ZJD 6th Year Masters of Architecture Studio 3 Portfolio by CPU-Ai-ZJD

the pursuit of happiness hULME EDITION STUDIO 3

ZOHRA ABBAS JESSICA CORNS DEVEN KARA

THESIS STATEMENT Cities are becoming places of social disconnection leading to increased unhappiness. This project uses gamification to determine what people value in their urban environment, in order to improve design for ‘happiness’ in urban areas. The resulting data is compared to the research on happiness presented in Studio 2 and is used to test the validity of our platform as an approach to participatory planning.

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INTRODUCTION Studio 3 will explore the development and implementation of a game in which players lay out their ideal neighbourhood. Through the responses generated from the game, the results will be used to test the platform as a proof of concept for participatory planning as well as providing a point of comparison against the research presented in Studio 2. This will question whether the research is a correct depiction of what generates happiness in urban environments, or if this platform could provide a way to better understand what makes people happy in their urban environment.

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ST2 RECAP

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POSITION STARTING ST3 STAGES THREE - FIVE OUTLINE ST3 FOCUS

ST2 FOCUS

X We have completed up to Stage Two of our plan. Commencing Studio 3 we will begin building the game and proceed to run iterations to analyse and compare against the pillars of happiness.

STAGE ONE

STAGE TWO

RESEARCHING THE GAME

DESIGNING THE GAME

COMPLETED

STAGE THREE

STAGE FOUR

STAGE FIVE

BUILDING THE GAME

PLAYING THE GAME

EVALUATING THE GAME

see chapter: 03

see chapter: 04, 05, 06

see chapter: 07, 08

1. Coding each step of the game. 2. Constructing a user interface and graphic style. 3. Test running the game, making adjustments where necessary.

1. Getting participants to play the game. 2. Collecting data, sorting into demographics and first, second attempts etc.

1. Analysing the data based on demographics, sorting patterns and forming conclusions. 2. Comparing the results to the principles of the pillars of happiness.

COMPLETED

ST3

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RECAP OF RESEARCH UNDERTAKEN IN ST2 AND THE POINT OF EVALUATION AGAINST THE RESULTS FROM THE GAME

BETTER MINDSET = KINDER DECISIONS

CHARLES MONTGOMERY

RELATIVE HAPPINESS

ENCOURAGED INTERACTIONS WITH STRANGERS

‘LOVE NIGHT’

SOCIAL CONNECTION

HAPPY CITIES

FACADE INTERACTION

INTERACTIVE INSTALLATIONS

EMOTIONAL INFRASTRUCTURE

SATISFACTION TOGETHERNESS

PROFESSION

STIMULATION

LYKKE

RELATIVE POVERTY

MONEY

URBAN HAPPINESS

PILLARS OF HAPPINESS

KINDNESS

TRUST

FREEDOM

HAPPINESS ECONOMICS

KEY

JAN GEHL

GREEN SPACE

HEALTH

TYPE OF WORK

SAFETY

KEY ELEMENTS OF FOCUS

ENVIRONMENT

URBAN VALUE

WALKABLE CITIES

HYGGE

COMMUNITY

ECONOMY

COMMUNITY

SECONDARY RESEARCH

INCOME

PUBLIC SPACE

HEALTH

PRIMARY RESEARCH

RYAN BRENNAN

WORK LIFE BALANCE COMMUTE DISTANCE

MENTAL HEALTH

THIRD PLACES

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WHAT IS HAPPINESS?

S ES

ESS MO

TOGET HE

KIN

OM AL

TR U

The term happiness is subjective, but happiness economics attempts to make happiness more objective. Happiness economics and the pillars of happiness break ‘happiness’ down into factors that contribute to a person’s wellbeing, and when a person’s life has those factors that person tends to have greater life satisfaction in the context of their urban environment. N

hap·pi·ness | Definition of happiness 1a: a state of well-being and contentment 1b: a pleasurable or satisfying experience (Definition of HAPPINESS, 2020)

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WHAT ARE WE DOING? We are making a tool; a game, the aim of which is to visually curate a neighbourhood. Our primary focus is using the game as a bottom-up approach to record what aspects of a neighbourhood players value, in order to inform designers. The purpose of the game, however, is not to make the players the designers. The game will output iterations of the neighbourhood, from which we will extract measures of what players value in their neighbourhood. This data will be compared to our findings in ST2, to determine whether the values listed in The Pillars of Happiness is in keeping with what the players value. We are proposing a tool for urban planning by providing a platform where inhabitants can freely express their needs in a 3D context without the conflicting environment of a public setting.

