the aggregated number of temporary and permanent inhabitants per hectare area of block.

The data is classified in 5 equal quantiles (Figure 58). The minimum density is in the range of 0-1000 inhabitants per hectare, and the maximum is in the range of 15000-100000.

|

Figure 58.

Building population density GIS map

|

Methodology

XIV. Reflection

15000 - 100000 /ha 10000 - 15000 /ha 5000 - 10000 /ha 1000 - 5000 /ha 0 - 1000 /ha

57

Nihar Mehta

|

Building age

V. State of Art

|

VI. How?

|

VII. Methodology

This is the average of the number of operational years, since its construction year, of the buildings in the block.

The data is classified in 5 equal quantiles (Figure 59). The minimum number of years is in the range of 0-30 years, and the maximum is in the range of 130-170 years.

IV. Scientific Interest |

130 - 170 years 100 - 130 years 70 - 100 years 30 - 70 years 0 - 30 years

Figure 59.

Building age GIS map Building maintenance cost

|

II. Global Context

|

III. Barcelona Context |

Maintenance cost is the cost which is incurred on a monthly basis for the upkeep of the property.

This includes maintenance of the building/s and the amenities.

The data is classified in 5 equal quantiles (Figure 60). The minimum range being in the range 0-150 Euros/month, and the maximum is in the range of 600-700 Euro/month.

I. What?

N/A 600 -700 €/month 450 - 600 €/month 300 - 450 €/month 150 - 300 €/month <150 €/month

58

Figure 60.

Building maintenance cost GIS map RE{CODE}

VIII. Framework | IX. Protocol | X. Stakeholders |

low potential{0}

XI. Platform

high potential{1}

Figure 61.

Redevelopment potential index GIS map

| ........... | RE{CODE} |..........

XIII. Global outlook |

Methodology

Figure 62.

Redevelopment potential index in vulnerable neighbourhoods GIS map

|

high potential{1}

XIV. Reflection

low potential{0}

59

3.3.2

Determining value improvement, activities, and provides practical ways to unlock more social value for communities.It has been opportunities and proposals

Intervention proposals that can improve socio-economic equality in cities can be devices using the different scales of analysis done. Social along with economic value improvement and intervention, or combination of interventions has the potential to hint towards the type of design proposal required. The research follows the National TOMs Measurement Framework (Social Value Portal 2021) to understand and determine the themes, outcomes and measures of different interventions.

The TOM framework is designed to embed social value into their procurement measurement

Sr. No.

Economic

Social

1

Creating direct jobs

Training opportunities

2

Creating jobs for unemployed, not in education or training

Promotion of skills and employment

3

Employability support for people over 24

Supporting health and wellbeing

4

Spend of voluntary, community and social enterprises

Reduction of carbon emissions

5

Advice and support to VCSEs and MSMEs

Saving car miles

The following catalogue (Table 5) of possible proposals is made based on the type of intervention and corresponding opportunity that can improve social and economic values. Implementing these proposals can create a better value in the neighborhood, thereby mitigating deficiencies.

Table 5. Catalogue of proposals Sr. No.

Economic

Social

1 2 3 4

Addition of amenities Addition of incubators and training facilities Addition of spaces for local businesses Addition of education and learning facilities

Introduction of co-housing Addition of green spaces Addition of community spaces Addition of co-working spaces

I. What?

II. Global Context

Different interventions with different programs can thus be added to different urban areas with different specific vulnerabilities. Although different vulnerabilities and their magnitudes would require one or multiple interventions. The research develops a matrix of correlation which determines the addition of interventions corresponding to the presence of a vulnerability or a combination of vulnerabilities in a given urban area, neighborhood in case of Barcelona. The correlation in terms of intervention programs (Figure 63) of is as detailed below:

|

|

III. Barcelona Context |

created with input from more than 120 organisations across sectors, led by the National Social Value Task Force. The term social value is in effect defined through the Well-beingof Future Generations Act (Wales, 2015) which requires public bodies in to think about the long-term impact of their decisions, to work better with people, communities and each other, and to prevent persistent problems such as poverty, health inequalities and climate change.

Based on the indicators investigated through the research along with National TOPs Measurement Framework, themes of interventions required can be extrapolated (Table 4).

Table 4. Types of interventions for value improvement

IV. Scientific Interest |

V. State of Art

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

60

RE{CODE}

Population density

% of migrant population Average life expectancy

% of migrant population Co-housing Passive green spaces

Average life expectancy Education facilities/100

|

Education facilities/100

VIII. Framework

Population density

Community spaces

Area of house/cap % pop. with higher education

Co-working Active green spaces Local businesses

Area of commerce to housing Area of house/cap

IX. Protocol

Area of commerce to housing

% pop. with higher education

Education facilities

Amenities/100

% of inactive spaces

Temporary spaces

% of inactive spaces

|

Amenities/100

% of employability {age 16-64) % of unemployment

Amenities Incubators and training

Available disposable income % of employability {age 16-64)

X. Stakeholders

Available disposable income

% of unemployment

|

Green space/cap

Green space/cap

XI. Platform

Figure 63.

Matrix of correlation

XIII. Global outlook | XIV. Reflection |

61

........... | RE{CODE} |..........

Methodology

|

Addition of Co-housing spaces : Low with higher education and high green space/capita. population density, low average life expectancy, high education facilities and high number of commerce.

Addition of spaces for local businesses : Low number of population with higher education, low percent of inactive spaces, low available disposable

Addition of Green spaces : Low green space/ income, low percentage of unemployment, high capita, low number of amenities, low available population density, high average life expectancy, disposable income/capita, high population density, high percentage of unemployment and high green high average life expectancy, high area of house/ spaces/capita. capita, percentage with higher education and high percentage of inactive spaces.

Addition of education facilities : Low number of education facilities, high number of employable

Addition of Community spaces : Low and population and high percentage of unemployment. high number of education facilities, low and high number of commercial activities, low and high area Addition of amenities : Low population of house/capita, low percentage of population with density, low number of amenities and high green higher education, high population density, low and spaces/capita. high percentage of migrant population and high average life expectancy.

Addition of incubator and training spaces : Low and high number of education facilities, low

Addition of Co-working spaces : Low and and high percentage of employable population, high number of commercial activities, low and high high population density, high percentage of migrant percentage of employable population, high number population, high available disposable income, high of education facilities, high number of population . percentage of employment , high green space/capita.

To elaborate on this method of selection, 3 vulnerabilities (Table 6) and the corresponding value neighborhoods are taken as example, number 02, improvements required followed by determining the 07 and 73 (Figure 56), to examine their analyzed proposals (Table 7) that can be most beneficial to the neighbourhoods, using the correlation matrix.

Figure 64.

Vulnerable neighbourhoods - 02, 07, 73

V. State of Art

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

IV. Scientific Interest |

02

Vulnerable sites

Table 7. Analyzed vulnerability measures in neighbourhoods - 02, 07, 67 Indicators

I. What?

|

II. Global Context

|

III. Barcelona Context |

73

07

BCN

N. 02

N. 07

N. 73

Population density

689.4

546

382

721

Percentage of migrant population

20.6 %

61.9 %

25.4 %

14.8 %

Average life expectancy

81.8

81.8

83.7

84.4

Available education facilities per 100

2.9

0.2

0.4

59.6

Ration of commercial to residential spaces

9.2

15.2

2.5

16.8

Household area per capita

29.9

30.8

48.1

29.5

Percentage of population with higher education

20.1 %

18.8 %

11.3 %

26.2 %

Available amenities per 100

2.8

9

6.6

2.6

Percentage of inactive spaces

28.3 %

17.4 %

10 %

28.1 %

Average family income per capita

93.7

106.1

175.9

57

Percentage of employable demography (age 16-64)

64.5 %

71.2 %

66.2 %

60.9 %

Unemployment percentage

6.8 %

5.6 %

4.2 %

8.3 %

Green space per capita

17.9 m

4.6 m

1.1 m

3.9 m2

2

2

2

Deficient value

62

RE{CODE}

| IX. Protocol

In addition to determining the intervention programs, the research develops a method to quantify the areas of the intervention programs.

Although, in the course of development, it was evident that a specific formula to derive the areas would require an extensive investigation into the relationship of area to value improvement.

For the purposes of this research, a subjective

approach was opted taking hints from case studies conducted under the previously discussed Pla de Barris program.

Within the calculations, the standard deviation is remapped, defined as Multiplier, into a domain of 0 to 2, values closer to 0 are values closer to the mean value in the bell curve and values closer to 2 are values with more deviation in both, positive and negative, directions of the mean. Table 8 shows the Multiplier values.

