We previously ide need for cities for both climate resilie
However, while th of research to su design of climate there is an insuff research to supp resilient spa
entified the urgent to be designed e and pandemic ence.
here is a plethora upport the spatial e resilient cities; ficient amount of port a pandemic tial strategy.
We believe tha future pandemics developed a pan spatial strategy an tool to add
It is essential f planners and othe to be able to test impact their spat the spread of infe
at there will be s, and so we have ndemic resilient nd computational dress this.
for architects, er urban designers and visualise the ial design has on ectious diseases.
Therefore we p computational d optimises the sp cities for mitigati infectious
Speculate > Generate
propose vectr, a design tool that patial design of ing the spread of s diseases.
e > Simulate > Analyse
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/CONTENTS
Chapter 1: Project Introduction .............. 12 The Brief and Design Premise High Level Theories Spatial Strategy Building Typology Library Simulation Strategy
Chapter 2 : vectr ......................................... 98 Overview Computational Breakdown Instruction Manual
Chapter 3: Analysis .................................. 154 Set-Up Preliminary Analysis Detailed Analysis
Chapter 4: Conclusions ......................... 200
Dantzic Street, Lower Irk Valley
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Chapter 1
PR
ROJECT INTRO
//Studio 1
Design Brief
/Project Journey
This portfolio focuses on Studio 3 where we finish developing and building our computational tool (vectr) alongside the building typology library that we have developed across all three studio projects. We then generate, simulate, compare and analyse multiple masterplan iterations in vectr to determine the optimal spatial configuration to reduce the spread of infectious diseases.
Future Cities 200
500
1000m
H Th
The Northern Gateway
September 2020
Here is a breakdown of how our project has developed from the start of Studio 1 to the end of Studio 3.
Scale 1:12500
0
Site Analysis & Urban Research
The Premise: Design for both
Climate Resilience AND Pandemic Resilience
4
3
2
1
De
High Level Theories & heir Urban Application
//Studio 2
//Studio 3
Building Typology Library
Analysing & Evaluating Masterplan Iterations
Threshold
Simple (simulation) & Detailed Generative Models
Designing for Resilience
Spatial Strategy:
= Commercial
Infection Graph / Heatmap / Hotspots / Trails
May 2021
= Residential
= Education
= Green Space
= Community
= Healthcare
= Industrial
= Woodland
= Transport
Conclusions
Infection Stats
Designing / Building the Computational Tool
With a Focus on Pandemic Resilience
10 00
Cluster 1
m
Cluster 2 1000m
1) Generate
2) Optimise
3) Simulate
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ecentralisation / Adaptability / Connectivity / Urban Greening / Walkability
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THE BRIEF & DESIGN PREMISE An overview of the project brief and a justification for the need to design cities for both climate and pandemic resilience.
1.1 /The Brief //CPU Atelier Over view The CPU (Complexity, Planning and Urbanism) Atelier brief focuses on using complexity framework to develop digital design tools, computational thinking and urban theory. This year the atelier is focusing on: Resilient Urban Futures.
//Project Brief
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3
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Our task is to work as a consultant for Manchester City Council (MCC) on the Northern Gateway redevelopment project (a joint venture between MCC and the Far East Consortium). To do this we will need to: examine and analyse the existing site and proposed development plans from a future city perspective, in order to ultimately determine how the project could become more resilient.
The Nor thern Gateway Development
Manchester Cit y Council (MCC)
The Far East Consor tium (FEC)
CPU Atelier (consult ant)
MCC ’s L ist of Challenges and Oppor tunities: 1: How can a balance between public and private spaces foster a sense of community and belonging in new urban morphology. Ensure public spaces are active throughout the day and evening and do not adversely interfere with a residential setting (in terms of noise, ASB etc.)? 2: The distribution of facilities, amenities and community spaces is an essential aspect of successful residential development. How do we design to ensure this aspect of sustainability in urban strategy and design. 3: How can a new urban development be designed to change and adapt with its residents (from students to young professionals, families and aging) 4: How can a network of high-quality open and public spaces support well-being and enhanced diversity. Integrating green spaces/public realm towards wellness and mitigation of climate change? Ecologies? How can you integrate green environments and the City River Park ecosystem? 5: How can you design for sustainable movement and minimise motorised transport use? Consider last mile/3 mile responses including transport oriented design, walk-ability, cycling and technological disruptions (CAV). 6: How to design zero-carbon future cities (is urban morphology adequate). How do you understand the environmental impact of future cities.
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Address the need to design for pandemic resilience (the low transmission city).
Mean Global Temperature Change (oC) 6
5
1.1
4
/The Premise //The Rise of Pandemics Alongside Global Warming In Studio 1 we highlighted the importance of designing for both climate change and infectious diseases. This timeline shows the severity of the two global issues. As the population has rapidly increased due to advances in technology and medicine, so has the carbon emissions and therefore the global mean temperature.
3
2
Spanish
Russian Flu: 1 1 The Third Plague: 12M Deaths Cholera 6: 1M Deaths 0
-1 1700
1750
1800
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3
2
1
The s
Temperatur P
Global Population
6 Pandemics in 200+ years
6 Pandemics in 20 years
Target trajectory COVID-19: 1.23M Deaths
10 Billion
Ebola: 11.3k Deaths MERS: 850 Deaths Swine Flu: 200k Deaths SARS: 770 Deaths Flu: 40-50M Deaths 5 Billion
1M Deaths HIV/AIDS: 25-35M Deaths (Ongoing) Hong Kong Flu: 1M Deaths
Asian Flu: 1.1M Deaths 0 1850
1900
1950
2000 (2016)
2050
2100
Year
Pandemic data adapted from: “Visualising the History of Pandemics” (LePan, 2020) re data adapted from: “The Physical Science Basis, Summary for Policy-makers” (IPCC AR5, 2012) Population data adapted from: “World Population Growth” (Our World in Data, 2019)
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simult aneous trajector y of Pandemics and Climate Change
1.1
Generate urban design based on Sustainability principles
/Thesis Statement In Studio 1 we argued that the two greatest threats to the human race are climate change and the emergence of infectious diseases (pandemics). However, there seems to be a void in research regarding the effect of spatial design on the spread of diseases, particularly at an urban scale.
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3
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Therefore, with vectr we propose a computational tool that simulates the spread of an infectious disease in urban design, where the user can create the urban design from scratch or import an existing model for analysis.
Sliders
Optimise
Simulate
it’s spatial form
it for the spread of an infectious disease
Metric Graph
it’s performance
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Analyse
1.1 /Site Location The generative design tool we create will be able to be applied to any site in the UK. We have chosen the Northern Gateway development projects as a case study to test it. Manchester
//Urban Setting The Northern Gateway is located roughly 2.1km North-East of Manchester City centre (Centroid to centroid), at 155 hectares in area - it is one of the largest regeneration projects in Europe.
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As consultants for our clients: Manchester City Council, we have been asked to propose an alternative solution to the SRF Master plan (see “Proposed Master plan”).