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HOW ARE WE DOING IT? 15

WHY ARE WE DOING IT?

PLANNERS

C LIE N T S

A R C HIT E C T S

USERS

PARTICIPATORY PLANNING

USING THE POINT SYSTEM IN THE GAME

TO UNDERSTAND USER NEEDS THROUGH PARTICIPATORY PLANNING

The rules of the game were generated from the research on happiness in cities. These rules, that can be used to increase the users wellbeing, are categorised and given a score. Throughout the game each action earns points that are used to generate a final score on completion; the scoring will allow us to analyse what the players valued in their neighbourhood.

Designs can be developed around the actual needs of the inhabitants, rather than an assumption of what they may need. In turn, it provides a sense of ownership to the inhabitants, as they helped to design their environment.

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LEVELS OF PARTICIPATORY PLANNING

C O NF

APPROACH

N CE RE

CASE STUDY

PARTICIPATORY PLANNING

In participatory planning, there are a number of levels which require a certain amount of public involvement; this usually depends on the project at hand.

TOP DOWN S UR

CHANGE BY DESIGN: MASHIMONI (Kenya, 2011) Architecture Sans Frontières UK

The sharing of information of a decision that has already been made before it is implemented.

THE LEVELS OF PARTICIPATORY PLANNING AND THE APPROACH.

MINIMAL PUBLIC INVOLVEMENT

1 INFORM

USERS: Mashimoni’s residents & organisations

2 CONSULTATION Voicing the initial plan before final decisions are made. All participants are invited to give feedback.

PROJECT TOP DOWN WORK O SH

A research workshop to upgrade a neighbourhood in Mashimoni, a slum in Nairobi, collaborating with Pamoja Trust, a local NGO and UNHABITAT. The workshop included visits and symposiums with over 120 participants including the collaborating organisations.

DISCUSSIONS INSTITUTIONS

TOP DOWN MAP P

NEIGHBOURHOOD

DWELLING

As differences were found between individuals and the majority, participatory design for this process enabled the issues to be analysed at three different scales; institutional, neighbourhood and dwelling.

OUTCOME

G IN

Each group came together to create a portfolio of potential and recommended changes on the planning regulations.

NEGOTIATIONS

Engaging with one another to decide together the best course of action. LEVEL WORL

D CA

MAJORITY PUBLIC INVOLVEMENT

Joint conversation allowing the public to offer ideas and suggestions, decisions are influenced by discussion.

UNSUCCESSFUL: Lack of detailed analysis: Due to the limited time-frame and the large scale of the neighbourhood prevented a relationship with the wider city processes.

Life stories: Creating a timeline of the residents milestones.

5 INDEPENDENT Local communities or an individual self-organise their plans and decisions are made independently.

SUCCESSFUL: Drawing during interviews: residents were encouraged to discuss their drawings while drawing during interviews.

BOTTOM UP

Models: Used for urban scale models to identify problems but also for the residents to explore their desired dwelling.

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THE GAMING TOOL ON THE SCALE OF PARTICIPATORY PLANNING

Where does the game stand in the levels of participatory design?

INDEPENDENT

HOWEVER...

HAPPINESS RESEARCH The research presented is an example of a top-down method, as it informs us of what is needed in urban design to increase happiness. The point system and rules have been generated from this research and will be a point of comparision.

IT IS AN ALTERNATIVE APPROACH TO GETTING USER FEEDBACK

OUTCOME Resulting scores from the game will highlight the scale of happiness they have achieved. The results are analysed against research - is the users happiness in line with the research?

in addition...

Gathering responses helps to address peoples wellbeing and what they value - not to be used as a method for actual urban designing.