VIII. Framework

Area calculation of programs

Table 7. Calculated values of multiplier co-efficient for each program

X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Methodology

|

N.No Co-Housing Passive Green Areas Community Spaces Co-Working Spaces Active Green spaces Local Business spaces Education spaces Amenity spaces Incubator Spaces 1 0.10 0.55 1.09 0.89 0.70 0.70 0.87 1.01 1.00 2 0.10 0.33 1.00 0.95 0.10 0.40 0.75 0.87 0.70 3 0.10 0.33 0.95 0.92 0.70 0.40 0.90 1.04 0.70 4 0.10 0.10 0.84 0.83 0.10 0.10 0.77 0.52 0.40 5 0.10 0.33 1.00 0.64 0.10 0.40 0.67 0.99 0.40 6 0.10 0.33 0.98 0.64 0.10 0.40 0.68 0.97 0.40 7 0.55 0.55 0.56 0.49 0.40 0.70 0.36 0.37 0.40 8 0.10 0.33 0.72 0.58 0.10 0.40 0.54 0.61 0.40 9 0.10 0.33 0.79 0.61 0.10 0.40 0.64 0.67 0.40 10 0.10 0.33 0.79 0.63 0.10 0.40 0.73 0.60 0.40 11 0.10 0.33 0.99 0.70 0.40 0.10 0.82 0.87 0.40 12 0.55 0.33 0.43 0.35 0.70 0.70 1.06 0.01 0.70 13 0.10 0.33 0.94 0.62 0.40 0.10 0.88 0.72 0.40 14 0.10 0.33 0.95 0.63 0.10 0.40 0.86 0.67 0.40 15 0.10 0.55 0.89 0.70 0.40 0.40 0.77 0.68 0.40 16 0.10 0.55 0.86 0.61 0.40 0.40 0.78 0.68 0.40 17 0.10 0.55 1.00 0.63 0.40 0.40 0.72 1.04 0.40 18 0.10 0.55 0.90 0.65 0.40 0.40 0.76 0.76 0.40 19 0.10 0.33 0.83 0.52 0.10 0.40 0.66 0.74 0.40 20 0.10 0.33 0.80 0.53 0.10 0.40 0.69 0.73 0.40 21 0.55 0.33 0.47 0.38 0.40 0.40 0.26 0.38 0.40 22 0.55 0.55 0.56 0.55 0.70 0.40 0.42 0.39 0.40 23 0.55 0.33 0.72 0.39 0.40 0.40 0.28 0.58 0.40 24 0.55 0.55 0.59 0.36 0.40 0.70 0.25 0.57 0.40 25 0.55 0.55 0.67 0.39 0.40 0.70 0.33 0.59 0.40 26 0.10 0.33 0.62 0.40 0.10 0.40 0.32 0.59 0.40 27 0.10 0.33 0.78 0.49 0.10 0.40 0.49 0.70 0.40 28 0.55 0.33 0.82 0.56 0.40 0.40 0.70 0.57 0.40 29 0.10 0.55 0.87 0.64 0.70 0.40 0.71 0.76 0.40 30 0.10 0.10 0.94 0.57 0.10 0.10 0.70 0.76 0.40 31 0.10 0.33 0.76 0.67 0.10 0.40 0.64 0.51 0.40 32 0.10 0.33 0.87 0.59 0.10 0.40 0.72 0.67 0.40 33 0.10 0.33 0.99 0.59 0.10 0.40 0.76 0.89 0.40 34 0.10 0.55 0.94 0.63 0.70 0.40 0.78 0.79 0.40 35 0.10 0.78 0.91 0.61 0.70 0.70 0.76 0.78 0.40 36 0.55 0.78 0.80 0.56 0.70 0.70 0.86 0.45 0.40 37 0.10 0.78 0.98 0.61 0.70 0.70 0.87 0.85 0.40 38 0.10 0.78 0.95 0.64 0.70 0.70 0.81 0.82 0.40 39 0.55 0.78 0.88 0.60 0.70 0.70 0.86 0.59 0.40 40 0.10 0.33 0.91 0.56 0.40 0.10 0.83 0.68 0.40 41 0.10 0.33 0.88 0.53 0.40 0.10 0.82 0.61 0.40 42 1.00 0.78 0.59 0.83 0.70 0.40 0.66 0.72 0.40 43 0.55 0.55 0.94 0.60 0.70 0.40 0.87 0.74 0.40 44 0.10 0.55 0.95 0.56 0.40 0.40 0.85 0.72 0.40 45 0.10 0.55 0.95 0.62 0.40 0.40 0.82 0.84 0.40 46 0.10 0.55 0.97 0.62 0.40 0.40 0.79 0.92 0.40 47 0.55 1.00 0.91 0.62 1.00 1.00 0.87 0.72 0.40 48 0.10 0.10 1.00 0.47 0.40 0.40 0.96 0.76 0.70 49 0.10 0.33 1.00 0.46 0.40 0.70 1.00 0.71 0.70 50 0.10 0.78 1.01 0.69 1.00 1.00 0.95 0.87 0.70 51 0.10 0.55 0.97 0.62 0.70 0.70 0.94 0.76 0.70 52 0.10 0.55 1.08 0.59 0.70 0.70 0.90 1.06 0.40 53 0.10 0.33 0.93 0.68 0.70 0.40 1.08 0.78 0.40 54 0.55 0.55 0.70 0.68 1.00 0.70 0.92 0.52 0.40 55 0.10 0.55 1.07 0.73 0.70 0.70 1.08 0.89 0.40 56 0.55 0.55 0.71 0.50 1.00 0.70 1.04 0.39 0.40 57 0.10 0.55 1.08 0.75 1.00 0.70 1.05 0.95 0.40 58 0.10 0.55 0.86 0.62 0.70 0.40 0.91 0.79 0.10 59 0.10 0.33 0.95 0.67 0.40 0.10 0.90 0.76 0.10 60 0.10 0.55 0.94 0.57 0.40 0.40 0.83 0.77 0.10 61 0.10 0.55 0.88 0.59 0.40 0.40 0.79 0.71 0.10 62 0.10 0.55 0.92 0.60 0.40 0.40 0.78 0.77 0.10 63 0.10 0.33 0.96 0.59 0.10 0.40 0.79 0.93 0.10 64 0.10 0.55 0.91 0.63 0.40 0.40 0.82 0.71 0.10 65 0.10 0.55 0.98 0.64 0.40 0.40 0.84 0.88 0.10 66 0.10 0.10 0.88 0.66 0.10 0.10 0.70 0.71 0.10 67 1.00 0.55 0.88 0.71 0.40 0.40 0.49 0.91 0.10 68 0.10 0.55 0.92 0.64 0.40 0.40 0.72 0.77 0.10 69 0.10 0.10 0.86 0.49 0.10 0.10 0.57 0.72 0.10 70 0.10 0.33 0.91 0.73 0.70 0.40 0.86 0.80 0.40 71 0.10 0.55 0.90 0.56 0.40 0.40 0.74 0.80 0.10 72 0.10 0.33 0.91 0.41 0.10 0.40 0.66 0.87 0.10 73 0.10 0.55 0.87 0.36 0.70 0.70 0.65 0.77 0.40

63

VII. Methodology

|

Nihar Mehta

The calculations done are elaborated below: Addition of Co-housing : | Population of neighbourhood - Median population per neighbourhood of Barcelona | X Multiplier X 25(minimum area of house per capita)

|

Green spaces area :

VI. How?

| [Population density X average green space of Barcelona] - [ Population density X green space of neighbourhood] X Multiplier |

Addition of passive green space :

|

Green space area - [Green space area * Active green space Multiplier]

Green space area X Community Space multiplier Commerce space area : [[ [Median percentage of employable population of Barcelona - Percentage of employable population of neighbourhood + Unemployment percentage] / 100 ] /100 X Co-working Multiplier ] X | Population of neighbourhood + [Population of neighbourhood - Median population per neighbourhood of Barcelona] Addition of spaces for local businesses : Commerce space area X local business Multiplier Addition of incubator and training spaces : [ Commerce space area - Local business area ] X Incubator Multiplier Addition of Co-working spaces : Commerce space area - Local business area - Incubator and training area Addition of education facilities : [ Education facility Multiplier X | Median value of education facilities per 100 of Barcelona - Number of education facilities per 100 of the neighbourhood | ] X 100 Addition of amenities : Amenities Multiplier X | Median value of amenities per 100 of Barcelona - Amenities per 100 of the neighbourhood | X 100

I. What?

|

II. Global Context

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

Addition of Community spaces :

64

RE{CODE}

X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

65

|

Methodology

IX. Protocol

Calculated areas of all programs in sqm in each neighbourhood

|

Table 8.

Green Community Working Green Business Education Amenity Incubator N.No Co-Housing Areas Spaces Spaces spaces spaces spaces spaces Spaces 1 69800 2700 1200 18900 6200 3300 2000 5700 1400 2 3000 800 100 100 100 200 2000 4900 200 3 13000 400 200 100 900 200 2000 2300 200 4 7000 2000 300 6500 200 200 2000 11800 900 5 30700 2300 300 15200 300 3800 2000 10200 2300 6 78800 8100 1200 28500 900 7100 2000 11300 4300 7 327800 7800 1700 117700 5200 14200 2000 11900 2400 8 56800 6800 1000 26100 800 6500 2000 12100 3900 9 95700 7500 1100 35700 800 8900 2000 10500 5400 10 45500 7200 1000 19600 800 4900 2000 9900 2900 11 50100 56500 12100 27300 37600 1100 2000 7100 3800 12 263600 4100 1800 33900 9500 4800 2000 11700 1500 13 26800 3400 700 24500 2300 900 2000 9100 3400 14 25000 1000 100 100 100 0 2000 8700 0 15 10500 2900 600 3400 1900 800 2000 9300 500 16 2300 900 200 6800 600 1700 2000 9100 1000 17 10300 5100 1100 9900 3400 2500 2000 9000 1500 18 54800 4700 1000 21000 3100 5300 2000 8400 3200 19 65600 4600 700 33300 500 8300 2000 7100 5000 20 9000 400 100 12200 0 3000 2000 9600 1800 21 115100 5100 1100 2100 3400 500 1800 10200 300 22 215100 1500 700 4600 3600 1100 1800 9100 700 23 65500 4700 1000 17400 3200 4300 1800 10300 2600 24 50900 2400 500 28200 1600 3400 1800 11400 600 25 78900 2300 500 66500 1500 8000 1800 8100 1400 26 68900 4700 700 42800 500 10700 1800 8500 6400 27 23800 3800 500 19100 400 4800 1800 10700 2900 28 61500 800 200 4700 500 1200 1800 6800 700 29 32100 1600 700 1800 3700 500 1800 8500 300 30 17400 400 100 4300 0 200 1800 4600 600 31 76100 8000 1100 25400 900 6400 1800 10400 3800 32 37100 5500 800 19500 600 4900 1800 10400 2900 33 14000 2600 400 12200 300 3100 1800 9000 1800 34 27800 3800 1600 700 8800 200 1800 4800 100 35 42100 300 100 72100 700 8700 1800 8600 1500 36 149400 1000 400 2100 2200 300 1800 5300 0 37 29500 500 200 58100 1100 7000 1800 2900 1200 38 21500 900 400 3900 2100 500 1800 6700 100 39 178500 100 0 7800 300 900 1800 7300 200 40 38000 5100 1100 6800 3400 300 1800 6000 900 41 36400 4700 1000 6400 3100 200 1800 5400 900 42 492200 1400 600 3000 3300 800 1800 18200 500 43 97700 1700 700 13100 3900 3300 1800 5600 2000 44 13800 4600 1000 13000 3100 3300 1800 6900 2000 45 15500 600 100 11900 400 3000 1800 4600 1800 46 11400 200 0 4100 100 1000 1800 6200 600 47 249200 0 0 35500 100 3600 1800 2300 0 48 12400 400 100 700 300 1300 1800 7800 1400 49 33700 100 0 10500 100 1500 1800 8300 500 50 10300 0 400 22200 3000 2200 1800 2400 0 51 19500 1100 500 5300 2600 800 1800 4400 200 52 16200 1500 600 47300 3500 5700 1800 5400 1000 53 31900 900 400 1500 2200 400 1800 2900 200 54 239800 0 1000 15900 7800 1900 1800 700 300 55 23800 100 0 1300 200 200 1800 0 0 56 260800 0 1000 30500 7600 3700 1800 700 600 57 25100 0 900 300 6600 0 1800 800 0 58 44400 900 400 11500 2200 1300 1800 6100 200 59 18100 900 200 100 600 100 1800 2800 100 60 94000 4500 1000 82300 3000 9700 1800 7000 1500 61 22600 1300 300 31900 900 3700 1800 6600 600 62 14500 1100 200 7000 800 800 1800 8100 100 63 4800 4100 600 19900 500 2300 1600 6000 400 64 46700 6900 1500 42700 4600 5000 1600 8100 800 65 17000 1200 300 24500 800 2900 1600 7100 400 66 11600 900 100 200 100 200 1600 8400 200 67 275600 2700 600 1000 1800 100 1600 800 0 68 34500 1500 300 34700 1000 4100 1400 6600 600 69 16900 3900 600 200 400 200 1400 5200 200 70 10700 900 400 7500 2000 1900 1400 1300 1100 71 2300 1200 200 19600 800 2300 1200 2500 300 72 14500 300 0 38200 0 4500 600 2700 700 73 21300 1100 500 82200 2600 9900 2600 4000 1700

VIII. Framework

With these calculations, all possible areas are in the table 8.

Case study : Neighbourhood 73, La Verneda i La Pau

La Verneda i la Pau is the northernmost neighborhood in Sant Marti district . Most of its area, until the 1950s, was occupied by fields with some masies (catalan style mansions). Like most suburbs in Barcelona, La Verneda also saw a drastic population increase during the following years. This resulted in rapid construction of a high number of buildings, but also saw a significant rise in deficiency of basic services.

There is only one metro station located inside the neighborhood limits and the district Sant Marti is relatively less connected to other cities of Barcelona’s metropolitan limits. Thus, the municipality has undertaken a major redevelopment project of constructing a transport hub (Figure 65).

This is the project, Sagrera railway station,

which is under construction in districts of Sant Andreu and Sant Martí. It is intended to serve as the main station for northern and eastern Barcelona metropolitan areas. Upon completion, it will be a major public transport hub with a bus station, with dedicated stations connected to existing Barcelona Metro lines.

In anticipation of increased activities because and around the station, new high value homes, retail spaces and hotels are proposed in the neighbourhood of La Verneda. This will result in gentrification and displacement risks to an already vulnerable population. Thus incorporating programs and interventions that will improve the socioeconomic vulnerability of the neighbourhood will help mitigate the current and future vulnerabilities.