Manchester Location
HMG Paints (key listed building) Northern Gateway Area: 155ha
Victoria Station
Royal Mail Sorting Site
E xisting Site Public Services
Residential Zones
Manchester City Centre
Mixed Residential / Commercial Zones
Community “focal points” Proposed Central Transport Hub
Scale 1:50000
Scale 1:20000
Site Location
Proposed Masterplan
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“SRF Development Zone A”
1.1 /Key Concepts for Design //Summar y Having looked at the key considerations for urban de-carbonisation, and how one can design for capacity that absorbs the severe disruption of a pandemic, the identified areas of focus are outlined as: - Adaptability - Green Space - Walkability - Decentralisation
Areas of priority
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Areas of consideration
Climate Resilience
/ Movement Prioritise Cyclists + Pedestrians Sustainable transport (electric vehicles) Hyper-proximity, walkability, urban clusters Dual-lane, wider pavements Pedestrianised streets Public
Transport modes
Individual
Centralised
Transport hubs
De-centralised
Open
Circulation
Restricted
/ Urban Form Adaptive Typologies: urban, site and building scale Urban greening Mixed-use buildings, decentralised building types Adaptive re-use of existing urban form Proximity to green space, location of green space Affordable housing, particularly for key workers Public
Increase Green space
Private
Closed-loop
Ventilation
Natural
Shared amenities
Typology
Individualised
South
Orientation
West
High
Compactness
Low
High
Density
Low
Low
Porosity
High
Pandemic Resilience
/ Material Flows Renewable energy sources Multiple, decentralised sources Conservation use and re-use where possible Self-sufficient use of all material flows Waste
Hygiene + disposal
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Circular economy
A comprehensive flood management plan is needed: A carefully thought out plan will need to be implemented to reduce the likelihood of the River Irk from flooding in the future. This should be based on the Greater Manchester Strategic Flood Risk Management Framework (SFRMF). High Risk Medium Risk Low Risk
1.1 /Proposal Critique //Summar y Insufficient provision of green space: More green space should provided (126,050m2) and distributed across the site to meet the WHO’s recommendation of 9m2 per person. 316,206m2
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E Pr xist op in os g ed
Based on our Studio 1 research on the zero-carbon city and the low transmission city, we analysed the Northern Gateway proposal to see if/how the proposal has been designed for climate change and/or the spread of infectious diseases. We have summarised our findings on the right. Our analysis of the Northern Gateway proposal reinforces the need to design for both climate and pandemic resilience.
323,950m2
y
Areas of the proposal are conducive to low air flow: Streets should be orientated towards the prevailing wind to improve air movement outside and inside (for naturally ventilated buildings) to reduce the spread of infectious diseases. Sufficient air flow is needed within cities to mitigate against the formation of urban heat islands.
Density should be distributed: High population densities increase the rate of infectious diseases spreading. However compact cities are a positive when dealing with energy flows. High
Medium - low
A lack of healthcare provision: The proposed master plan does not show any increase in the provision of healthcare services despite the SRF (Strategic Regeneration Framework) document acknowledging the need for its increased capacity in the development. Healthcare facilities are an integral part of a city’s response to climate and pandemic related disasters.
Scale 1:20000
= Residential
= Water
= Commercial
= Healthcare
= Education
= Industrial
= Transport Hub
= Community
= Green Space
Additional Transport Hub Needed: The central public transport hub location does not take into account the number of people using it which presents a problem when managing the spread of infectious diseases.
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//Nor thern Gateway Proposal
Eastford Square, Dalton Street
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HIGH LEVEL THEORIES A summary of the key theories we looked at in Studio 1 that have informed our spatial strategy and the design approach of our computational tool in Studio 2.
A system involves a set of elements and the relations between them
element
relation
The process of emergence occurs when elements and relations are arranged in a specific order which allows them to function as an entirety
1.2 /Complex Systems There is no set definition of what a complex system is despite scientists studying it across different disciplines. Therefore based on our research we have attempted to categorise the formation and characteristics of a complex system instead. This will inform our approach and decision making when re-designing an urban area (a complex system).
Emergence
Complexity theory contributes to this model, leading to the formation of a complex system. Net
wo r k T heory
Se lf-
Syste
ising
ms Theo
g an
ry
Or
No n in
ea
n
-L rS
yst
em s
A da p
ta
ti o
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//Complex Systems
Interdependence Evolution Chaos Theory
Phase Transition Feedback Loops
Dynamic
Non-linear Systems Theory
Adaptive Systems Theory
Diversity
Complex Adaptive Systems The Butterfly Effect Systems Dynamics
Complexity Theory
Systems Theory
Information
SelfOrganisation Theory
Efficiency Energy Differentiation
Emergence Cellular Automata
Network Theory
No Centralised Control
Vulnerability
Graph Theory
Connectivity
Data Robustness
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//Underst anding Complexit y Theor y
1.2 /Designing for Resilience Resilience theory looks at the probability of a system’s extinction and it’s ability to tolerate disruptions without collapsing. In this project we will design an urban area that is able to absorb different eventualities and disruptions (climate and pandemic related) without collapsing.
1 / Connectivit y and Decentrali
An interconnected (not segregated) and decentralised syst reduces vulnerabilities to failure.
Gr severe su
// Principles of a Resilient System (Using Green Space as an E x ample) Green space that can be transformed into mobile testing centres in the event of a pandemic
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3
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1
4 / Adapt abilit y
Disruptions are unexpected so systems need to be able to ac future changes.
sation
A wide distribution of categories at different scales / levels allows a system to better adapt to changes.
Public Park
reen space that can absorb urface water in the event of flooding
ccommodate / absorb any
Botanical Garden
Allotment Garden
Garden Terrace
Rooftop Garden
3 / Diversit y Diversity encourages learning and experimentation. In the event of a disaster it is more likely that one of the categories is the key to survival.
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tem (e.g. infrastructure)
2 / Variet y in Scale
Buildings and open space
1.2 /Urban Morphology
Plots and secondary links (roads)
//Proposed Approach Artery (Energy Flows)
Link (Transport)
Volume
Void
Blocks (Clusters) and main links (e.g. roads and railways) This urban morphology considers the urban landscape as a series of clusters, each with decentralised amenities, and connected by a hierarchy of link networks. The urban form is inter-woven by patches and strings of open space or “void”, this relationship of volume and void is adaptive, where the urban fabric is able to expand, convert and displace. Evolutionary urbanism is a “bottom-up” methodology, which considers disruptions or stimuli at building scale to take affect on the urban fabric it sits within.
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Urban Fabric
Urban Form L ayers
Stimulus Agent
Surrounding Adapt ation
A significant new building type is built
Nearby buildings respond, the urban form adapts
Uncontrolled E xpansion
System stabilises to an equilibrium
Evolutionar y Urbanism
Knock-on effect as other buildings continue development
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Self Organisation
Complex Systems
1.2
Adaptive Systems Theory
Process of Evolution
Agent Based Modelling
Computational Tools
/Complex Adaptive Systems
Cellular Automata
Feedback Loops
(CAS) CAS are a type of complex system that has the capacity to adapt. It is understood through the interaction (acting and reacting) between different agents (e.g. people or land use) which are governed by simple rules in their local environment. From this, global patterns emerge which would not have been predicted by looking at the agents in isolation.