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WHY IS THE GAMING TOOL A SUITABLE METHOD FOR PLANNING

PLANNING OFFICER

BOTTOM UP APPROACH = This platform allows the users to the chance to express their opinions and be understood first hand.

Hello! I am here to gather data as I am unaware of what the residents really need in their neighbourhood - your input will help us to design!

IN REFLECTION ON CURRENT METHODS A way to measure what people value!

No conversation needs to take place, the user can play the game with others or individually and in their own time.

Open consultations can be restricting - controlled topic conversations similarly to surveys. With no dictation from the game organisers and others in public talks, it prevents the users being influenced by outside opinions.

Interacting with a game can provide more honest and open responses than having a framed answers from questions the questions posed in surveys.

User are free to express their thoughts and ideas visually through the game.

Tool is adaptable for other scenarios in the future.

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HOW THE RESEARCH IS USED FOR THE RULES AND POINT SYSTEM IN THE GAME

POINTS

EVALUATE

RULES PILLARS OF HAPPINESS

+ Research from the Happiness Research Institute has been extracted in ST2 and spatialised in urban elements in the city/ neighbourhood.

COMMUNITY

PUBLIC SPACE

WALKABILITY

The rules were generated from dividing the pillars into 3 spatial categories. Each pillar is addressed within the category as a spatial element that can be addressed through urban design.

GAME

RESEARCH

VS.

RESULTS Each rule has a point system, the closer to the exact figure, the higher the point. The points were based on the urban priorities found in the research i.e. community ranked higher therefore highest points the user can get is 15 but in walkability, the highest point you can get is 5.

At the start of the game the player is presented with a choice of future scenarios in which to play, these are based on research on Hulme and Manchester. The user then builds their neighbourhood under this scenario, which will go on to be analysed based on the score recieved.

After all the responses have been received, the score will start the evaluation process. At this point, the responses will be ranked, this will give us an indication of what the players favour.

Once the results have been analysed, the key points drawn will be evaluated against the initial research uncovered. How valid is the research? Are the Pillars of Happiness a correct guide for happiness in cities? What makes the people in cities happy? ...

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CONTENTS

EXPLAINING THE GAME

RESULTS OF THE GAME

GAME MECHANICS HOW IT WORKS

COMPARING RESULTS AND RESEARCH

USER INTERFACE AND PLAYERS

EVALUATING THE GAME

RUNNING THE GAME

BIBLIOGRAPHY

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NO.: 015 SCENARIO: 2 OCCUPATION: RETIREE AGE: 65+ LIVING STATUS: SINGLE

EXPLAINING THE GAME

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REFLECTING ON CHANGES OUTLINING THE CHANGES TO THE GAME STEPS BASED ON MECHANICS EXPERIMENTATION AND USER FRIENDLINESS

CLICK GO The player starts the game by pressing go. The game has been coded in a way that to progress to the next step, the player must press next.

PICK SCENARIO The player has the choice to choose which scenario to design for. Their placement choices within each scenario may award extra points.

CREATE GRID The player picks the cell size and rotation of the grid, and positions it where they would like it.

CREATE PUBLIC SPACES

CREATE MACRO NETWORK

The player uses curve tools to draw their public spaces. The game recognises these curves and occupies the relevant cells.

The player uses curve tools to draw the path of the road network. The game recognises the placement and inserts a road in its place.

CREATE ZONES The player designates which areas are used for each category of typologies. This will then restrict which typologies they can place where.

CREATE MICRO PUBLIC SPACES

PLACE TYPOLOGIES

CREATE MICRO NETWORK

FILL IN THE BLANKS

This step is the same as the larger public spaces, but used to create smaller public spaces.

The player can place a typology from a selection that are present. These fall into the categories of: residential, education, amenities, commercial and office.

This step is the same as creating the macro network, but on a smaller scale, placing side roads.

The last step, the game evaluates the grid and gives the player the choice to fill the blanks.

CURRENT STEPS OF GAME

ORIENT GRID The player now has the option to rotate a pre-existing and presized grid. The rest of the game and artefact placements are now orientated to this grid.