Transport Hub

IV. Scientific Interest |

V. State of Art

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

|

II. Global Context

|

III. Barcelona Context |

73

I. What?

Figure 65.

La Verneda i La Pau

66

RE{CODE}

VIII. Framework

Figure 66.

Land use map

| IX. Protocol | X. Stakeholders |

Density

689.4

721

Migrant

20.6%

14.8%

81.8

84.4

2.9

59.6

9.2

16.8

29.9

29.5

20.1%

26.2%

2.8

2.6

28.3%

28.1%

93.7

57

64.5%

60.9%

Unemployment %

6.8%

8.3%

Green space/cap

17.9m2

3.9m2

Life expectancy Education facilities/100 Commerce Housing ratio Area of house/ cap Higher education % Amenities per 100 Inactive space %

Employable %

XIV. Reflection

Disposable income

| |

Methodology

73

XIII. Global outlook

Neighbourhood 73 vulnerabilities

Barcelona

........... | RE{CODE} |..........

Table 9.

N.No.

|

In neighbourhood number 73, La Verneda i La Pau, there are relatively acceptable or ideal conditions for the socio-economic indicators of population density, percentage of migrant population, average life expectancy, ratio of commercial to residential spaces, household area per capita, percentage of population with higher education and percentage of employable demography. But the values in the indicators of available education facilities per 100, available amenities per 100, percentage of inactive spaces, average available disposable income per capita, unemployment percentage and green space per capita highlights values that are outliers and extremes as compared to Barcelona’s average or specified ideal conditions, thus, highlighting its prevailing vulnerabilities. Table 9 shows the values of the different indicators, and Figure 67-Figure 73 shows the box plot graphs of the vulnerable indicators with the values of the neighbourhood highlighted in contrast to the overall distribution of data. Figure 74 shows the redevelopment potential of blocks in this neighbourhood.

XI. Platform

Work + Commerce Housing Amenities Green Spaces

67

VII. Methodology

|

Nihar Mehta

Figure 67.

|

VI. How?

|

Available disposable income per capita

V. State of Art

Figure 68.

IV. Scientific Interest |

Percentage population with higher education

Figure 69.

III. Barcelona Context |

Available amenities per 1000 capita

|

Figure 70.

|

II. Global Context

Percentage of inactive spaces

Figure 71.

I. What?

Percentage of employable demography

68

RE{CODE}

VIII. Framework | IX. Protocol

Figure 72.

Unemployment percentage

| X. Stakeholders

Figure 73.

Greenspace area per capita

|

Figure 74.

XI. Platform

Land use map

| ........... | RE{CODE} |..........

XIII. Global outlook

Selected block

| XIV. Reflection

not suitable low potential{0}

Methodology

|

high potential{1}

69

|

VII. Methodology

|

Nihar Mehta

Following the catalogue of interventions for value improvement, the vulnerabilities of this neighbourhood can be improved by focusing on interventions for supporting health and wellbeing, training opportunities, creating direct jobs, spending on voluntary, community and social enterprises. This hints towards the programs of addition of green spaces, community spaces, amenities, education and learning facilities and spaces for local businesses.

Table 10 shows the required value improvements in the neighbourhood based on its specific vulnerabilities.

N. No

02

Corresponding proposals selected

-Supporting health and wellbeing

-Addition of green spaces

-Training opportunities

-Addition of community spaces

-Creating direct jobs

-Addition of co-working spaces -Addition of incubators and training facilities

07

-Supporting health and wellbeing

-Addition of green spaces

-Training opportunities

-Addition of community spaces

-Creating direct jobs

-Addition of co-working spaces

-Creating jobs for unemployed, not in education or training

-Addition of education and learning facilities

-Advice and support to VCSEs and MSMEs 73

II. Global Context

|

III. Barcelona Context |

Value improvements required

-Addition of education and learning facilities

IV. Scientific Interest |

V. State of Art

|

VI. How?

Table 10. Required value improvements

-Addition of incubators and training facilities -Addition of amenities -Addition of spaces for local businesses

-Supporting health and wellbeing

-Addition of green spaces

-Training opportunities

-Addition of community spaces

-Creating direct jobs

-Addition of amenities

-Spend of voluntary, community and social enterprises

-Addition of education and learning facilities -Addition of spaces for local businesses

|

Figure 75 shows the proposal as the potential opportunities of the selected block to improve the socio-economic performance of the neighbourhood and reduce its vulnerabilities.

I. What?

Figure 75.

Redevelopment potential in neighbourhood 73

70

RE{CODE}

........... | RE{CODE} |..........

~16 m

~16 m

XIII. Global outlook

|

m

|

XIV. Reflection

110

Private

Government Government Private agencies agencies entities

Private

Private Inhabitants Inhabitants entities

Government Government Private agencies agencies entities

Public

Private

Private

Primary Secondary Secondary

Private Inhabitants Inhabitants entities

Government Government Private agencies agencies entities

Social Value Social Value Value Economic Economic Value

Public

Primary

Government Government Private agencies agencies entities

Public

Private

Private

Private Inhabitants Inhabitants entities

Government Government Private agencies agencies entities

Social Value Social Value Value Economic Economic Value

Public

Controlled Controlled Exposed to sun Exposed to sun

2600 sq.m.

2600 sq.m. GREEN SPACES GREEN SPACES1100 sq.m.

1100 sq.m.

Private Inhabitants Inhabitants entities

Social Value Social Value Value Economic Economic Value

Private

Public

Public

Private

Primary Secondary Secondary

Primary

ESSRNTIAL ESSRNTIAL SERVICES SERVICES

XI. Platform

m

Private

4000 sq.m.

4000 sq.m.

COMMUNITY COMMUNITY SPACE SPACE 800 sq.m.

800 sq.m.

Private Inhabitants Inhabitants entities

SPACE FOR SPACE AMENITIES FOR AMENITIES

|

110

Private

Social Value Social Value Value Economic Economic Value

X. Stakeholders

m

Public

Primary Secondary Secondary

|

110

Public

Primary

IX. Protocol

m

Public

Primary Secondary Secondary

Social Value Social Value Value Economic Economic Value

|

110

Government Government Private agencies agencies entities

Public

Primary

EDUCATION EDUCATION FACILITYFACILITY SPACE FOR SPACE LOCAL FOR ENTITIES LOCAL ENTITIES 9900 sq.m.

9900 sq.m.

2600 sq.m.

2600 sq.m.

Private Inhabitants Inhabitants entities

Economic Economic Value Social Value Social Value Value

Private

Public

Public Private

Primary Secondary Secondary

Primary

VIII. Framework

|

Methodology

71

3.3.3

3.3.4

Calculating value improvement opportunities and proposals

in Proposal report for planning policy

The outcome of the analysis and protocol is

Apart from the tangible result of addition compiled into a report (Figure 76). This can help the of programs, it is also important to measure the city council make planning strategies and policies, financial value the proposal will generate. Usually and for development agencies to design proposals. during the development of projects, the eventual economic value is calculated. But it is equally important to calculate the social value of the project.

Social Value is an umbrella term for economic, social and environmental effects of actions by projects.

Organisations that make a conscious effort to ensure positive effects can be considered as adding social value. This contributes to the long-term wellbeing and resilience of individuals, communities and society. Businesses can make decisions both about what they do and how they do it in ways that add social value. This gives a comprehensive result of socio-economic value improvement of the proposal.

It is calculated using National TOMs Measurement Handbook (Social Value Portal, 2021).

Figure 76.

Sample proposal report

Taking the example of neighborhood no. 73, the calculations are elaborated (Table 11).

Table 11. Value improvement calculation - neighborhood 73 Theme

Measure

Units

Value

Social

Training opportunities

No. of weeks per person

€ 50/week X 100

Supporting health and wellbeing

Invested time, services and equipments

€ 5000 pp X 1000

Creating direct jobs

No. of employees

€ 12000 X 100

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

VI. How?

|

VII. Methodology

|

Nihar Mehta

II. Global Context

Economic

€ 10000 X 10

Total Social value

= Sum (Measures * Value) = € 6,32,600

Total Economic value

= Cadastral value/m2 * Developable area = € 1,23,66,250

Total value created

= Social value + Economic Value = € 1,86,92,650 (€ 18.69 million)

I. What?

|

Spend of voluntary, community and social enterprises No. of businesses benefited

72

RE{CODE}

VIII. Framework | IX. Protocol | X. Stakeholders | XI. Platform | XIV. Reflection

Nodes Diffusion network

|

Methodology

XIII. Global outlook

In addition to the value calculations, the proposal is also validated in terms of change in the SEVI of the neighbourhood where the intervention is planned, its adjacent areas and on the city’s as a whole. A decreased index, tending towards 0, overall is a positive proposal.

Computational method of diffusion algorithm is used to check and carry out the impact analysis. Diffusion algorithm is a propagation algorithm where values are iteratively diffused from nodes to neighbors across connections in a network (Figure 77).

Diffusion network with connections and nodes

........... | RE{CODE} |..........

Impact analysis of interventions

Figure 77.

|

3.4 3.3

73

The diffusion from nodes is calculated as the change in original value of the parent node through node gain and node loss. Node gain is the value gained from neighbor nodes and node loss is the value diffused to neighbor nodes (Figure 78).

Figure 78.

Diffusion network with connections and nodes

VI. How?

|

VII. Methodology

|

Nihar Mehta

V. State of Art

|

N1 k1

IV. Scientific Interest |

N2

k2

N5

A k5

k3

k4 N4

III. Barcelona Context |

N3

|

Parent Node

II. Global Context

Neighbor nodes Diffusion network

|

New value of parent node (VA’) =

I. What?

Original value of parent node (VA) - Node loss + Node Gain

74

RE{CODE}

k2=1 k1=0.03/0.03

VP = 0.89 VC = 0.86

X. Stakeholders

Na = 0.03

Diffusion coefficient calculation example

|

k1=0.03/0.06

Figure 79.

IX. Protocol

Nb1 = 0.06

k1=0.5

VP = 0.71 VC = 0.68

|

VP = 0.42 VC = 0.48

VIII. Framework

Node loss is calculated as the product of original value of parent node and sum of diffusion coefficients (kn) of each connection in the network.

Node gain is the sum of the products of values of neighboring nodes (VNn) and their respective diffusion coefficients (kn). Diffusion coefficient, between connected neighbourhoods in the network, is the ratio of change of value in time of parent node (Na) over change of value in time of neighboring nodes (Nb) (Figure 79).

Nb2 = 0.03

| XI. Platform

VA’ = VA - VA*(k1 + k2 +...kn) + (VN1 * k1 + VN2 * k2 +...VNn * kn) k = Change in parent node (Na) / Change in neighboring node (Nb)

| ........... | RE{CODE} |..........

Change in node = Previous year’s value (VP) - Current value (VC)

XIII. Global outlook

Performing the algorithm over one neighborhood would give the change in SEVI (Figure 80) (Table 12) as an altered SEVI, of the parent neighborhood node and the connected neighborhood nodes (Figure 81) (Table 12). For this example, the change in the neighbourhood’s SEVI is by -0.2 and for the city by -0.07, which indicates positive impact.

| XIV. Reflection |

Methodology

75

|

Nihar Mehta

Original SEVI

0.44

|

VII. Methodology

Figure 80.

0.68

0.79

VI. How?

0.62

|

0.86 0.87

Neighbor nodes Diffusion network low potential{0}

high potential{1}

Figure 81.

Altered SEVI

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

Parent Node

0.53

II. Global Context

|

0.47

0.48

0.45

0.83 Parent Node

0.62

|

Neighbor nodes Diffusion network

I. What?

low potential{0}

high potential{1}

76

RE{CODE}

Refinement and testing diffusion algorithm

of

the the expansion of Barcelona outside the old city

| X. Stakeholders | XI. Platform

Figure 83.