Complex Adaptive Systems
Diversity
Competition
Self-Organisation No one is in control
Cellular Automat a (CA) Modelling CA modelling is a computational tool used to study complex adaptive systems. It involves giving agents simple rules of interaction within an environment and then running a simulation to see how the system adapts/evolves over a period of time. In this project we will use CA modelling to allocate land use in our masterplan.
Examples Everywhere Ecosystems
Governance Cities
Human Immune system
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//CA S Characteristics
1) Identify Agents
2) Select Environment
3) Set Number of Neighbours
(e.g. land use types)
(e.g. plot boundaries you want land use agents to occupy)
(e.g. an agent’s proximity to it’s 3 closest neighbours)
IF
THEN
DO
4) Set Starting State
5) Set Simple Rules of Interaction
6) Determine Simulation Time
(e.g. random allocation or based on existing plot use)
(e.g. IF residential is next to industrial, THEN kill (DO) residential)
Specify a period of time (n) to see how the system adapts.
7) Run Simulation
8) Observe Changing Interactions
9) Analyse/Utilise Resulting Pattern (e.g. the results indicate which land use type will be located in each plot)
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// CA Modelling Proces s
1.2 /Application of Theories
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This diagram shows how the key theories we researched in Studio 1 influenced our spatial strategy in Studio 2.
Complex Systems (CS)
Complex Adaptive Systems
Resilience Theory
Urban Morphology
(CAS)
(Ecological Resilience)
(UM)
Threshold
Decentralisation of Land Use & Density
Adaptive Land Use / Building Typologies
Cluster Formation
Cluster Connectivity
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Land Use Allocation
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SPATIAL STRATEGY
//The Decentralised Cit y Distributed density and land use across the city
1.3 /Key Urban Principles This is a summary of research completed in Studio 2 on the pandemic resilient urban principles that influenced how we developed our spatial strategy for vectr.
A decentralised city is less vulnerable to failure (e.g. enter lockdown) in a pandemic as interactions are reduced when services are distributed across a city.
//L and Use Goals Residential = Matches NG Proposal Healthcare = increase to 0.08m2 pp based on NHS England’s Benchmark Water = Matches Existing Site Education = Matches NG Proposal Community = Matches NG Proposal Commercial = Matches NG Proposal Industrial = Matches Existing Site Green Space = Increase to 9m2 pp based on the WHO’s Benchmark
0
50K 100K 150K 200K 250K 300K 350K 400K Area (m2)
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From our analysis of the existing site and the NG proposal’s provision of land use we set land use goals for ReSIM (based on a population of 50,000 people).
ot
el
Industri f i ce al Of
R
id en
munity Re t ai l
//High Connectivit y
City Cluster es
H
//The Walk able (15 minute) Cit y
tia l
Transport Hub
Com
Hospit alit y
Transport Hub
Key Transport Route
at
ca
uc
re
Ed
City Cluster
io
n
G ree
n Space He
alt
h
1000m Cluster Diameter = 15 minute walk
Our clusters are designed for walkability, with their diameter based on the 15 minute walkable city. They contain everything needed for a city to function.
Clusters are located along key transport routes to allow us to decentralise amenities. Transport hubs are located at cluster centres to increase connectivity.
//Adaptive L and Use
//Urban Greening Communal Rooftop Garden Private Green Balconies
Overflowing river in a climate disaster
Sky Garden
Overcapacity hospital in a pandemic disaster
Sky Garden Green Facade Ground Floor Garden
A comprehensive green space strategy benefits our climate and pandemic resilient design. Green space provision = 450,000m2 (based on WHO benchmark).
47
We are locating land use based on it’s likeliness to be impacted and forced to adapt in a pandemic or climate related disruption (our proximity preference).
The Rive
= Residential
1.3
Scale 1:12500 0
200
500
1000m
/Generative Spatial Strategy In Studio 2 (using the Northern Gateway as a case study example), we developed our pandemic resilient spatial strategy. Here is a summary of the step-bystep process we went through to design and build our computational tool’s generative spatial strategy.
1) Import Existing Site Geometry
=
= Green Space
=
= Healthcare
=
= Woodland
= 2) Selec
New Str Networ
Retained Road
Route to Connect to = Residential
At the end (step 12) are a series of sliders that the user of our computational tool will be able to control Scale 1:12500 to change different spatial aspects of the masterplan 0 200 500 generated.
= Commercial
= Commercial
= Green Space = Healthcare
1000m
5) Set External Transport Routes to Connect to
= Woodland
6) Gen
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From typolo
9) Allocate Land Use
10) As
er Irk
The River Irk
10 00
Cluster 1
m
Non-Buildable Area Village Park
Cluster 2 1000m
Listed Building Rochdale Road
ct Elements to be Retained
Rochdale Road = Residential = Green Space = Healthcare = Woodland
= Commercial
= Residential
= Education = Community = Industrial
= Transport 3) Set Restricted Area Around Rivers
= Commercial
= Education
= Residential
= Green Space
= Community
= Green Space
= Healthcare
= Industrial
= Healthcare
= Transport
= Woodland
= Woodland
4) Locate Cluster(s)
reet rk
Adjusted cluster centre along key transport route
7) Generate Plots
m building ogy library
ssign Building Typologies
8) Locate Transport Hub(s)
Rigidity Compactness Porosity Density Single or Mixed-Use
11) Assign Building Typology Mix
Function
12) Adjustable Spatial Parameters
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nerate the Street Network
Transport Hub
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BUILDING TYPOLOGY LIBRARY In Studio 1 we created a Building Typology Booklet and Grasshopper Library (https://drive.google.com/drive/ folders/13s8hreYrldlJEJVoMLLAzyIOhuR30nfs?usp=sharing) as a CPU MArch 2 Atelier. Then, in Studio 2 we started to build vectr based on this research. This section shows how we developed the building typologies further in Studio 3.
Tower Typology:
1.4
Block Typology:
/Parametric Design //Simulation Building Typologies
Perimeter Typology:
Our computational tool has five base building typologies that have been designed to automatically adjust shape and size depending on the plot size selected by the user.
Linear Typology:
Terrace Typology:
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3
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1
30
The user will be able to adjust the height of the buildings however these will be constrained depending on the building typology/use.
Plot Width, m
53
90
3%
1.4 /Hospitality Building(s) //Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s hospitality building typology.
//Propor tion of L and Per Cluster
Hospitality should be located next to green space and office plots but away from healthcare and education plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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//Proximit y Preference
Mixed-use additions: - Green space (roof) - Office (01 & above)
single-use
mixed-use
Function
//Det ailed Model
0
40 2-4
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 8
Number of Access Points
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//Simulation Model: Block Typology
40%
1.4 /Residential Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s residential building typologies.
Residential plots should be located next to green space plots but away from industrial plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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3
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//Proximit y Preference
Mixed-use additions: - Office (00 floor)
Mixed-use additions: - Retail (end terrace)
single-use
mixed-use
single-use
mixed-use
Function
Function
0
40
0
2-4
40 5-8
Density (number of floors)
0
Density (number of floors)
10
0
3
10 3
Floor to Floor Height (m)
10 6
0
10 4
Number of Access Points
Number of Access Points
//Simulation Model: Terrace Typology
//Simulation Model: Perimeter Typology 57
0
Floor to Floor Height (m)
40%
1.4 /Residential Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s residential building typologies.