CREATE PUBLIC SPACES

CREATE MACRO NETWORK

PLACE TYPOLOGIES

CREATE MICRO NETWORK

CREATE MICRO PUBLIC SPACES

FILL IN THE BLANKS

The same as the public space step, but with spaces that are only one cell large.

The player now has the choice to place from a selection of pre-determined public spaces in two typologies.

NEW REVISED STEPS OF GAME CREATE ROAD NETWORK This step combines the creation of the macro and micro networks.

PLACE TYPOLOGIES

CREATE PUBLIC SPACES

FILL IN THE BLANKS

This step combines the creation of the macro and micro public spaces.

ALTERNATIVE STEPS FOR GAME

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CHANGES TO THE PROCESS OF PLAYING THE GAME

STEP 01

STEP 02

STEP 03

STEP 04

STEP 05

STEP 06

STEP 07

STEP 08

CREATE GRID

CREATE MACRO PUBLIC SPACE

CREATE MACRO NETWORK

CREATE ZONES

CREATE MICRO PUBLIC SPACE

PLACE TYPOLOGIES

CREATE MICRO NETWORK

FILL IN THE BLANKS

STEP 01

STEP 02

STEP 03

STEP 04

STEP 05

STEP 06

STEP 07

STEP 08

CHOOSE SCENARIO

ORIENTATE GRID

CREATE MACRO PUBLIC SPACE

CREATE MACRO NETWORK

PLACE TYPOLOGIES

CREATE MICRO NETWORK

CREATE MICRO PUBLIC

FILL IN THE BLANKS

Neighbourhoods with public spaces provide a place to visit, to interact with others and creates a community but they are also key to improving the quality of life particularly for residents to live in the city centre.

The creation of the macro network enables the neighbourhood to remained connected to the surrounding context. Without this network, the neighbourhood would be at risk of isolating itself.

Placing the typologies is a crucial step to creating a neighbourhood; they provide places to live, shop, work and is important for creating an identity and sense of place.

The typologies need to be connected to the wider network, whether pedestrian network or vehicle to maintain access for residents/workers etc.

Within the smaller communities, creating micro public spaces gives provides opportunities to interact with other locals. Particularly within a larger neighbourhood, a micro public space can be more convenient and personal.

To prevent the leftover spaces in the grid from becoming unused or deralict, this step ensures all spaces are utilised and a part of the development.

PREVIOUS STEPS...

UPDATED STEPS...

1 4

45°

The player has a choice of 5 scenarios that are a possible future outcome in Manchester in the current economy and well suited in the wider context.

The orientation of the grid at the neighbourhood scale is to ensure the site sits well within the context as its connection to the rest of the city is important.

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THE GAME EXECUTION METHOD AND CONTINGENCY PLAN DUE TO UNFORESEEN CIRCUMSTANCES

PREVIOUSLY...

PARTICIPATORY DESIGN WITH HULME RESIDENTS Visit Hulme and converse with residents and get their involvement with the game to understand as inhabitants of the area what they feel is best for their neighbourhood.

As the residents of Hulme will not be able to actively take part in the process, the results received

will not be a true representation of the residents. Nevertheless, the method will still be

MSA

PLAY AMONGST ONE OTHER

CIRCULATE THE GAME WITHIN MSA

DISTRIBUTE THE GAME ONLINE

the same - understanding what the users value but will be used as a proof of concept.

Option 1 looks at playing the game amongst the group as many times as possible under different character agents, such as property developer, retiree, climate activist etc. to prevent personal biases being implemented.

Option 2 explores circulating the game between the students of Manchester School of Architecture (MSA). A more diverse opinion involving more students under different ateliers can contribute.

Option 3 looks to distribute the game on gaming communities online such as discord to access a wider network.