IX. Protocol

When Catalan urban planner Ildefons Cerdà i Sunyer, back in the 1850s, designed the Eixample,

|

The diffusion algorithm calculates the change in value of each node of the interconnected system.

In this research’s urban context, it helped calculate the change in SEVI of all Barcelona neighbourhoods being influenced by addition of an intervention in one of the neighbourhoods. A validation test was done to check the accuracy of the algorithm, which can help in its fine tuning. This step requires rigorous testing, with multiple case studies and multiple test datasets. For the purposes of this thesis, instead of an extensive investigation into refinement of the diffusion algorithm, the research focuses on a historic case that had irrefutable and significant consequences on the neighbourhood and the city.

This case selected is of the Superblock project of Poble Nou, Barcelona (Figure 82).

walls, he envisioned a city based on community living, where people could interact on wide streets, with health of public and private gardens, and where transportation of people and goods wouldn’t dominate public space. What Cerdà could not foresee in his plan, approved by the city in 1859, was the arrival of the automobile and the resulting transformation of mobility that took place in the middle of the 20th century.

With the current problems of increased air pollution and congestion, the city council devised the plan of converting existing urban infrastructure to host more public spaces and reduce the use of vehicles. That gave neighbours access to their garages and parking spaces but kept the Superblock clear of vehicle traffic. The establishment of the superblock in Poblenou, in 2016, resulted in a 70% increase in public space. Hence from the Superblock project, interventions used as the test case is the proposal with addition of 25000 sqm area of green spaces and 1000 sqm area of local businesses (Figure 83).

VIII. Framework

3.5

Superblock model | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Methodology

77

Nihar Mehta

|

Figure 83.

VII. Methodology

Superblock model

The SEVI of neighbourhoods were calculated for 2017 using the indicators established, and the inputs from the proposal were substituted with to calculate the SEVI of the altered neighbourhood.

This altered value of the neighbourhoods was cross referenced with the actual data from 2017, and the calculated difference is the error percentage. This difference was measured for both the neighbourhoods in isolation, but also the city as a whole. The algorithm was then fine tuned to reduce the error with a small margin of error. In this case, the difference achieved after tuning the algorithm is 12.6 %. Table 12 shows the SEVI values of the current and altered 2017.

Table 12.

Actual against altered SEVI values using diffusion algoritm

I. What?

|

II. Global Context

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

VI. How?

|

(Dutch, Bicycle. “The Barcelona Superblock of Poblenou.” BICYCLE DUTCH, November 7, 2017. https://bicycledutch.wordpress.com/2017/11/07/the-barcelona-superblock-of-poblenou/.)

78

RE{CODE}

VIII. Framework

19 65600 0.653 4600 43 700 333001700 97700 0.505 0.459 0.199 20 9000 0.711 400 44 100 122004600 0.216 13800 0.358 0.610 21 115100 0.956 5100 45 1100 2100600 0.301 15500 0.763 0.983 22 215100 0.822 1500 46 700 4600200 0.592 11400 0.714 0.798 Green Community Working Green Business Education 23 SEVI 65500 1000 17400 Green Community Working Green Green Community Business Green Education Green 0.442 0.396 47 SEVI 249200 0 Co 0.339 0.676 SEVI 17 SEVI 17 17 SEVI 17 4700 17 Working SEVI 17 Areas Spaces Spaces spaces spaces spaces N.No Co-Housing Areas N.No Spaces Spaces Areas spaces N.No Spaces spaces Spaces Areas spaces spaces Sp 24 Co-Housing 50900 500 28200 (Actual) (Altered) (Actual) (Altered) (Actual) (Altered) 0.445 0.876 48 Co-Housing 12400 400 0.072 0.526 2400 1 69800 0.021 2700 2700 25 1200 18900 62001200 3300 200 78900 0.021 2300 49 500 66500 1 1200 69800 18900 2700 16200 69800 18900 2700 2000 0.551 0.706 337003300 100 0.380 0.670 0.130 0.130 0.130 0.021 800 26 100 1004700 1000.236 200 2 3000 0.247 800 2 100 3000 0.247 100 800 50 2 100 100 3000 200 100 8002000 68900 700 42800 0.449 0.386 0.288 0.619 10300 0200 0.236 0.236 0.247 400 27 200 1003800 9000.384 200 200 3 13000 0.373 400 3 200 13000 100 400 51 3 900 200 13000 1001100 4002000 23800 0.373 500 19100 0.476 0.742 0.209 0.178 19500 200 0.384 0.384 0.373 2000 28 300 6500 2000.377 200 200 4 7000 0.294 2000 4 300 7000 0.294 6500 2000 4 200 300 7000 200 6500 2000 2000 61500 800 52 200 4700 0.644 0.909 0.497 0.690 16200 1500 0.377 0.377 0.294 2300 29 300 15200 3000.562 3800 200 5 30700 0.402 2300 5 300 30700 15200 2300 5 300 300 30700 15200 2300 2000 32100 0.402 1600 53 700 1800 0.805 0.864 0.627 0.638 319003800 900 0.562 0.562 0.402 8100 30 1200 28500 9001200 7100 6 78800 0.491 8100 6 1200 78800 28500 8100 6 900 788007100 28500 81002000 17400 0.491 400 54 100 4300 0.521 0.799 0.360 0.592 0.486 239800 0200 0.486 0.486 0.491 7 327800 0.396 7800 7 1700 327800 7800 75200 327800 14200 117700 7800 2000 7800 31 1700 117700 52001700 14200 200 0.541 0.662 76100 117700 8000 55 1100 25400 0.305 0.346 0.281 0.281 0.396 0.281 0.396 23800 100 8 56800 0.154 6800 8 1000 56800 26100 6800 8 800 568006500 26100 68002000 6800 32 1000 26100 8001000 6500 0.366 0.746 37100 0.154 5500 56 800 19500 0.000 0.279 0.175 0.175 0.175 0.154 260800 0200 9 95700 0.219 7500 9 1100 95700 35700 7500 9 800 95700 35700 75002000 7500 33 1100 35700 8001100 8900 0.486 0.776 14000 0.219 2600 57 400 12200 0.151 0.363 0.394 0.394 0.394 0.219 251008900 0200 10 45500 0.400 7200 1000 45500 19600 7200 45500 19600 7200 7200 10 1000 19600 8001000 4900 200 0.435 0.581 34 27800 0.400 3800 10 1600 700 0.425 0.399 0.301 0.301 0.301 0.400 58 800 444004900 9002000 11 50100 0.46756500 50100 27300 56500 37600 12100 50100 27300 56500 56500 11 12100 27300 37600 1100 200 0.432 0.796 35 12100 42100 0.467 300 11 100 72100 0.356 0.484 0.517 0.517 0.517 0.467 59 181001100 9002000 12 263600 0.485 4100 1800 263600 33900 4100 263600 33900 4100 4100 12 1800 33900 95001800 4800 200 0.312 0.322 36 149400 0.485 1000 12 400 2100 0.202 0.526 0.301 0.301 0.301 0.485 609500 940004800 45002000 13 26800 0.700 3400 700 26800 24500 3400 700 26800 24500 3400 3400 13 700 24500 23001.000 900 200 0.408 0.716 37 29500 0.700 500 13 200 58100 0.524 0.592 1.000 1.000 0.700 612300 22600 900 13002000 14 25000 0.774 1000 100 25000 100 1000 100 25000 003900 1001000 1000 14 100 100 1000.853 200 0.199 0.465 38 21500 0.774 900 14 400 0.295 0.505 0.853 0.853 62 100 14500 0.774 11002000 15 10500 0.438 2900 600 10500 0.438 3400 2900 600 10500 3400 2900 2900 15 600 3400 19000.195 800 200 0.291 0.443 39 178500 100 15 04800 800 7800 0.390 0.777 0.195 0.195 0.438 631900 41002000 16 2300 0.634 900 200 2300 0.634 6800 900 16 200 23001700 6800 9002000 900 16 200 68005100 6000.455 1700 200 0.445 0.622 40 38000 1100 68006900 0.315 0.467 0.455 0.634 0.455 64 600 46700 17 10300 0.406 5100 1100 10300 9900 5100 10300 9900 5100 5100 17 1100 9900 34001100 2500 200 0.568 0.579 0.394 0.498 41 36400 0.406 4700 17 1000 6400 0.507 0.507 0.406 0.507 653400 170002500 12002000 18 54800 0.631 4700 1000 54800 0.631 21000 4700 54800 21000 4700 4700 18 1000 21000 31001000 5300 200 0.459 0.702 0.202 0.149 42 492200 1400 18 600 3000 0.312 0.312 0.631 0.312 663100 116005300 9002000 19 65600 0.653 4600 700 65600 33300 4600 700 656008300 33300 4600 4600 19 700 33300 5000.199 8300 200 0.449 0.550 0.459 0.505 43 97700 0.653 1700 19 700 13100 0.199 0.653 0.199 67 500 275600 27002000 20 9000 0.711 400 100 9000 0.711 12200 400 20 100 90003000 12200 4002000 400 20 100 122004600 3000 200 0.209 0.236 0.216 0.358 44 13800 1000 130001500 0.610 0.711 0.610 68 000.610 34500 21 115100 0.956 5100 1100 115100 2100 5100 115100 21005100 5100 21 1100 2100 34001100 500 180 0.668 0.585 0.301 0.763 45 15500 0.956 600 21 100 11900 0.983 0.983 0.956 0.983 693400 16900 500 39001800 22 215100 1500 22 700 215100 4600 1500 22 3600 700 215100 1100 4600 1500 1800 22 215100 0.822 1500 46 700 4600200 7036000.798 0.466 0.376 0.592 0.714 0.798 0.822 11400 0.822 0 1100 4100900180 0.798 10700 23 65500 1000 65500 0.676 17400 47000 23 65500 17400 4700 23 65500 0.676 4700 4700 23 1000 17400 32001000 4300 180 0.377 0.494 0.442 0.396 0.339 0.339 0.676 0.339 47 249200 023004300 35500 713200 12001800 24 50900 2400 24 500 50900 28200 2400 24 1600 500 50900 3400 28200 2400 1800 24 50900 0.526 2400 48 500 28200400 7216000.072 0.435 0.556 0.445 0.876 0.072 0.526 0.072 12400 0.526 100 700300180 145003400 25 78900 500 78900 66500 2300 500 78900 66500 2300 25 78900 0.670 2300 2300 25 500 66500 15000.380 8000 180 0.171 0.305 0.551 0.706 0.380 0.380 0.670 49 33700 0.670 100 25 0 8000 10500 731500 21300 11001800 26 68900 700 68900 42800 47000 26 500 700 68900 10700 42800 47001800 26 68900 0.619 4700 4700 26 700 42800 5000.288 10700 180 0.449 0.386 0.288 0.288 0.619 50 10300 0.619 400 22200 27 23800 3800 27 500 23800 19100 3800 27 400 500 23800 4800 19100 3800 1800 23800 0.178 3800 51 500 191001100 4000.209 0.476 0.742 0.209 0.209 0.178 19500 0.178 500 4800 5300 180 3.6 27 28 61500 200 61500 4700 800 28 500 200 615001200 4700 8001800 28 61500 0.690 800 800 28 200 47001500 5000.497 1200 180 0.644 0.909 0.497 0.497 0.690 52 16200 0.690 600 47300 is usually used for forecasting and finding out the16001800 Future projection 29 32100 1600 29 700 32100 1800 1600 29 3700 700 32100 500 1800 29 32100 0.638 1600 53 700 1800 37000.627 500 0.805 0.864 0.627 0.627 0.638 0.638 31900 400 variables.