Residential plots should be located next to green space plots but away from industrial plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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3
2
1
//Proximit y Preference
Mixed-use additions: - Green space (roofs) - Office (top floor) - Retail (00 & 01 floors)
Mixed-use additions: - Hospitality (00 floor)
single-use
mixed-use
single-use
mixed-use
Function
0
Function
40
0
40
5-8
17-32
Density (number of floors)
0
Density (number of floors)
10
0
10
3
3
Floor to Floor Height (m)
0
Floor to Floor Height (m)
10
10 1
Number of Access Points
Number of Access Points
//Simulation Model: L inear Typology
//Simulation Model: Tower Typology 59
2
0
1.4 /Residential Building(s)
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//Det ailed Model: Terrace
//Det ailed Model: Tower
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//Det ailed Model: Perimeter
0.3%
1.4 /Healthcare Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s healthcare building typology.
Healthcare plots should be located next to green space and hotel plots but away from industrial, hospitality and education plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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3
2
1
//Proximit y Preference
single-use
mixed-use
Function
0
40
//Det ailed Model
2-6
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 4
Number of Access Points
63
//Simulation Model: Block Typology
6%
1.4 /Education Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s education building typology.
Education plots should be located next to community plots but away from healthcare and hotel plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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3
2
1
//Proximit y Preference
single-use
mixed-use
Function
0
40
//Det ailed Model
2-6
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 4
Number of Access Points
65
//Simulation Model: Block Typology
1%
1.4 /Hotel Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s hotel building typology.
Hotel plots should be located next to healthcare plots but away from green space and education plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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3
2
1
//Proximit y Preference
Mixed-use additions: - Hospitality (00 floor)
single-use
mixed-use
Function
0
40 5-8
//Det ailed Model
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 4
Number of Access Points
67
//Simulation Model: Perimeter Typology
1.7%
1.4 /Community Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s community building typology.
Community plots should be located next to education plots but away from retail and office plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
4
3
2
1
//Proximit y Preference
single-use
mixed-use
Function
0
40
1-3
//Det ailed Model
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 4
Number of Access Points
69
//Simulation Model: Block Typology
3%
1.4 /Retail Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s retail building typology.
Retail plots should be located next to industrial plots but away from community plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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//Proximit y Preference
Mixed-use additions: - Hospitality (00 floor)
single-use
mixed-use
Function
0
40 2-8
//Det ailed Model
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 8
Number of Access Points
71
//Simulation Model: Block Typology
3%
1.4 /Industrial Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s industrial building typology.
Industrial plots should be located next to retail plots but away from residential and healthcare plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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//Proximit y Preference
single-use
mixed-use
Function
0
40
//Det ailed Model
1-2
Density (number of floors)
0
10 6
Floor to Floor Height (m)
0
10 4
Number of Access Points
73
//Simulation Model: Block Typology
3%
1.4 /Office Building(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s office building typology.
Office plots should be located next to hospitality plots but away from community plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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//Proximit y Preference
Mixed-use additions: - Green space (roof) - Hospitality (top floor) -Retail (00 & 01 floors)
single-use
mixed-use
Function
0
40
//Det ailed Model
17-32
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 1
Number of Access Points
75
//Simulation Model: Tower Typology
39%
1.4 /Green Space(s)
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s public green space.
Green space plots should be located next to residential, hospitality and healthcare plots but away from hotel plots.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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//Proximit y Preference
77
//Det ailed Model: Green Space
1.4 /Transport Hub
//Propor tion of L and Per Cluster
//Comput ational Model Key considerations / parameters / rules we made when designing and locating vectr’s transport hub building typology.
Transport plots should be located at the centre of each cluster.
//Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office = Green Space = Transport Hub
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//Proximit y Preference
Mixed-use additions: - Retail (end terrace)
single-use
mixed-use
Function
0
40
//Det ailed Model
1-2
Density (number of floors)
0
10 4
Floor to Floor Height (m)
0
10 2
Number of Access Points
79
//Simulation Model: L inear Typology
The Northern Gateway in the Evening from Vectr
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SIMULATION STRATEGY In Studio 2 we began building the simulation aspect of vectr and in this section we show how we developed this further in Studio 3.
1.5 /Simulation Model //Two Different Methods The simulation model is split into two parts to analyse the performance of the urban design. vectr: Dynamic: The dynamic version uses a simulation engine (python) to move every agent (person) along their route to their destination and back. This method models the people as particles (points) that can record their state in real-time. This method is used for simulation periods of up to 10 days. vectr: Static
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The static version can be used for a 100 day simulation, as it is based on fixed outcomes using static geometry; each person has a route, and each route is plotted in time and space. By using the z axis as time, we can generate each curve in 3D and solve the entire system for intersections. The intersections are interaction moments between people.
[place] person
Force
dynamic y [home]
x
Short term analysis (10 days): Advantage: Very accurate, physics based Disadvantage: Can be slow for large models
person’s projected 3D “time-curve”
[place]
person’s route
z (time) y [home]
x
Long term analysis (100 days): Advantage: Faster to run Disadvantage: Less accurate
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static
y
1.5
x
/Simulation Movement //How does it work? The dynamic simulation model moves each agent (person) along their respective routes at the same constant walking speed, which is assumed as: vwalk = 5000m / hr The Simulation “engine” gives each agent a mover force, which constantly repeats itself, this in turn creates a sequence of vectors, that take the agent to their destination and back. The static model uses the same walking speed as a constant to multiply the agent’s displacement by. The constant = 0.72, therefore if a person travels 1000m, their respective “time-curve” would be the same location, but elevated 720m in the z axis.
z (time) y
x
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[home] to [place]
dynamic
static
returning [home]
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staying at [place] for fixed duration
1.5 /Simulation Movement //Agent Routines The model analyses people’s movement based on 10 fixed “routines”, where the agent travels to a fixed destination once a day, every day cycle. The agents are produced at each [home] point depending on density (tower typology = 40 agents), these are then randomly assigned a “class” which dictates their routine. The user is able to choose the proportion split of the classes with a range of sliders. Office
At [place]
Green Space
Travelling
Education Retail Community Space Healthcare
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Hospitality
Crossover Period
18:00
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A: Office Worker B: Student C: Nurse D: Bartender E:Child-minder F: Retired Pensioner G: Community Helper H: Shopper I: Personal Trainer J: Restaurant Manager
24:00 0
6:00
12:00
(Every Day Cycle)
Class Proportions
Generate Agents
Assign Classes [A]
1.5
[B] [C]
/Computational Method //Schematic Diagram A simplified representation of the tool’s script.