We chose these options as the distribution method

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AGENT PROFILES: SELECTED AGENTS AND THEIR ATTRIBUTES TO PLAY THE GAME UNDER IN OPTION 1

STUDENT CLIMATE ACTIVIST AGE PROFILE: 16-30 FAVOURED TYPOLOGIES: ITY RES I AM E

S EN

HIGH

SINGLE PARENT

TIES NI

FAVOURED TYPOLOGIES:

ITY RESI MID D E

MICRO

HIGH

LIC SPA

ITY RESI

TIES NI

AGE PROFILE: 65+

TIES NI

C OM M

AGE PROFILE: 25+

AM E

FAVOURED TYPOLOGIES:

CIAL

ITY RES I

S EN

CIAL

ITY RESI

LOW

S EN

C OM M

FAVOURED TYPOLOGIES:

AM E

DEVELOPER

HIGH

S EN

LOW

FAVOURED TYPOLOGIES:

BLIC SPAC PU

B PU

AGE PROFILE: 25+

ITY RES I

RETIREE

S EN

M A CR O

UN IV

AGE PROFILE: ALL SITY

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NO.: 013 SCENARIO: 3 OCCUPATION: CLIMATE ACTIVIST

AGE: 35-44 LIVING STATUS: WITH PARTNER

GAME MECHANICS

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USER INTERFACE AND RESTRICTIONS THE OPTION SELECTIONS AND BUTTONS THAT WILL NEED TO BE PRESSED BY THE PLAYER AS THEY PLAY THE GAME.

STEP 1: CHOOSE SCENARIO Button to progress to next step

Scenario 1: Demographic Change to Education

Next Step

Scenario 1: Demographic Change to Education Scenario 2: Demographic Change to Residential

Select Scenario

Scenario 3: High-Rise Residential Scenario 4: Business District

THE GAME

Scenario 5: Pedestrianisation

(AND ALL ITS INNER WORKINGS)

STEP 2: ORIENT GRID Angle

Place Grid

STEP 3: PLACE MACRO PUBLIC SPACE 2x2 Cells

Place Space

2x2 Cells

Grass

3x3 Cells 4x4 Cells

Paving

5x5 Cells

Grass

Move Left/Right

STEP 4: CREATE MACRO NETWORK Place Network

STEP 6: CREATE MICRO NETWORK Move Up/Down

Place Network STEP 5: PLACE TYPOLOGIES

School

Place Typology

Low-Rise Residential Detahced

STEP 7: PLACE MICRO PUBLIC SPACE

Low-Rise Residential Terraced

Paving

Mid-Rise Residential Closed Mid-Rise Residential High Density

Place Space

Paving

High-Rise Residential Cluster

Grass

Small Amenities School

Move Left/Right

Small Commercial

Low-Rise Office Cluster

Move Up/Down

High-Rise Office Point

Rotation

STEP 8: FILL THE GAPS

Move Left/Right Move Up/Down

Paving

Fill Gaps

Paving 4

Grass

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USER INTERFACE AND RESTRICTIONS THE STEPS THAT THE USER WILL TAKE ALONGSIDE THE CODING LOGIC THAT MECHANISES THE GAME AND THE RESTRICTIONS IN PLACE FOR GAMEPLAY.

ROADS GO! GRID

STEP 1: CLICK GO USER

CLICKS A START GAME BUTTON

The game has been designed so that decisions that are made are permanent. It has been coded to restrict backwards movements. This forces the users to commit to their decisions and create new iterations for different ideas they have.

STEP 5: DRAW MAIN ROADS STEP 5A: MOVE TO NEXT STEP

SCENARIO STEP 2: CHOOSE SCENARIO STEP 2A: MOVE TO NEXT STAGE USER PICKS THE SCENARIO THEY WISH TO DESIGN FOR The scenarios have been chosen to represent possible outcomes for urban sites, which can also apply to the Hulme site. The placement of certain typologies within certain scenarios can award players with extra points.

STEP 3: ORIENT GRID STEP 3A: MOVE TO NEXT STEP USER CHOOSES THE ANGLE OF ROTATION OF THE BASE GRID, AND ORIENTS IT AS THEY LIKE The grid placement has changed from the player being able to change the cell size and placement, to only being able to rotate a pre-existing grid of a set size. This was in part to simplify it for the user, whilst also making it easier on the coding and computational power.