1500 180 cause and effect900 relationship between 30 17400 400 30 100 17400 4300 400 30 0 100 17400 200 4300

The methodology so far has evaluated 30 17400 0.592 400 54 100 4300 180 0.521 0.799 0.360 0.360 0.592 0.360 0.592 239800 0 mostly 0differ 1000 based200 15900 Regression techniques on the 4001800 proposals for the vulnerabilities through current data 31 76100 1100 76100 25400 8000 900 76100 6400 25400 number 0.346 of independent variables and type of80001800 31 76100 0.346 8000 8000 31 1100 25400 9001100 6400 180 0.541 0.662 0.305 0.305 0.305 0.346 55 23800 100 31 0 the 1300 and current urban situation. Although it is important relationship between the independent and dependent 32 37100 5500 32 800 37100 19500 5500 32 600 800 37100 4900 19500 5500 1800 32 800 19500 0 6000.000 0.366 0.746 0.000 0.000 0.279 260800 0.279 1000 4900 30500 180 to understand the37100 impact of0.279 these 5500 proposals56in time, variables.122002600 33 300 400 33 14000 2600 33 400 14000 14000 3100 12200 26001800 14000 400 12200 0 3000.151 180 0.486 0.776 0.151 0.363 2600 0.151 0.363 for a 33 better and more informed decision-making 57 25100 0.363 900 3100 300 34 27800 3800 34 1600 27800 700 3800 34 8800 1600 27800 200 700 3800 1800 process.

To do so, regression techniques in machine 0.435 0.581 34 27800 0.399 3800 58 1600 700900 88000.425 200 0.425 0.425 0.399 44400 0.399 400 11500 180 learning can be deployed.

35 42100 300 35 100 42100 72100 300 35 700 100 42100 8700 72100 0.432 0.796 35 42100 0.484 300 59 100 72100900 7000.356 180 0.356 0.484 0.356 18100 0.484 200 8700 1003001800 36 149400 1000 36 400 149400 2100 1000 36 2200 400 149400 300 2100 1000 1800 0.312 0.322 36 149400 1000 60a target 400 21004500 22000.202 300 0.202 0.526 0.202 0.526 94000 0.526 1000 82300 180

Regression is a method of modelling 37 29500 500 37 200 29500 58100 500 37 1100 200 29500 7000 58100 0.408 0.716 37 on0.524 29500 0.592 500This61method 200 581001300 11000.524 180 0.524 0.592 22600 0.592 300 7000 319005001800 value based independent predictors.

38 21500 900 38 400 21500 3900 900 38 2100 400 21500 500 3900 900 1800 0.199 0.465 38 21500 0.505 900 62 400 39001100 21000.295 500 0.295 0.505 0.295 14500 0.505 200 7000 180 39 178500 100 39 178500 0 7800 100 39 300 178500 0 900 7800 0.291 0.443 39 178500 0.777 100 63 04800 0.777 78004100 3000.390 900 180 0.390 0.777 0.390 600 199001001800 40 38000 5100 40 1100 38000 6800 5100 40 3400 1100 38000 300 6800 5100 1800 0.445 0.622 0.315 0.467 40 38000 0.467 5100 64 1100 68006900 34000.315 300 180 0.315 46700 0.467 1500 42700 Methodology 79 41 36400 4700 41 1000 36400 6400 4700 41 3100 1000 36400 200 6400 4700 1800 0.568 0.579 0.394 0.498 0.394 41 36400 0.498 4700 65 1000 64001200 31000.394 200 17000 0.498 300 24500 180 42 492200 1400 42 600 492200 30001400 423300 600 492200 800 300014001800 | IX. Protocol | X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Simple linear regression is a type of regression analysis where the number of independent variables is one and there is a linear relationship between the independent(x) and dependent(y) variable. The dark pink line in the graph (Figure 84) is referred to as the best fit straight line. In the case of Barcelona, data from 2010 to 2020 are used as input to project future data. To achieve a projection dataset with acceptable levels of error, year 2023 was considered as favorable.

The research has created a projected dataset (Figure 85 to Figure 96) using linear regression in (Lunchbox 2020), with historical data of the selected urban indicators.

V. State of Art

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VI. How?

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VII. Methodology

|

Nihar Mehta

Figure 85.

III. Barcelona Context |

IV. Scientific Interest |

Population density projection

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 86.

I. What?

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II. Global Context

|

Percentage of migrant population projection

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

80

RE{CODE}

VIII. Framework

Figure 87.

Average life expectancy projection

| IX. Protocol | X. Stakeholders

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 88.

Available education facilities per 100 inhabitants projection

| XI. Platform | ........... | RE{CODE} |..........

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 89.

Ratio of commercial to residential spaces projection

XIII. Global outlook | XIV. Reflection

Methodology

|

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 81

Nihar Mehta

|

Figure 90.

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VI. How?

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VII. Methodology

Household area per capita projection

V. State of Art

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 91.

III. Barcelona Context |

IV. Scientific Interest |

Available amenities per 1000 inhabitants projection

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 92.

I. What?

|

II. Global Context

|

Percentage population with higher education projection

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 82

RE{CODE}

VIII. Framework

Figure 93.

Percentage of inactive spaces projection

| IX. Protocol | X. Stakeholders

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 94.

|

Percentage of employable demography (age 16-64) projection XI. Platform | ........... | RE{CODE} |..........

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 95.

Unemployment percentage projection

XIII. Global outlook | XIV. Reflection

Methodology

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2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 83

Nihar Mehta

|

Figure 96.

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Performing the algorithm over the neighborhood, with the projected data set, would give the change in current SEVI as projected SEVI, of the parent neighborhood node and the connected neighborhood nodes (Figure 97).

For example, observing the changes over the same neighbourhood as earlier (Page 72), we can see the change in the neighbourhood’s SEVI is by -0.09 and for the city by -0.02 which indicates a positive impact (Table 13).

Figure 97.

Altered SEVI

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

Available family income per capita projection

0.43

II. Global Context

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0.52

0.59

0.59

0.59 Parent Node

0.38

|

Neighbor nodes Diffusion network

I. What?

low potential{0}

high potential{1}

84

RE{CODE}

VIII. Framework

0.844 0.548 0.199612 28 615000.744065 800 3 13000 400 200 100 1600 900 0.367 0.274 0.119605 29 321000.644251 4 7000 0.281304 2000 6500 0.356 30 3000.752 174000.594912 400 200 5 30700 0.261686 2300 0.648 0.2462148000 300 31 3000.263 7610015200 6 78800 0.52109 8100 0.619 0.0917745500 900 32 12000.456 3710028500 327800 7800 17000.544 117700 5200 Refinement and testing of the machine 7learning 0.581 0.457558 33 140000.2900812600 8 56800 0.076581 6800 1000 26100 800 algorithm 0.396 0.667 34 278000.3686613800 9 95700 0.174399 7500 1100 35700 800 0.611 0.556 35 42100 0.38295 300 Poble Nou Superblock was used as the10test case45500 7200 1000 19600 800 0.7 0.331126 0.767 36 1494000.6692021000 for the refinement and testing of the regression 11 50100 0.249499 56500 12100 27300 37600 0.296 0.611 0.641415 500 37 29500 algorithm. Altered dataset calculated for 12 2017 was 263600 4100 1800 33900 9500 0.378 0.647527 used along with historic datasets from 2010 to 0.407 0.384224 900 38 21500 13 26800 0.717551 3400 700 24500 2300 0.356 project the data for 2018 and 2019. The difference 0.733 0.744119 39 178500 100 14 25000 0.816869 1000 100 100 100 0.959 between the projected 2019 dataset was compared 0.415 0.586865 40 38000 5100 10500 0.444372 2900 600 3400 1900 0.341 against the actual dataset for 2019. In this15case, the 0.285 0.498513 41 36400 4700 16 2300 900 200 6800 600 0.589 0.575461 difference achieved after tuning the algorithm is 6.5 42 11000.363 4922000.151165 17 current10300 5100 9900 14003400 0.63 0.396072 %. Table 13 shows the SEVI values of the 0.32449417003100 43 10000.359 9770021000 18 54800 0.681837 4700 and altered 2019.