Connect points to [path] network Reference [home] points Reference [place] points Reference [path] network
Custom Functions User Inputs
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Outputs
Find Closest [place
Duration Infection Period Speed Infectivity Rate, R0 Fatality Rate, Fr
Simulation Engine Vector map
vectr Dynamic
Visualiser
e] Identify Newly Infected Curves
Construct Time-curves
Solve for Intersections
vectr Static
Data Output Timeline Graph
Plot Shortest Walk Visual Output Layers Interactive Output
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(For each day cycle)
1.5 /Simulation //Determining Infections Each person (agent, point) has their own unique “time-curve”, from their home location to their destination, dictated by their assigned “routine” such as Office Worker = Office. Therefore to determine the interactions between infected curves and susceptible ones, we find the intersection point between the two. Then a randomly selected percentage of these “interacted curves” then become infected. This repeats for each day cycle, using the accumulative infected curves as the input for each day. Newly Infected Curves = Total interacting Curves x R0 (Infectivity Rate) 3D Intersection Point
(Per Day Cycle) Example: Linfected = 100 Curves x 0.04 (4%) Linfected = 4 new curves
Projected Ground Level Location Infected Curve
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Susceptible Curve
Day 3
Day 2
Day 1
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E x ample Model
1.5 /Simulation Model //Current St ate As of the end of Studio 2, the state of the tool is as pictured, with a distinct visual aesthetic. The tool is capable of running both the Static and Dynamic Scripts to simulate an infectious disease through an urban model. In Studio 3, we will develop the tool further, to produce a series of “visual outputs” such as Infection hotspots, walkability, infection heatmaps.
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The tool will also output data that can be used to construct other visual outputs, including a timeline graph.
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1.5 /Home Points //Residential Building Typologies As residential building typologies increase in density (how many people occupy them), the number of access points (AP) they have (where people enter the buildings) decreases. Therefore, there are more interactions between residents in high density building typologies because there are more people moving through fewer access points. This ultimately results in the increased chance of catching/spreading infectious diseases.
Vertical route to AP
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3
Access Point (AP)
2
Terrace housing
LOW
Horizontal route to AP
1
5 people flowing through AP
10 people per floor x N = No. of people flowing t
Linear block
k
10 people per floor x No. of floors = No. of people flowing through AP
Perimeter block
Building Density
20 people per floor x No. of floors = No. of people flowing through AP
40 people per floor x No. of floors = No. of people flowing through AP
Low-density tower
High-density tower
HIGH
No. of people per AP
97
No. of floors through AP
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Chapter 2
vectr
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vectr OVERVIEW Stage 1 = Generation > Stage 2 = Optimisation > Stage 3 = Simulation
Architects and Urban Designers can use the tool in the early stages of a project to act as a starting point to base their key spatial principles on. Results from their simulation tests can also influence the way they approach future projects.
2.1 /vectr //Introduction
Architects & Urban Designers
Our computational tool (vectr) is primarily intended to be used by architects and urban designers to help them create pandemic resilient cities.
Students can use the tool to generate and test out different masterplan spatial configurations which can influence the way they approach their own design projects.
Local Authorities can use the results from our tool to improve future planning legislation on designing for pandemic resilience.
Local Authorities
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Students
The Users
vectr a computational design tool with the purpose of enabling it ’s users to create pandemic resilient urban masterplans.
This is achieved by:
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1) Generating urban masterplans based on key pandemic resilient urban principles. 2) Allowing users to adjust the spatial design of their masterplans to find the optimal configuration. 3) Running simulations, analysis and data comparisons between multiple masterplans on the spread of infectious diseases.
2.1 Summary of the Tool Our computational tool exists in 3 main stages;
s1
Generation St age
1) Generation 2) Optimisation 3) Simulation
Import Geometry
Set Site Boundary
Listed Buildings
Green Spaces
Significant Buildings
Buildings
Transport Routes
Rivers
Roads to connect new street network to
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First the user imports the existing site geometry and sets the site boundary before selecting site elements to retain (e.g. listed buildings). Then the user initiates form generation which is based on pandemic resilient urban principles.
s3
Optimisation St age
Simulation St age
0
Compactness (%)
100
0
Infection Rate, R0 (%)
100
0
Porosity (%)
100
0
Fatality Rate, F (%)
100
0
Rigidity (%)
100
1
Spread Radius (m)
5
0
Density (%)
100
1
Infection Period (days)
100
Mixed-Use
1
Duration (days)
100
Function
The user then has the ability to customise or “optimise” the urban design for various spatial design sliders such as density, rigidity and compactness.
Finally, the user can launch the simulation of the model, which simulates the spread of an infectious disease through the urban environment.
5
Single-Use
10
s2
2.1 /Workflow
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//How the Tool Work s
St ar t Import site geometry
Set site boundary
User specifies site elements to retain
Start spatial generation
for example:
The tool creates initial 3D form
Listed Buildings Green Spaces
Clusters are located along key transport routes
GO
... ... ...
3D plot typologies, nodes and density are assigned to plots Typologies are generated from the typology library based on certain parameters. Plot mix is then allocated
Land use is allocated around a central transport hub
Street networks and plots are created
based on a decentralised, well-connected, walkable and adaptable rule set
User optimises the model for the desired spatial form using 5 sliders
Feedback Loop: the user can adjust spatial sliders to test if a different configuration performs better.
using 5 sliders
User selects simulation overlays for example:
GO
Vectr Map
Export masterplan(s)
End
Heatmap ... ... ...
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Launch Simulation
Visualise the infection spreading and infection rate data in real-time
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User specifies the epidemic parameters
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COMPUTATIONAL BREAKDOWN In this section we demonstrate how we built vectr digitally.
2.2 /Computational Approach
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//Why we used Rhino/Gras shopper
Despite having a very limited amount of knowledge using Rhino and Grasshopper before this project started last September; we decided that the best way for us to communicate, design and build vectr would be through Rhino and Grasshopper. This is due to the following:
Quick to Iterate
1 2
Easy to Experiment
Flexibility in Design
3 4
THEN
Automate Repetitive Processes
IF THEN
Relatively Straightforward to Learn
DO
DO
5 6 Easy to Model Complex Designs
Efficient Workflows
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Model Spatial Designs Changing Over Time
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//The Benefit s of Communicating vectr in Rhino/Grasshopper
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2.2 /Grasshopper Script //Summar y On the right is the full grasshopper script we created for vectr. The following pages break down this script to explain how we brought our vision for vectr to life, step by step.
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1 = Document Set-Up 2 = Street Network Generation 3 = Creating Blocks & Plots 4 = Locating Transport Hub 5 = Allocating Land Use 6 = Creating Building Typologies 7 = Generating Access Points 8 = Simulation Stage
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2.2
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1) Document Set-Up
Listed Buildings
Rochdale Road
Railway
Village Park
Site Elements Retained
5
Existing Site
The River Irk
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Site Boundary
2.2 2) Street Network Generation Cluster boundary (input)
Incoming streets we want our street network to connect to
Incoming streets from outside the cluster boundary (input)
U
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U
Existing roads within the cluster boundary we want to retain
Generate street network
Apply width to primary and secondary roads based on TFL guidance research in ST2.