PUBLIC SPACE STEP 4: CHOOSE PUBLIC SPACE TYPE STEP 4A: PLACE AND REPEAT STEP 4B: MOVE TO NEXT STEP USER CHOOSES THE PUBLIC SPACE TYPOLOGY THEY WANT TO PLACE. THEY POSITION IT, PLACE IT AND REPEAT UNTIL THEY WANT TO MOVE ON

USER DRAWS THE PATHS FOR THE MAIN ROADS UNTIL THEY ARE READY TO CONTINUE. WHEN HAPPY PRESS CONFIRM AND ROAD SURFACES ARE PLACED The user has the freedom to draw the roads wherever they wish, as long as it doesn’t intersect with the previously placed public spaces. We didn’t want to restrict placement of roads, or have it so that the roads needed to be placed in linear paths. This way allows for a more free, indicative placement of the roads.

A difference from the original step, which allowed users to draw their spaces, the user can now choose between two types of public space, in 4 different sizes. They can place as many of these as they wish, but these sizes are chosen to provide variation in the iterations, so that users can place smaller or larger public spaces. This change was driven by user friendliness and simplicity.

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USER INTERFACE AND GRASSHOPPER THE STEPS THAT THE USER WILL TAKE ALONGSIDE THE CODING LOGIC THAT MECHANISES THE GAME THEY PLAY.

BUILDINGS STEP 6: CHOOSE BUILDING TYPOLOGY STEP 6A: PLACE AND REPEAT STEP 6B: MOVE TO NEXT STEP USER CLICKS TYPOLOGY BUTTON, USER CHOOSES THE TYPOLOGY THEY WISH TO PLACE FROM A LIST, AND CONTINUES PLACING DIFFERENT TYPOLOGIES UNTIL READY TO MOVE ON A selection of typologies have been provided to cover all bases for typical urban typologies. These are able to be placed wherever the user wishes with no restrictions. The typologies are simple in design, and can be placed as many times as the player wishes. But once a typology is placed, it cannot be undone, meaning choices by the player are permanent.

FILL

ROADS STEP 7: DRAW SECONDARY ROADS STEP 7A: MOVE TO NEXT STAGE USER DRAWS THE PATHS FOR THE SECONDARY ROADS UNTIL THEY ARE READY TO CONTINUE. EACH ROAD CAN BE DRAWN AND PLACED SEPARATELY The same as the main roads, the user has the freedom to place this secondary network wherever they wish. In the case of the pedestrianisation scenario, this micro network will be pedestrian only.

PUBLIC SPACE STEP

8: CHOOSE MICRO PUBLIC SPACE TYPE STEP 8A: PLACE AND REPEAT STEP 8B: MOVE TO NEXT STEP USER CAN NOW PLACE SMALLER GREEN OR PAVED SPACES TO FINISH OFF ANY EMPTY CELLS THAT MAY REMAIN

STEP 9: FILL IN THE GAPS ANY REMAINING CELLS THAT ARE UNINHABITED ARE FILLED IN WITH EITHER GRASS OR PAVING AT THE USERS CHOICE This is the last step where the user can choose to fill all the empty gaps with paving or grass. This means they won’t have to place something in every cell and can choose to finish the iteration as they wish.

These micro public spaces are limited to a single cell to force the user to use this step to finish and curate any spaces that might require it, rather than using it to occupy large swathes of public spaces.

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TYPOLOGY CATALOGUE [CLICK ON TYPOLOGY IMAGES]

TYPOLOGY ATTRIBUTES HIGH RISE APARTMENT SLAB BLOCK

RESIDENTIAL

BUSINESS

COMMERCIAL

EDUCATION

ACCESS:

PUBLIC

PRIVATE

DENSITY:

HIGH

LOW

ZONE:

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“If you’re in a good place, it produces oxytocin” - Ricardo Marini

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NO.: 016 SCENARIO: 4 OCCUPATION: SINGLE PARENT AGE: 25-34 LIVING STATUS: WITH FAMILY

USER INTERFACE AND PLAYERS

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It’s time to show you the game. Scroll through the following pages to experience the game interface!

GET READY... START

ZJD PRESENTS...

MANC SKYLINES

PICK A SCENARIO... Demographic: Education Manchester Metropolitan University are increasing their intake of students. The expansion calls for more teaching spaces and accommodation for the students.