0.541 0.4921464600 500 44 7000.433 1380033300 19 65600 0.709009 4600 0.604 Table 13. 600 0.4 12200 0.844925 45 100 15500 20 9000 0.895941400 0 0.585 21 115100 5100 2100 46 11000.567 114000.728783 2003400 0.8 1.111712 Actual against altered SEVI values from linear progression 22 215100 1500 0.73921 03600 47 7000.489 249200 4600 0.352 1.107696 23 65500 0.748994 4700 0.866671 4003200 0.444 48 10000.663 1240017400 Green Community Working Education Amenit Green Community Working Green Busine Green Community Working Green Business Education Amenity Inc 24 SEVI 50900 2400 500 28200 1600 19 SEVI 19Green SEVI 19 SEVI 19 0.544 0.647154 Green Comm 1 SEVI 0.768099 SEVI 19 19 100 49Business 33700 N.No Co-Housing Areas Spaces Spaces spaces spaces N.No Areas Spaces Spaces spaces spaces N.No Co-Housing Areas Spaces Spaces spaces spaces spaces spaces spaces Spa 25 Co-Housing 78900 2300 500 66500 (Actual) (Altered) (Actual) (Altered) 0.533 0.784891 N.No Co-Housing Areas Space (Actual) (Altered) 0.333 0.260045 50spaces 10300 01500 1 69800 2700 1200 18900 6200 11 69800 2700 1200 18900 6200 6200 1 3300 2000 5700 69800 -0.098056 2700 12000.404 18900 3300 2000 26 68900 4700 500 -0.098056 0.426 0.650289 0.404 69800 2700 -0.098056 0.43 0.721862 51 7000.404 1950042800 1100 2 3000 800 100 100 100 22 3000 800 100 100 100 200 2000 4900 3000 0.199612800 100 3800 10052 200 2000 27 1000.548 23800 0.199612 0.57 0.098096 0.548 0.548 0.199612 2 5000.489 300019100 800 400 0.925799 16200 1500 3 13000 400 200 100 900 33 13000 400 200 100 900 200 2000 2300 13000 0.119605400 100 800 900 3 2000.511 200 0.883162 2000 28 2000.274 61500 0.119605 4700 500 0.844 0.744065 0.274 0.274 0.119605 13000 400 53 31900 900 4 7000 2000 300 6500 200 44 7000 2000 300 6500 200 200 2000 11800 7000 0.281304 2000 6500 1600 200 4 7000.356 200 2000 1 29 3000.356 32100 0.281304 1800 0.367 0.644251 0.356 0.281304 7000 2000 0.256 0.89424 54300 239800 03700 5 300 30700 2300 300 15200 300 55 30700 2300 15200 3800 2000 10200 30700 0.261686 2300 3800 4300 2000 30 3000.648 17400 15200 01 0.261686 0.752 0.594912400 300 5 1000.474 0.648 0.648 0.261686 2300 0.690959 55 23800 100 61200 78800 8100 900 1200 28500 900 6 78800 8100 28500 7100 30700 2000 11300 6 78800 0.52109 8100 7100 2000 31 12000.619 76100 28500 8000 900 6 11000.619 25400 9001 0.52109 0.263 0.246214 0.619 0.52109 78800 8100 71700 327800 78005200 1700 117700 5200 1 0.2 0.969209 56 0 7 327800 7800 117700 14200 260800 2000 11900 7 327800 0.457558 7800 1700 117700 5200 14200 2000 1 32 37100 5500 800 19500 600 0.581 0.457558 0.456 0.091774 0.581 0.581 0.457558 81000 56800 6800 800 1000 26100 800 7 65000.248 327800 7800 8 56800 6800 26100 2000 12100 0.800588 57 25100 0 8 56800 6800 1000 26100 800 6500 2000 33 14000 2600 400 12200 3001 0.396 0.076581 0.544 0.290081 0.396 0.076581 0.396 0.076581 91100 95700 7500 800 1100 35700 800 9 95700 7500 35700 8900 2000 10500 8 56800 6800 0.415 58 444000.668251 900 95700 0.174399 7500 11000.611 35700 34 27800 3800 1600 8900 700 2000 88001 0.174399 0.667 0.368661 0.611 10 45500 7200 1000 19600 800 0.174399 109 45500 7200 1000 19600 800 80059 9900 9 4900 0.611 95700 7500 0 2000 0.702108 18100 900 35 10000.556 42100 300 800 100 4900 72100 700 10 45500 7200 19600 2000 0.7 0.331126 0.38295 11 50100 56500 12100 27300 37600 0.7 0.331126 0.7 0.331126 11 50100 56500 12100 27300 37600 1100 2000 7100 10 45500 7200 0.356 0.506114 60 94000 4500 12 263600 4100 1800 33900 9500 36 149400 1000 4000.296 2100 2200 0.296 0.249499 0.767 0.669202 11 50100 0.249499 56500 12100 27300 1100 2000 0.296 12 263600 4100 1800 33900 95003760011 4800 2000 11700 0.249499 50100 56500 0.478 0.778735 61 22600 1300 13 26800 3400 700 24500 2300 37700 29500 2000.378 11001 0.647527 0.611 0.641415500 12 263600 4100 18000.378 33900 4800 58100 2000 9100 0.378 13 26800 0.647527 3400 24500 2300 9500 900 2000 0.647527 12 263600 4100 0.326 0.439687 62 14500 1100 14 25000 1000 100 100 100 0.356 0.717551 38 21500 900 400 3900 2100 0.407 0.384224 13 26800 0.717551 3400 24500 900 2000 2000 8700 14 25000 1000 100700 100 100 2300 0 0.356 0.356 0.717551 13 26800 3400 0.696 0.472879 15 105003400 3400 1900 4800 4100 15 10500 2900 1900 1006360080000.959 9300 0.959 0.816869 0.733 0.744119 39600100178500 100 7800 300 14 25000 0.816869 1000 1002900 0 2000 2000 0.959 0.816869 14 25000 1000 16 2300 900 200 6800 600 0.596063 46700 6900 16 2300 0.444372 900 6800 600 190064 1700 9100 0.444372 0.415 0.586865 402006000.341 38000 11000.444 6800 3400 15 10500 2900 3400 5100 800 2000 2000 0.341 0.341 0.444372 17 10300 5100 1100 9900 3400 15 10500 2900 17 10300 5100 11002000.589 9900 3400 60065 2500 9000 0.584567 170002000 1200 0.575461 0.285 0.498513 41 36400 10000.407 6400 3100 16 2300 0.575461 900 6800 4700 1700 2000 0.589 0.589 0.575461 181000 54800 47003100 1000 21000 3100 16 2300 900 18 54800 4700 21000 5300 2000 8400 116000.731973 9003300 0.63 0.396072 0.363 0.151165 42 1100492200 3000 17 10300 0.396072 5100 9900 1400 340066 6000.067 2500 2000 0.63 0.63 0.396072 19 700 65600 4600 500 700 33300 500 17 10300 5100 19 65600 4600 33300 8300 2000 7100 0.521152 0.681837 0.359 0.324494 275600 2700 43 10000.541 97700 21000 1700 310067 7000.081 13100 3900 18 54800 0.681837 4700 5300 2000 0.541 0.541 0.681837 20 9000 400 100 12200 0 20 9000 400 100 12200 0 3000 2000 9600 18 54800 4700 0.604 0.709009 0.433 0.492146 44 700 13800 33300 4600 50068 10000.059 13000 0.066334 34500 15003100 19 65600 0.709009 4600 8300 2000 0.604 0.709009 21 5100 1100 2100 3400 21 115100 5100 1100 1151002100 3400 500 0.604 1800 10200 19 65600 4600 0.585 0.895941 0.4 12200 0.844925 45 100 15500 600 11900 0.574846 16900 3900 400 20 9000 0.895941400 0697001000.115 3000 2000 0.585 22 1500 4600 3600 0.585 0.895941 22 215100 1500 700 2151004600 3600 1100 1800 9100 9000 400 0.8 0.728783 1.111712 46 11000.567 11400 200 340020 00.296 4100 100 0.274286 21 115100 5100 2100 500 1800 70 10700 900 0.8 1.111712 23 65500 4700 1000 17400 3200 0.8 1.111712 23 65500 4700 1000 17400 3200 4300 1800 10300 1 21 115100 5100 0.352 0.489 1.107696 0.73921 0 47 700249200 00.352 35500 100 24 28200 1600 22 215100 1500 1100 1800 0.352 0.686057 2300 1200 1.107696 24 50900 1.107696 2400 500 50900 28200460024001600 360071500 34000.459 1800 11400 22 100 215100 1500 0.748994 0.663 0.866671 48500 12400 400 700 1800 3001 25 78900 2300 66500 1500 0.444 23 65500 0.748994 4700 10000.444 17400 4300 0.7 0.635426 14500 300 0.748994 25 78900 2300 66500 1500 320072500 8000 0.444 1800 8100 65500 4700 26 68900 4700 42800 500 0.647154 1 28200 0.768099 497005000.544 33700 100 10500 10011 0.544 24 50900 0.647154 2400 3400 1800 0.47 0.249588 73700 21300 1100 26 68900 4700 42800 500 160023 1070000.544 1800 8500 0.647154 27 23800 3800 500 19100 400 0.784891 0.333 0.260045 400 5090022200 2400 505005000.533 10300 0 150024 4800 4000.533 3000 0.533 25 78900 0.784891 2300 66500 8000 1800 27 23800 3800 19100 1800 10700 0.784891 28 61500 800 200 4700 500 0.434700 0.721862 0.650289 789001800 2300 512007000.426 19500 1100 50010700 5300 2600 0.426 28 61500 800 500 50025 1200 6800 26 68900 0.650289 4700 42800 1800 0.426 0.650289 29 32100 1600 700 1800 3700 0.925799 0.571800 0.098096 527005000.489 16200 1500 600 4800 35001 6890047300 4700 29 32100 1600 3700 40026 500 1800 8500 0.57 27 23800 0.098096 3800 19100 1800 0.57 0.098096 30 17400 400 100 4300 0 0.511 0.883162 0.744065 531002000.844 31900 900 0 50027 200 4000.844 1500 1800 2200 30 17400 400 4300 4600 238001800 3800 0.844 28 61500 0.744065 800 47008000 1200 Methodology 31 76100 1100 254000.74406585 900 0.256 0.89424 0.367 0.644251 31 76100 8000 1100 239800 25400 900 1800 1800 10400 54 0 370028 6400 10000.367 15900 7800 61500 800 0.367 29 32100 0.644251 1600 500 32 70037100 18005500 800 195000.644251 600 32 37100 5500 800 19500 600 4900 1800 10400 0.474 0.690959 0.752 0.594912 55 100 23800 4300 100 0 32100 1600 200 0.752 30 17400 0.594912400 029 200 1300 1800 | IX. Protocol | X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

I. What?

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II. Global Context

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

VI. How?

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VII. Methodology

|

Nihar Mehta

Result applic

86

RE{CODE}

VIII. Framework

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IX. Protocol

|

X. Stakeholders

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XI. Platform

|

........... | RE{CODE} |..........

XIII. Global outlook

|

ts and cation

4.1 Digital simulation platform stage

XIV. Reflection

4.2 Opportunities for wellbeing

|

87

4.1

Digital simulation platform stage

Building urban resilience requires looking at a city holistically: understanding the systems that make up the city and the interdependencies and risks they may face. By strengthening the underlying fabric of a city and better understanding the potential shocks and stresses it may face, a city can improve its development trajectory and the wellbeing of its citizens. This holistic approach can be achieved using conventional digital technology as a digital platform which serves curated urban, environmental, and economic data to urban practitioners and equipping them with the analytic capabilities to ​ plan and improve communities faster, better, and at lower cost.

| II. Global Context | I. What?

The methodology of development for socioeconomic improvement is expanded through a digital platform developed in the research. This would allow stakeholders to visualize and analyze urban conditions and plan informed proposals.

Different stakeholders can extract information at different scales and resolutions (Figure 98).

Stakeholders

Municipality & City Planners Detailed report

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

VI. How?

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VII. Methodology

|

Nihar Mehta

Architects

Real Estate Developers

Public Business Entities

End Users / Inhabitants

Opportunities and social value as direct and indirect influences

Opportunities, social value and economic benefits

Check blocks for changes and potential sites for investment and growth

View map and potential changes and direct influences

Primarily, city council and planners would get the entire report which includes the analysis of all the indicators, the calculated current SEVI, the calculated change in SEVI, the projection data, the projected impact on SEVI, the socio-economic value created and baseline proposal with areas for each program.

Developers would get the value potential of specified interventions, architects and urban designers would be able to retrieve baseline design proposals with their direct and indirect influences, business entities would benefit with information on potential clocks for investment and growth, and inhabitants will be able to visualize and participate in the decision making on the changes to their neighborhoods.

88

Figure 98.

Stakeholders to benefit most from the platform

RE{CODE}

VIII. Framework

The platform operates with the following steps :

| IX. Protocol |

00 : The home page of the platform shows options to select the city and type of stakeholder (Figure 99).

Figure 99.

X. Stakeholders |

01 : After selection of the stakeholder and city [City council and Barcelona in the example], this step allows the user the ability to analyse the city with the indicators (Figure 100).

XI. Platform | ........... | RE{CODE} |..........

Figure 100.

XIII. Global outlook | XIV. Reflection

Results and Application

|

It also allows the user to see the most vulnerable areas based on the selected indicators (Figure 101).

Figure 101.

89

In addition, this step also allows the user to observe the city as a landscape of different clusters of performances instead of the traditional land use (Figure 102).

VI. How?

|

VII. Methodology

|

Nihar Mehta

02 : This step allows users to select the neighbourhood for development, either based on prerequisite or from the analysis (Figure 103). Further it allows the user to check for potential opportunities to improve the neighbourhood’s socio-economic vulnerability.

The platform shows the specific vulnerability from the indicators, the comparison against Barcelona’s data as box plots and the opportunity as programs and the ideal area required. The user can thus make their proposal based on the parameters selected with the platform interface.

I. What?

|

II. Global Context

|

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

|

Figure 102.

Figure 103.

90

RE{CODE}

VIII. Framework | IX. Protocol

Figure 104.

04 : This step allows the user to carry the diffusion algorithm to see the impact of the proposal generated on the SEVI of the city (Figure 105).

After the algorithm calculates the altered SEVI, the user can check the altered and the current SEVI to change the parameters from previous steps and alter the proposal.

Figure 105.

Additionally, this step also enables the ability to run the regression algorithm of machine learning to calculate the projected dataset and measure the change in SEVI of 2023 (Figure 106).

Figure 106.

X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Results and Application

|

03 : This step enables the analysis of the redevelopment potential of the blocks in the selected neighbourhood (Figure 104).

91

05 : This step allows the user to see the baseline design generated as a massing model over the block with the proposal designed by the user (Figure 107).

Figure 107.

The interface shows essential information of characteristics of the programs, area of the programs, area of block, building volume, maximum buildable floors (Figure 108).

Figure 108.

I. What?

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II. Global Context

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III. Barcelona Context |

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V. State of Art

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VI. How?

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The platform operation is documented in a video (https://youtu.be/S8hBTU5V6pw).