User Adjustable Slider: Rigidity
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User Adjustable Slider: Compactness
2.2
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3) Creating Blocks & Plots
Create blocks
Divide blocks into plots
Remove plots that are less than 10m2 (not a suitable size for a plot)
Usable plots
Cluster boundary (input)
New street network (input)
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User Adjustable Slider: Porosity
2.2 4) Locating Transport Hub
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(One at the Centre of Each Cluster)
Closest point on a curve (key transport route) to the cluster centre = ‘adjusted cluster centre’
Cluster boundary (input)
Transport Hub
Cluster centre
Key transport route: Rochdale Road (input) Plots (input)
Centre of plots
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The plot centre closest to the ‘adjusted cluster centre’ point
2.2 5) Allocating Land Use (Cellular Automat a Modelling)
Plots, excluding transport hub (input)
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R
Neighbours = 4 to control a plot’s relationship with its immediate neighbours
Implement rules (e.g. proximity preference & land use area goals)
Adjusted land use allocation
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Randomly allocate land use initially to decentralise it
Simple building typol
Plot types (input)
2.2
User Adjustable Slider: Density
6a) Creating Building Typologies
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(Simple Simulation & Det ailed Model)
Detailed building typologies
Simple Model
5
Detailed Model
12
logies
2.2 6b) Applying Building Mix
Single-use buildings (input)
(Single-Use and Mixed-Use) //Key = Hospitality = Residential = Healthcare = Education = Hotel = Community = Retail = Industrial = Office Mixed-use buildings (input)
= Green Space
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= Transport Hub
User Adjustable Toggle: Function True = single-use buildings generated False = mixed-use buildings generated
Single-use buildings
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Mixed-use buildings
2.2 6c) Building Breakdown (Mixed-Use Office Typology) Here is an example of how we generate building typologies in rhino/grasshopper using the mixeduse office building.
//Key
Building offset from plot boundary
Building form generat set in building typ
Plot types (input)
Single-use design
= Green Space = Hospitality = Office
User Adjustable Slider: Density
= Retail
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Mixed-use design
Simple Model
Detailed Model
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Breakdown of building use
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ted by parameters pology library
(see key)
Generate access points (n
Residential Terrace Typology (input) Residential Perimeter Typology (input)
Residential Tower Typology (input)
2.2 7) Generating Access Points
Residential Linear Typology (input)
(Home & Place Point s) Each typology produces access points (‘home’ points for residential buildings and ‘place’ points for the others. These points are where people move between during people’s routines set in the simulation stage.
Hospitality Typology (input)
Healthcare Typology (input) Education Typology (input) Hotel Typology (input) Community Typology (input) Retail Typology (input)
Industrial Typology (input)
Office Typology (input)
Green Space (input)
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Transport Typology (input)
number based on typology input)
Home points
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Place points
2.2 /Computational Process Video CLICK THE LINK BELOW TO VIEW THE VIDE0:
https://youtu.be/PgEIb9hCwSo
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INSTRUCTION MANUAL A breakdown of how a user would use vectr in our developed user interface.
2.3 /Opening Interface
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//Step 0
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2.3 /Project Set-Up //Step 1
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The first thing the user must do is import their site geometry file. Once this is done they then need to set their site boundary before selecting the site elements they wish to retain.
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2.3 /Spatial Generation //Step 2 Once the user has set up the starting state of their project they must click the ‘Generate Masterplan’ button. This will generate a masterplan based on various pandemic resilient urban principles we have defined.
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The user will then have the freedom to adjust 5 spatial sliders to create different masterplan iterations, which they can test the performance of in a pandemic.
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2.3 /Launch Simulation //Step 3 The user must set the characteristics of the infectious disease they want to test their masterplan on, as well as setting the proportion of people in different categories. Then, the user can launch their simulation and place various model overlays (e.g. ‘Vectr Map’) over the ‘Simple Building Design’ overlay to visualise the spread of the infectious disease in their masterplan option.
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Next the user must save the option.
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2.3 /Analysis & Comparison //Step 4a
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A list of the users saved masterplan options will appear in the ‘Analysis Settings’ panel. The user must select the masterplan options they wish to compare in order for summaries of them to be compiled side by side.
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2.3 /Analysis & Comparison //Step 4b Once the user has selected the masterplan options they want to compare they can then click on the ‘Comparison Graph’ button which will generate an ‘Infection Graph’ plotting the results of the two options together.
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If the user is not satisfied with the performance of any of the options they can return to the ‘Generate’ tab and set-up an new masterpaln spatial configuration.
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Chapter 3
ANALYSIS
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SET-UP
Option 3
Option 1 0
(%)
0
100
Rigidity
Rigidity
Compactness
Compactness
Porosity
Porosity
Density
Density Single
(Use)
Single
Mixed
(%
(U
Function
Function
3.1 /Overview of Options These 10 masterplan options were simulated to test how well they mitigate the spread of an infectious disease. With the results used to then inform the design of an ‘optimal’ spatial configuration for pandemic resilience.
0
4
3
0
100 Rigidity
Compactness
Compactness
Porosity
Porosity
Density
Density
Function
2
(%)
Rigidity
Single
1
Option 4
Option 2
(Use)
Single
Mixed Function
(%
(U
%)
Use)
0
(%)
100
0
Option 9 (%)
100
0
Rigidity
Rigidity
Rigidity
Compactness
Compactness
Compactness
Porosity
Porosity
Porosity
Density
Density
Density
Mixed
Single
(Use)
Mixed
Function
Single
(Use)
Mixed
Function
Option 6 100
0
Single
100
0
(%)
100
0
Rigidity
Rigidity
Compactness
Compactness
Compactness
Porosity
Porosity
Porosity
Density
Density
Density
Single Function
(Use)
Mixed
Single Function
Mixed
Option 10
Rigidity
Mixed
(Use)
100
Function
Option 8 (%)
(%)
(Use)
Mixed
Single
(%)
(Use)
100
Mixed
Function
//Masterplan Spatial Configurations to Test 3
Use)
100
Option 7
15
%)
Option 5
3.1 /Performance Criteria //Metrics used for Analysis The following 6 criteria were used to critically analyse a set of 10 ‘design options’, these are calculated within the tool itself using a series of geomtric operations in Grasshopper. The 10 design options were created directly from the generative aspect of the tool, using the 5 ‘spatial sliders’ mentioned previously in ST2.
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Also displayed here are the ‘bounds’; the lowest and highest values retrieved from analysing the 10 options, showing what is considered low and high performance for each.
Poor 4883
Good 28720
Poor 1.59x106m2
Good 3383m2
Tenable Unit s
Infection Coverage
How many ‘lettable’ residential units in the model
Area occupied by infection locations (outer-most points)
380m
178m
1.84
1.03
Walk abilit y
Average R-Value (acros s 50 days)
An average length of every agent’s route in the model
Average number of people each infected person infects
5.67m2
14.18m2
99.699%
1.57%
Tot al % Infected Population
Total provided greenspace land / agent population
The percentage of the population that got infected after 50 days
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5
Greenspace area per person
3.1 /Data Key //How to read the Analysis
Poor
Simulation settings for the infectious disease, which were kept consistent across the 10 options: R0 Infection Probability (not to be mistaken for the R-value, which changes daily):
[2% (0.02)] Duration (days):
[50 Days] Infection Period (how long an infected agent is active):
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[5 Days]
Baseline Option 1 (For reference)
Good
Good Performance:
Good Performance: Peak Immunity
Peak Infection Cases Poor Performance:
25 Days
50 Poor Performance:
New Infections (per day) Infection Transmission Location
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New Immune agents (per day)
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PRELIMINARY ANALYSIS
3.2
Tenable Units
Infection Coverage (m2)
/Preliminary Data //Testing ‘Function’ The ‘Single-use’ (Option 1) and ‘Mixed-use’ (Option 2) options were simulated with otherwise baseline parameters.