Demographic: Residential Unfortunately, MMU has gone bust. The site has become attractive to people wanting to relocate closer to the city centre, causing a change in demographic from a high student population to a diverse range of residents.

Construction increase: High-rise residential Following the increase in demand for accommodation and the current growing development of high-rise accommodation in Manchester - could Hulme be the next location for the city’s core expansion?

Business District With the site being in close proximity to other business districts and with the large scale of development currently being undertaken, the proposed site is a suitable location to address the growing demand and provides an opportunity to increase jobs in the city – what a great boost to Manchester’s economy!

Pedestrianisation Autonomous vehicles are the prevalent mode of transport, this has induced an increase in car sharing and pedestrianised zones.

HIGH-RISE RESIDENTIAL

3 Here you will find help and support when progressing through the game! look out for a pop up! PROGRESS STARS will be tracked here!

Ms HIGH-RISE RESIDENTIAL

Orientate the grid to your preference!

ORIENTATE GRID 20°

CONFIRM

HIGH-RISE RESIDENTIAL

Choose from the two types of public spaces and multiple sizes to create your public space!

PLACE MACRO PUBLIC SPACE

5x5

Grass CONFIRM

HIGH-RISE RESIDENTIAL

This is the main access route through the site, choose for the selection on the left!

DRAW YOUR MAIN NETWORK

CONFIRM

PLACE YOUR TYPOLOGIES Residential Mid-Rise U-Shape

Mid-Rise Closed

Mid-Rise High Density

High-Rise Point

High-Rise Slab

High-Rise Cluster

Amenities Businesses Commercial Education CONFIRM

HIGH-RISE RESIDENTIAL

You’re ready to place your typologies! Choose these using the library catalogue on the left!

PLACE YOUR TYPOLOGIES Residential Low-Rise Detached

Low-Rise Semi-Detached

Low-Rise Terrace

Mid-Rise L-Shape

Mid-Rise U-Shape

Mid-Rise Closed

Amenities Businesses Commercial Education CONFIRM

HIGH-RISE RESIDENTIAL

You have placed over 50 typologies! YAY! Your neighbourhood is growing!

PLACE YOUR TYPOLOGIES

HIGH-RISE RESIDENTIAL

Residential Amenities Businesses

Low-Rise Office Cluster $

Mid-Rise Office L-Shape

Mid-Rise Office U-Shape

Mid-Rise Office Closed

High-Rise Office Point

Commercial Education CONFIRM

HIGH-RISE RESIDENTIAL

These are your side roads or pedestrian routes. Choose from the selection bar on the left!

DRAW YOUR SECONDARY NETWORK

CONFIRM

HIGH-RISE RESIDENTIAL

These spaces are smaller public spaces, choose from either paving or grass!

PLACE YOUR MICRO PUBLIC SPACE

1x1

Grass CONFIRM

HIGH-RISE RESIDENTIAL

Fill in the remaining spaces with paving or vegetation.

FILL IN THE BLANK SPACES

Paving CONFIRM

Your final neighbourhood! 30

COMMUNITY

Successful no. of public spaces but lacked in micro networks!

PUBLIC SPACE

Excellent ratio of green/public spaces within the reccomended distances!

POINTS!

WALKABILITY

S POIN NU T O

High levels of walkability to public spaces from residential!

You earned 25 progress stars! - you placed over 50 typologies - over 5 macro public spaces - you used the whole site! Well done!

replay...

PLEASE CLICK THE BUTTON BELOW TO VIEW THE VIDEO PLAYER WALKTHROUGH VIDEO

THE VIDEO CAN ALSO BE FOUND IN THE ADDITIONAL UPLOADS SECTION

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NO.: 008 SCENARIO: 1 OCCUPATION: DEVELOPER AGE: 35-44 LIVING STATUS: WITH PARTNER

RUNNING THE GAME 0001001000010010 101001101101001101 0001001000010010 11110010011111001001 0001001000010010 110011100110011100 01010011100101001110

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DEMONSTRATING THE METRICSTHE EXISTING HULME NEIGHBOURHOOD

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METRIC ANALYSIS ONE COMMUNITY