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Opportunities for wellbeing

IX. Protocol |

29700 sq.m / 73 : Community spaces 57000 sq.m / 57 : Education facilities

|

Implementation of all these opportunities using the methodology developed through this research

Deploying this research’s methodology over would result in a utopian reduction of the city’s the city gives an overview of all the opportunities in vulnerability to a near 0.

Barcelona (Figure xx). This are the opportunities to develop 3500 Co-housing units (350000 sqm) , 42 Co-working spaces (197900 sqm), 73 Community spaces (29700 sqm), 57 Education facilities (57000 sqm), 21 Spaces for local businesses (26500 sqm), 160 spaces for amenities (53400 sqm), 51 Incubator Social 350000 sq.m / 3500 : Co-Housing units spaces (34500 sqm) and 770400 sqm of green spaces.

VIII. Framework

4.2

X. Stakeholders

Growth{Economic} 197900 sq.m / 42 : Co-Working spaces 26500 sq.m / 21 : Space for MSMEs 53400 sq.m / 16 : Space for amenities 34500 sq.m / 51 : Incubator spaces Environmental 770400 sq.m : Green Space

| XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection

Figure 109.

Results and Application

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Opportunities to create positive value in Barcelona

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Conclusi discus

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VIII. Framework | IX. Protocol | | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook

5.1 RE{CODE} methodology in the context of simulation platforms and policy tools

X. Stakeholders

ions and ssions

| XIV. Reflection

5.2 Global platform 5.3 Next steps and Future application

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5.1

VI. How?

RE{CODE} methodology in context of simulation platforms policy tools

the and

IV. Scientific Interest |

V. State of Art

|

The research addresses the issue of socioeconomic inequity through its methodology and the platform. These expand upon the outcomes and approaches taken by the states of art. East New York rezoning, Co-Housing Barcelona and Superilla Barcelona address a certain urban issue by implementing proposals that benefit a delimited area and community, increasing affordable housing stock, addition of co-housing facilities and remodeling infrastructure to increase green spaces respectively.

I. What?

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II. Global Context

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III. Barcelona Context |

As explored through the research, for sustainable development that benefits not just vulnerable communities, addressing multiple urban factors in parallel and correlation allows for proposing a more inclusive solution. Archistar, Metricmonkey, Morphocode and many more similar tools address the availability and use of urban data through digital platforms which can help urban planners and developers analyze and make informed decisions. Although these platform’s primarily focus on residential and commercial structures, thereby narrowing the scope to understand and plan for other functions in the city. The platform developed through the research enhances this through its incorporation of multiple stakeholders and the benefits for each, while proposing opportunities for socio-economic equality.

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VIII. Framework | IX. Protocol | X. Stakeholders

The hypothesis of the research was to decouple urban planning from land use and zoning to one based on evaluation of urban performance and deficiencies, which would result in sustainable proposals for socio-economic equality. The methodology followed and the resultant platform address the issues, but there are certain limitations in some of the methods deployed. The indicators, extracted from the guidelines and projects by city council, are not exhaustive and subjective to the dynamics they measure. And the machine learning regression with the diffusion algorithm used to project future impacts requires exclusive investigation for higher degree of refinement and accuracy.

| XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook

The research is successful in understanding urban parameters for equality at multiple scales and identifying vulnerable areas by creating the SocioEconomic Vulnerability index. It also resolves for solutions at multiple scales and validation of their potential by calculating the value that they would create complimented by their current and future impacts. The digital platform further widens this scope of development for socio-economic equality by involving multiple stakeholders and allowing for access to information to make informed decisions.

| XIV. Reflection |

Conclusions and discussions

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5.2

Global platform

Zooming out and looking back at the issue of gentrification, dispersion and segregation, improvement of socio-economic parameters in cities has the potential to create positive values for society.

Although these parameters are contextual and different for all cities across the world, understanding expansion and demographic change in relation to economic and living patterns can give us informed insights for planning policies for different cities.

To do so, a global platform is developed, as part of the overall RE{CODE} platform. This global platform allows the user to visualise data at country level. To understand gentrification, dispersion and segregation, improvement of socioeconomic patterns. parameters across themes of urbanization, demography, economy and social are focused in the platform. This data is also compiled for multiple decades, from 1990-2020, enabling the comparison across time. The global platform (Figure 110, Figure 89) operation is documented in a video (https://youtu.be/HSF1HpVLgs0).

III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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I. What?

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II. Global Context

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Figure 110.

Figure 111.

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Next steps and Future application

| IX. Protocol

Future applications of this research and platform involves building up on the research by taking steps for refinement in accuracy of the diffusion and machine learning algorithms and developing the platform into an online portal for global reach and ease of accessibility.

Next steps also involve expanding the scope of the global platform through investigation into identification of cities with similar urban scenarios as Barcelona and further validating the research’s methodology. In addition, creating a framework to identify indicators specific to cities will allow for the platform to create proposals for different cities with different urban dynamics.

VIII. Framework

5.3

| X. Stakeholders | XI. Platform

The research’s methodology of performance based redevelopment |

can affect multiple parameters in the city, creating a ripple effect of positive

........... | RE{CODE} |..........

change. And by implementing it can allow us to propose opportunities that, not only decrease imbalances and inequalities, improve urban performance but also create social and economic value for better wellbeing of societies.

XIII. Global outlook | XIV. Reflection |

Conclusions and discussions

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VIII. Framework

Appendix

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6.1_Referencing Barcelona’s vulnerability matrix - Ajuntament de Barcelona

IX. Protocol

7.1_Iceberg model 7.2_Stakeholder assessment 8.1_Research Cosmogram 9.1_Alternate Thesis direction - Proposal through aggregation algorithm

| X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

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VI. How?

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6.1

Referencing Barcelona’s vulnerability matrix reference - Ajuntament de Barcelona

Disposable family income (RFD) : In the territories of Barcelona with a minor RFDs detect high unemployment rates, of eviction files, of pregnancy between adolescents or dilapidated

Both statistical evidence and scientific buildings, bad or deficient. They also agree with low literature indicate that confinement of low-income population percentages with higher education. neighbors in the most disadvantaged neighborhoods contributes to the worsening of their conditions of life Unemployment : The distribution of and becomes an added equal barrier to opportunities unemployment has an obvious relationship with low and access to income and services. In this context, RFD rates, but statistically it also becomes apparent the program for the improvement of neighborhoods that in neighborhoods with high unemployment rates is conceived as an instrument in the struggle against more evictions occur, the state of the housing stock the rise of social inequalities in the city. It aims is more deficient and more related problems occur to address in a way integral and transversal the with health, such as pregnancy among adolescents. shortcomings and the problems of each territory, affecting in some strategic areas, such as next: Buildings in a dilapidated, bad or deficient - Equal opportunities for people with low incomes. state : As for the condition of the buildings, for - Economic activity, with special emphasis in one on the other hand, it is observed that in the the social economy, local trade, the recovery of neighborhoods they have a higher rate of dilapidated industrial capacity and improving employability. buildings there are higher rates of foreign population, - Urban deficits, public space or the equipment. unemployment and eviction proceedings.

- The quality and health of the housing stock. On the other hand, in these neighborhoods there are Educational opportunities. few commercial premises on the ground floor, lower - Physical and mental health conditions. life expectancy and a percentage low population - Attention to groups in special need. with higher education.

In all neighborhoods covered by this program structural deficits are detected in many of these Eviction proceedings : The neighborhoods areas, which generates serious social inequalities with the highest number of records of eviction among residents and the rest of the city needs to also have a high index of unemployment, teenage be corrected. These inequalities are evident when pregnancies, and old and dilapidated buildings. analyzing specific indicators quantifiable, such as the RFD, life expectancy and rate of population with territorial distribution of family income available, higher education are relatively low. the educational level of the population in higher education, the unemployment rate, the rate of teenage Higher education : In terms of studies, it pregnancy or the state of housing stock, to name a few. is observed that higher the index of the population

However, it is important to keep in mind with studies higher, the higher the RFD. On the other that the indicators are a reflection of a complex hand, neighborhoods with a lower rate population reality and that, in general, they are highly with higher education concentrate higher rates of interrelated to each other. Therefore, it is through unemployment, pregnancy among adolescents, a cross-analysis between the different indicators eviction proceedings and poor housing. that one can have a broad view of this reality and, consequently, to raise more effective actions to These data reveal the interrelationship of reverse the situation. By means of a statistical social, educational, economic, health and housing analysis of correlations are revealed some of these imbalances which occurs in the territories more interrelationships, at the scale of city, between the disadvantaged in terms of RFD. All some are different vectors that affect territorial inequality. discussed in more detail below of these relevant indicators in the territories covered by this program.

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VIII. Framework | IX. Protocol | X. Stakeholders | XI. Platform | ........... | RE{CODE} |..........

Table of correlations between urban variables Anon, Pla de BARRIS DE L’ajuntament de Barcelona. Pla dels barris de Barcelona. Available at: https://pladebarris. barcelona/ [Accessed September 21, 2021].

| XIV. Reflection

Figure 112 shows the correlation matrix developed by the Ajuntment de Barcelona to highlight the vulnerable population in the city. In blue are the positive correlations between variables and in red, the negative correlations. Values and color intensity represent the magnitude of correlation.

XIII. Global outlook

Figure 112.

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Appendix

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7.1

Iceberg Model

Iceberg model is a tool that allows you to shift your perspective and see beyond the immediate events that everyone notices. It helps you to uncover root causes of why those events happen. That’s possible by looking at deeper levels of abstraction within the system that are not immediately obvious.

(UNTOOLS 2021)

Iceberg model consists of four levels: //Events

-What is happening right now? //Patterns

-What has been happening over

time? What are the trends? //Structures

-What’s influencing these patterns?

-Where are the connections between patterns?

//Mental models

-What values, beliefs or assumptions

shape the system?

Figure 113 shows the Iceberg model of RE{CODE}

Figure 113.

Iceberg Model Amran, A., Iceberg model. Tools for better thinking.

Available at: https://untools.co/iceberg-model [Accessed September 18, 2021].

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Stakeholder Assessment

IX. Protocol

Figure 114.

|

All possible internal entities, people and teams an organizational project will involve and affect are called stakeholders. Stakeholder assessment is the process of identifying these key stakeholders before the project begins. Further grouping them according to their levels of participation, interest, and influence along with determining how best to involve and communicate each of these stakeholder groups helps address conflicts and issues early on.

Figure 114 shows the list of potential stakeholders that could be involved in RE{CODE}.

Figure 115 shows the distribution of these stakeholders based on their level of interest. Figure 116 shows the matrix of their contribution.

Figure 117 shows the potential timeline of execution for the thesis.

VIII. Framework

7.2

List of stakeholders | X. Stakeholders | XI. Platform

Figure 116. |

Matrix of contributions

........... | RE{CODE} |..........

XIII. Global outlook | XIV. Reflection |

Appendix

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V. State of Art

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VI. How?

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Negative interest

Figure 115.

Distribution of stakeholders based on level of interest

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IX. Protocol

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X. Stakeholders

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XI. Platform

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Potential time-line of execution

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Appendix

........... | RE{CODE} |..........

Figure 117.

8.1

Research Cosmogram

In the course of the research, the cosmogram diagram served as a visual guide to place the many aspects of the thesis in relation to one another. This included everything in terms of initial research ideas, states of art, references, case studies, design development, research lines at different stages, outputs , parameters, and more. This diagram was developed during the initial stages of the thesis, to consolidate ideas developed so far, and to give a clear perspective for next steps in developing the thesis in a complete sense. Figure 118 is the Cosmogram of the thesis RE{CODE}.

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Figure 115.

RE{CODE} Cosmogram

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VIII. Framework

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IX. Protocol

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X. Stakeholders

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XI. Platform

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........... | RE{CODE} |..........