Walkability
Avg. R-Val
The results show that the mixed-use version of the model performs significantly better across the board, with superior walkability and infection mitigation.
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Greenspace (m2 per person)
Total % Infected
9022
[1]
Tenable Units:
16076
Walkability Index:
305m
Greenspace (pp):
10.8013m2
Total % Infected:
82.0168
Avg. R-Value:
1.2881
Infection Coverage:
1.3257x106m2
4957
0
25 Days
50
25 Days
50
lue
18526
Walkability Index:
178m
Greenspace (pp):
9.6674m2
Total % Infected:
11.3695
Avg. R-Value:
1.1237
Infection Coverage:
480454m
2623 398 0
1
2
16
[2]
Tenable Units:
3.2
Tenable Units
Infection Coverage (m2)
/Preliminary Data //Testing ‘Rigidit y ’ The minimum (Option 3) and maximum (Option 4) Rigidity options were simulated with the exact same starting conditions and parameters as the previous simulation.
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The results show that a high level of ‘rigidity’ (a rigid grid layout) have severly detrimental impacts on how the disease spreads throughout the urban environment.
Walkability
Greenspace (m2 per person)
Avg. R-Val
Total % Infected
[3]
Tenable Units:
21388
Walkability Index:
337m
Greenspace (pp):
8.1797m2
Total % Infected:
78.3797
Avg. R-Value:
1.1827
Infection Coverage:
1.1433 x106m2
6558 3676
0
25 Days
50
25 Days
50
17153
9103
Walkability Index:
364m
7588
Greenspace (pp):
9.3772m2
Total % Infected:
99.5468
Avg. R-Value:
1.3937
Infection Coverage:
1.5873x106m2 0
16
[4]
Tenable Units:
3
lue
3.2
Tenable Units
Infection Coverage (m2)
/Preliminary Data //Testing ‘Porosit y ’ The minimum (Option 5) and maximum (Option 6) Porosity options were simulated with the exact same starting conditions and parameters as the previous simulation.
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The results show that high porosity at an urban level can have a considerable positive effect on preventing the disease from spreading. Option 6 (Maximum Porosity) had the highest overall performance, particularly as it produced the lowest average R-value across the 50 day period.
Walkability
Greenspace (m2 per person)
Avg. R-Val
Total % Infected
[5]
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16482
Walkability Index:
380m
Greenspace (pp):
9.8490m2
Total % Infected:
99.6993
Avg. R-Value:
1.5874
Infection Coverage:
1.4058x106m2
10993
6035
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25 Days
50
25 Days
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lue
Walkability Index:
204m
Greenspace (pp):
7.8414m2
Total % Infected:
5.0627
Avg. R-Value:
1.0311
Infection Coverage:
28628.9921m2
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3.2
Tenable Units
Infection Coverage (m2)
/Preliminary Data //Testing ‘Compactnes s ’ The minimum (Option 7) and maximum (Option 8) Compactness options were simulated with the exact same starting conditions and parameters.
Walkability
Avg. R-Val
The findings show that changing the compactness, such as the spacing between buildings and plots, surprisingly leads to a negligible variation in how the disease spreads through a city.
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Greenspace (m2 per person)
Total % Infected
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17206
Walkability Index:
361m
Greenspace (pp):
8.9100m2
Total % Infected:
98.4744
Avg. R-Value:
1.4384
Infection Coverage:
1.4792x106m2
10039
5871
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25 Days
50
25 Days
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12471
Walkability Index:
278m
Greenspace (pp):
9.3337m2
Total % Infected:
99.5612
Avg. R-Value:
1.4277
Infection Coverage:
1.4217x106m2
6638
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7
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Tenable Units
Infection Coverage (m2)
/Preliminary Data //Testing ‘Densit y ’ The maximum (Option 9) and minimum (Option 10) Density options were simulated with the exact same starting conditions and parameters.
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The simulation revealed that, understandably, the dense urban model lead to the worst case scenario across the 10 options, the interesting finding here is the severity of such scenario, which was far greater than originally hypothesised.
Walkability
Greenspace (m2 per person)
Avg. R-Val
Total % Infected
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28720
Walkability Index:
317m
Greenspace (pp):
5.6724m2
Total % Infected:
98.6442
Avg. R-Value:
1.8365
Infection Coverage:
1.3753x106 m2
6439
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25 Days
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25 Days
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Walkability Index:
315m
Greenspace (pp):
24.1757m2
Total % Infected:
1.5712
Avg. R-Value:
1.0555
Infection Coverage:
3383.5067m2
151 0
9
4883
16
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Tenable Units:
3.2 /Preliminary Data //Comparison of all Options This figure shows each of the 10 design options side-by-side showing the number of new cases each day.
[1] Baseline Option [2] Mixed-use Variant [3] Rigidity Min. [4] Rigidity Max. [5] Porosity Min. [6] Porosity Max. [7] Compactness Min. [8] Compactness Max. [9] Density Max.
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9000
8000
7000
6000
5000
4000
3000
2000
1000
0 Day 50
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Day 25
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Option 1
Option
Score: 0.4117
Score:
3.2 /Data Summary //Which Options Per formed well? Here the 6 performance metrics are rationalised as an index for each option from 0 to 1, 0 being a poor performer, 1 being a good performer. With each design option having 6 index values for each metric (E.g. 0.2764, 0.7943...etc) an average can be calculated to assume the design option’s overall score.
Function
Rigidit y
Option 2
Option
Score: 0.7543
Score:
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Evidently, only 3 design options performed adequately, with the ‘Maximum Porosity’ option displaying the greatest level of mitigation to the disease.
n3
Option 5
Option 7
Option 9
: 0.4183
Score: 0.2336
Score: 0.2611
Score: 0.2427
Densit y
n4
Option 6
Option 8
Option 10
: 0.2634
Score: 0.7932
Score: 0.3753
Score: 0.7153
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3.2
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
/Evaluation //The Best ‘Per former ’ Following the analysis of all 10 design options, the “High Porosity” Option (6), was judged to have the highest level of performance for the given criteria and simulation parameters.
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Small plots can be seen, with a high level of connectivity throughout the design.
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[1]
[2]
[3]
[4]
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[6]
[7]
[8]
/Evaluation //The Poorest ‘Per former ’ In contrast, the “High Density” Option (9), was (understandably) seen to be the lowest performer, being mostly comprised of densely populated tower blocks, and a lower level of connectivity between them. The advantage would be from a developer standpoint, providing upwards of 21,000 new homes, which would generate a significant amount of revenue, although possibly at the detriment to human safety in the event of a crisis such as this.