XIII. Global outlook

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XIV. Reflection

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Appendix

8.1

Alternate Thesis direction - Proposal through aggregation algorithm

Like with most thesis projects, many research lines were explored. Not all research, and methodologies developed from these research lines are pursued or incorporated in the project, as some of the research do not fit in the overall development of the thesis.

Although most of the research lines were relatively insignificant in its magnitude to have changed the course of the thesis, the following work developed through aggregation algorithm could have resulted in a drastically different thesis.

During the course of the project, after having done the urban analysis and understanding the types of interventions that can potentially reduce vulnerability, the thesis aim was reviewed. There were three main ways in which the question “How to develop the protocol for proposal?” was addressed.

One was to develop a digital platform that allowed for creation of proposal as per stakeholder inputs.This method evolved to become the RE{CODE} methodology, as elaborated through this book.

Second was to develop an algorithm that would give a proposal as an architectural design based on types of interventions and vulnerability.

This method is the aforementioned aggregation methodology. And lastly, one hypothetical method as a combination of both.

The aggregation methodology

This research direction was opted as a method to design proposals from a list of programs and areas into an architectural output, a building.

For this, aggregation algorithms and aggregation plug-ins were used. In this thesis, WASP plugin for Grasshopper was chosen as a starting point. Figure 116 to Figure 122 shows the outputs using the WASP plugin.

Figure 116.

Volume connection possibilities

Figure 117.

Sample site to test the algorithm

Figure 118.

Catalogue of all aggregation possibilities

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

I. What?

Rossi, A., WASP - A COMBINATORIAL TOOLKIT FOR DISCRETE DESIGN. Food4Rhino. Available at: https:// www.food4rhino.com/en/app/wasp.

Erioli, A., ASSEMBLER. W.I.P, Developed by Co-de-iT

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Figure 119.

Example 01 from the catalogue

RE{CODE}

VIII. Framework

This plugin enabled the possibility to assign strict rules to the assemblage, giving the same output everytime for the same inputs. Figure 123 to Figure 133 shows tests carried using ASSEMBLER.

|

Example 02 from the catalogue

IX. Protocol

Figure 120.

Figure 129.

Figure 123.

| X. Stakeholders

Figure 130.

Figure 124. | XI. Platform

Figure 121.

Figure 131.

Example 03 from the catalogue

........... | RE{CODE} |..........

Figure 126.

|

Figure 125.

Figure 132.

XIII. Global outlook

Figure 122.

Aggregation in volumetric constraints with streets

Figure 127.

|

Figure 133.

Figure 123 - Figure 133.

Tests using ASSEMBLER

Appendix

|

Figure 128.

XIV. Reflection

There are many limitations to the WASP aggregation. As the algorithm is based on Stochastic probability, the outputs are not definite and same , even when given the same inputs. This means less control over the rules and specifications for the output.

Hence, a different aggregation method using ASSEMBLER was carried. ASSEMBLER is a work in progress plugin for grasshopper developed by Andrea Graziano and Alessio Erioli of Co-de-iT.

111

The method using ASSEMBLER resulted in promising outputs. Thus, further exploration was carried over a sample site. Figure 134 to Figure 1 shows this line of experimentation.

Figure 134.

Designed parts to connect in the assemblage

Module 01

Module 02

Module 04

Module 05

Module 03

Figure 135 - Figure 136.

Catalogue of possible connections

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

V. State of Art

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VI. How?

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VII. Methodology

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Nihar Mehta

I. What?

Figure 138.

Highlighting the components in the assemblage

112

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VIII. Framework

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IX. Protocol

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X. Stakeholders

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Assemblage over a sample site

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|

XIV. Reflection

Module 05

Module 02

Vertical Circulation

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Module 04

XIII. Global outlook

Module 03

........... | RE{CODE} |..........

Module 01

XI. Platform

Figure 137.

Bibliography Department of Economic and Social Affairs, United Nations. 2018. “68% of the world population

projected to live in urban areas by 2050, says UN”. 2018 Revision of World Urbanization Prospects

VII. Methodology

|

Nihar Mehta

|

Dr Ernest I. Hennig, Professor Jochen A. G. Jaeger, Tomáš Soukup, Erika Orlitová, Christian

Schwick, Die Geographen, Professor Felix Kienast. Urban sprawl in Europe.

Luxembourg: Publications Office of the European Union, 2016. https://www.eea.europa.eu/publications/urban-sprawl-in-europe

IV. Scientific Interest |

V. State of Art

|

VI. How?

Vandecasteele I., Baranzelli C., Siragusa A., Aurambout J.P. (Eds.), Alberti V., Alonso Raposo

M., Attardo C., Auteri D., Barranco R., Batista e Silva F., Benczur P., Bertoldi P., Bono

F., Bussolari I., Caldeira S., Carlsson J., Christidis P., Christodoulou A., Ciuffo B.,

Corrado S., Fioretti C., Galassi M. C., Galbusera L., Gawlik B., Giusti F., Gomez J.,

Grosso M., Guimarães Pereira Â., Jacobs-Crisioni C., Kavalov B., Kompil M., Kucas

A., Kona A., Lavalle C., Leip A., Lyons L., Manca A.R., Melchiorri M., Monforti Ferrario F., Montalto V., Mortara B., Natale F., Panella F., Pasi G., Perpiña C., Pertoldi

M., Pisoni E., Polvora A., Rainoldi A., Rembges D., Rissola G., Sala S., Schade S.,

Serra N., Spirito L., Tsakalidis A., Schiavina M., Tintori G., Vaccari L., Vandyck T.,

Vanham D., Van Heerden S., Van Noordt C., Vespe M., Vetters N., Vilahur Chiaraviglio

N., Vizcaino P., Von Estorff U., Zulian G. The Future of Cities - Opportunities,

challenges and the way forward. Luxembourg: Publications Office of the European

Union, 2019. https://ec.europa.eu/futurium/en/system/files/ged/the-future-of-cities_ online.pdf Hannah Ritchie and Max Roser. 2020.“Energy”. Published online at OurWorldInData.org.

Retrieved from: https://ourworldindata.org/energy Max Roser. 2013. “Future Population Growth”. Published online at OurWorldInData.org.

Retrieved from: https://ourworldindata.org/future-population-growth

III. Barcelona Context |

Hannah Ritchie and Max Roser. 2013.“Land use”. Published online at OurWorldInData.org.

Retrieved from: https://ourworldindata.org/land-use Center for International Earth Science Information Network - CIESIN - Columbia University.

2013. “Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area

Estimates”. Version 2. Published online at WorldBankGroup.org. Retrieved from:

https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2

II. Global Context

|

Center for International Earth Science Information Network - CIESIN - Columbia University.

2013. “Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area

Estimates”. Version 2. Published online at WorldBankGroup.org. Retrieved from:

https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2

|

Marta Roig, included Maren Jiménez, Alex Julca, Hiroshi Kawamura, Martijn Kind, Yern Fai

Lee, Jonathan Perry and Julie Pewitt. “Inequality in a rapidly changing world”. United

Nations publication, 2020. https://www.un.org/development/desa/dspd/wp-content/ uploads/sites/22/2020/01/World-Social-Report-2020-FullReport.pdf

I. What?

Municipal Institute of Informatics. “Land uses of the city of Barcelona”. V1. 2016. Distributed

by Open Data BCN Institut Municipal d’Educació de Barcelona. “Regulated education of the city of Barcelona”. V1.

2019. Distributed by Open Data BCN 114

RE{CODE}

VIII. Framework

Municipal Data Office - Department of Statistics and Data Dissemination. “Demographic

indicators. Population density (inhabitants/ha) of the city of Barcelona”. V1. 2019.

Distributed by Open Data BCN

|

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Register of

inhabitants. Population of the city of Barcelona according to sex, five-year age and

nationality”. V1. 2019. Distributed by Open Data BCN

IX. Protocol

Institut Municipal d’Educació de Barcelona. “Non-Regulated education of the city of

Barcelona”. V1. 2019. Distributed by Open Data BCN

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Life expectancy of

the city of Barcelona”. V1. 2019. Distributed by Open Data BCN

|

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Average surface of

the housing premises of the city of Barcelona”. V1. 2019. Distributed by Open Data BCN

X. Stakeholders

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Area size of ​​

the premises and services of the cadastral constructions of the city of Barcelona”. V1.

2019. Distributed by Open Data BCN

|

Gerència de Turisme, Comerç i Mercats. “Economic activities census on the ground floor of the

city of Barcelona”. V1. 2019. Distributed by Open Data BCN

XI. Platform

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Percentage of

population aged 16 years and over with university studies”. V1. 2019. Distributed by

Open Data BCN

|

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Disposable

household income per capita in the city of Barcelona”. V1. 2019. Distributed by

Open Data BCN

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Average age of

cadastral buildings of the city of Barcelona”. V1. 2019. Distributed by Open Data BCN

Departament del Pla de la Ciutat - Direcció d’Informació de Base i Cartografia - Institut

Municipal d’Informàtica (IMI) - Ajuntament de Barcelona. 2011. PARCEL·LARI. Dades

de la CIUTAT en format SHP. Distributed by CartoBCN

XIII. Global outlook

Andrew Heumann. Human UI, Interface paradigm for Grasshopper.V 0.8.1.3. PC. 2019

|

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Average cost of building service”. V1. 2019. Distributed by Open Data BCN

........... | RE{CODE} |..........

Oficina Municipal de Dades - Departament d’Estadística i Difusió de Dades. “Weight of the

registered unemployment in the population from 16 to 64 years of age of the city of

Barcelona”. V1. 2019. Distributed by Open Data BCN

Social Value Portal Ltd. 2021. “The National Social Value Measurement Framework For Wales”.

Part 3 - The National TOMs Wales (Measures, Values and Sources)

Bibliography

|

Nathan Miller. Lunchbox, Machine Learning for Grasshopper. PC. 2020

XIV. Reflection

Flanagan, Barry E.; Gregory, Edward W.; Hallisey, Elaine J.; Heitgerd, Janet L.; and Lewis, Brian.2011. “A Social Vulnerability Index for Disaster Management”. Journal of Homeland Security and Emergency Management: Vol. 8: Iss. 1, Article 3.

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VII. Methodology

World Bank national accounts data, OECD National Accounts data. 2018. “Economic Policy &

Debt: National accounts”. Published online at Worldbank.org.

Retrieved from: https://data.worldbank.org/indicator

|

World Bank national accounts data, OECD National Accounts data. 2018. “ Environment:

Density & urbanization”. Published online at Worldbank.org.

Retrieved from: https://data.worldbank.org/indicator

VI. How?

Dutch, Bicycle. “The Barcelona Superblock of Poblenou.” BICYCLE DUTCH, November 7,

2017. Retrieved from https://bicycledutch.wordpress.com/2017/11/07/the-barcelona superblock-of-poblenou/ Anon, Pla de BARRIS DE L’ajuntament de Barcelona. Pla dels barris de Barcelona. Available at:

https://pladebarris.barcelona/ [Accessed September 21, 2021].

|

Amran, A., Iceberg model. Tools for better thinking. Available at: https://untools.co/iceberg model [Accessed September 18, 2021].

V. State of Art

Rossi, A., WASP - A COMBINATORIAL TOOLKIT FOR DISCRETE DESIGN. Food4Rhino.

Available at: https://www.food4rhino.com/en/app/wasp.

I. What?

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II. Global Context

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III. Barcelona Context |

IV. Scientific Interest |

Erioli, A., ASSEMBLER. W.I.P, Developed by Co-de-iT

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RE{CODE} Urban simulation platform for socio-economic wellbeing Author Nihar Mehta

Developed at

MAA02

Master in Advanced Architecture