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DETAILED ANALYSIS
3.3 /Model Layers //Urban Level Throughout the project, the model was realised in 3 distinct “layers”, each for a specific purpose. The user is able to mould their urban design as a spatial model, with the presentation model being utilised for final visualisation only.
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During the simulation stage, the spatial model is rationalised as a mathematical model that only reads the [home] points, [place] points, and the [path] network connecting them.
Simulation Model
Spatial Model
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Presentation Model
3.3 /Model Layers //Building Level The simulation model rationalises each building typology for populated [home] points depending on density, for example a terraced housing typology has a density of 5 per house, whereas a tower typology has a density of 40 per floor. The initial point input for towers consist of only the lift core, the simulation model then randomly scatters these 8 times (with random x and y displacements) as “flats”, before generating 5 agents at each “flat” point. (Shown in the simulation building model)
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These “flat” points then find their closest “lift core” point, before finding their closest “base” point, these then connect together with the input [path] network to create each agents 3D route.
Simulation Model
Spatial Model
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Presentation Model
3.3 /Detailed Comparison //Over view Here, options 6 and 9 are being compared for the aforementioned perfornance metrics, the stark differences between them can be seen clearly. The high density option displayed a substantial spike in new daily cases, almost reaching 8000 new cases in one day at it’s peak. With a rapid climb to get to that point, this would lead to horrific consequences in a real scenario, with healthcare facilities reaching capacity without steady warning, likely to becoming overrun.
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The porous option meant that the infection rate R was kept under control, with no exponential growth, any exponential periods also flucuated back down, which is key to mitigating the spread of a disease in a city such as this.
Units
Area
W
8000
R
4000
0
[1] Baseline Option [6] Porosity Max. [9] Density Max.
Day 0
Option:
Day 25
[6]
Day 50
[9]
Tenable Units: 21197 28720 Walkability Index: 204m 317m 2 Greenspace (pp): 7.8414m 5.6724m2 Total % Infected: 5.0627 98.6442 Avg. R-Value: 1.0311 1.8365 Infection Coverage: 28628.9921m2 1.3753x106 m2
5
%
18
G/p
3.3 /Detailed Comparison //Walk abilit y Analysis The following analysis shows all walking routes the agents undertake for each of the design options being compared. The lengths of these are calculated to identify an average, and lower and upper bounds. The Average Curve deviation index (reversed to be from 0 to 1, 1 being low deviation) is also calculated from using for Grasshopper. It is evident that higher porosity is crucial to an effective urban plan. These need not be roads, here paths through plots are created, as well as footpaths through greenspace areas, which gives priority to the pedestrian.
Average Lowest R Highest R Walkabil
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Devation
Option
[6]
Option
[9]
204m 11.2566m 805.7667m 0.8712
Average Walking Distance: Lowest Route Distance: Highest Route Distance: Walkability Index:
317m 9.6608m 1000.4728m 0.3119
n Index:
0.6732
Devation Index:
0.4766
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Walking Distance: Route Distance: Route Distance: lity Index:
3.3 /Detailed Comparison //Infection Location Analysis The infection analysis here shows all locations where the disease was transmitted from one agent to another.
190 884
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R-value: 1.0318
Option 15937
[9]
6439
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25 Days
50
R-value: 1.8366
0
25 Days
50
9
06 4
[6]
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Option
3.3 /Detailed Comparison //Infection Densit y Analysis The following graphic shows the infection “hotspots” where most condensed regions of infection spread were.
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Infection Infection Avg. area
n Coverage: n Coverage Index: a per infection:
Option 28628m2 0.9841 283.4554m2
[9]
Infection Coverage: Infection Coverage Index: Avg. area per infection:
1.3753x106 m2 0.1339 76.3705m2
1
[6]
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Shudehill Tramline
3.3 /Masterplan Perspective //Infection Locations after 10 Days Here the urban design for the Northern Gateway is realised as the important artery junction betweent the Railways, the Tramlines and Rochdale Road. A hotspot of infections emerge at the centre point of the site, almost exactly where the original Farrell’s “transport” hub is proposed to be located.
Trainline into Victoria Station
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Rochdale Road
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Projected time-curve intersections that result in infection transmission
3.3 /Promo Video A short video demonstration of how to use vectr. CLICK THE LINK BELOW TO VIEW THE VIDE0:
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CONCLUSIONS
4.1 /What is Next? //Taking vectr to the Nex t Level
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We believe that vectr is a much needed computational tool in the architecture industry as designing for pandemic resilience should be a key consideration in all urban designs in the future. The current Covid-19 pandemic highlights this need. Therefore, if we were to keep working on this project we would look to develop it into:
https://www.vectr.com
Welcome to vectr A computational design tool that optimises the spatial design of cities for mitigating the spread of infectious diseases.
A Web-B ased Tool
Current proposals for the following masterplan developments in Manchester should be tested in vectr to see how they perform in a pandemic and subsequently highlight areas for improvement:
https://www.food4rhino.com
Vectr
Elk
Pufferfish
Decoding Spaces
Kangaroo
Lunchbox
A Rhino/Grasshopper Plug-in
vectr
The Northern Gateway Development
The Mayfield Development
The Ardwick Green Development
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A Branded Product
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Consult on All MCC Masterplans
4.1 /Conclusion //Group Conclusion Some of the main hurdles we faced when building our tool were a result of computational constraints; with a system that grows exponentially (such as the spread of an infectious disease through a city), each day cycle requires more computing power, and this grows exponentially each day. By the 20th day, computing times were as long as 2 hours, meaning other methods and work-arounds had to be used. This could be related to grasshopper’s lack of suitability to live, moving geometry. Therefore, if we had the opportunity to take this further, a gameengine would be used to fully realise this, such as Unity, where scripting a moving system is easily achievable.
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If we had more time to work on this project, creating a section of the tool that enables users to test existing masterplans would be hugely beneficial.
//Henr y ’s Concluding Thought s
//Elise’s Concluding Thought s
Our Design Thesis this year spent designing an generative/simulation+analysis computational design tool has been very rewarding, both in terms of learning new skills and investigating an extremely prevalent subject through a spatial lens.
At the begining of the year when we were told our CPU atelier brief was Resilient Future Cities we thought this would be a great opportunity to explore pandemic resilient cities. Up until the current COVID-19 pandemic none of my architectural education had ever considered this aspect of urban design and so I was really interesd to see where this project could take us.
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Of what was an incredibly challenging project, I believe we achieved our aims, while also building a design tool quite unique to anything currently available.
This project has taught me so much about designing at an urban scale (something I had only done briefly in my Part 1 placement year) as well as vastly improving my computational skills and thinking. We really pushed ourselves in building vectr; we had an idea of what it could be and what we would like it to be however turning that into an actual computational tool was incredibly challenging and rewarding.
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Vectr seeked to provide a spatial design tool at an urban scale, with the added functionality of analysing the design for viral transmission. Grasshopper proved an invaluable tool in this quest, and enabled us to fully implement our own design philosophy onto the project, one that values function over form, utilising technology to achieve a predominantly automated, efficient workflow.