1.1

5pts

1 : 59 +0 commercial : residential commercial typologies residential typologies

1.1 RULE: Ratio of 1:10 commercial blocks to residential blocks

1:5

1:8

1:10

1:12

1:15

1.2 +0

1.2RULE: Ratio of 1:6 places of work to residential blocks 1:3

1:4

1:6

1:8

1:9

+ +

1.3 RULE: Accomodating a public space every 100m

1.4

x>80%

79%>x>60%

59%>x>40%

1.3 +5

79%>x>60%

59%>x>40%

workplace typologies (including commercial, amenity and office typologies) residential

50% of public spaces within 100m public space

RULE: 10-15 houses per micro network x>80%

1 : 59 workplace : residential

1.4 +0

The existing site does not meet the ratio of commercial nor workplace ratio to residential. The site does not meet the standard suggested of 10-15 houses per micro network, however, the public spaces are distributed relatively across the site.

40% micro networks with 10-15 houses low rise housing

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METRIC ANALYSIS TWO PUBLIC SPACE

2 2.1

2.2

2.3

15pts

2.1 +0

RULE: Ratio of 9m2 green space per resident 9m2

7m2

5m2

2m2 per person public space

residential typologies

RULE: Maximised vehicle free zones x>80%

80%>x>60%

60%>x>40%

40%>x

2.2 +0

34% vehicle free space (including public space)

RULE: Distance to neighbourhood green spaces not to exceed 400m 400>x

x>400

+ +

2.3 +5

99% distance to green space

Overall, the existing site did not score well on public space metrics. The public space available did not allocate enough space per person, but there was public space within a distance of 400m for the majority of residential buildings.

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METRIC ANALYSIS THREE WALKABILITY

10pts

3.1

3.1 RULE: 400m distance to local amenities x>400

+5 37

2m 25

400>x

3.2

RULE: Max. 150m distance from residential to urban green spaces x>150m

150>x>130

130>x>110

110m>x

80%>x>60%

60%>x>40%

40%>x

3.4

RULE: Ratio of 1:6 places of work to residential blocks x>2.0

2.0>x>1.5

1.5>x>1.0

1.0>x

99% distance to amenities

3.2 +5

99% distance to urban green space

3.3 +0

3.3RULE: Maximised ratio of public:private space x>80%

22% public space private space

395m

Due to the size of the site, the majority of residential buildings had amenities within 400m and had micro green space within 150m. Despite this, the site had a low public to private ratio with only 22% of the site having public access.

3.4 +0

1 : 59 workplace : residential

49/102

001

002

003

004

005

006

007

008

009

010

011

012

013

014

015

016

017

018

50 /102

006

008

51 /102

1.1 +10

006

1:10 commercial to residential

008

SCENARIO: CHANGE TO EDUCATION AGENT: SINGLE PARENT

1:7

1.1

SCENARIO: CHANGE TO EDUCATION AGENT: DEVELOPER

1:10 commercial to residential

1 : 62

1.2 +0 1 : 62 1:6 work to residential

1:6 work to residential

1.3 +5 40%

Curated public space every 100m Public space every 100m

1.2 +5

1.3 +0 30%

Curated Public public space every space 100m every 100m

1:5

10-15 houses per micro network

2.1

9m2 green space per resident

+7 7m2 +

Neighbourhood green spaces 400m

2.3 +10 99% 3.1

2.1

Maximised vehicle free zones

+ Neighbourhood green spaces 400m

2.3 +10 99%

400m distance to local amenities

3.1

Max. 150m from residential to green spaces

Max. ratio of public:private

1:6 places of work to residential

9m2 green space per resident

+0 4m2

2.2 +10 93%

+5 99% 3.2 +5 99% 3.3 +2 70% 3.4 +1 1:5

10-15 houses per micro network

1.4 +15 80%

1.4 +5 40%

Maximised vehicle free zones

2.2 +7

61%

400m distance to local amenities Max. 150m from residential to green spaces

+5 95% 3.2 +5 99%

Max. ratio of public:private

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ALL

3.3 +0 22%

3.4 +0

1:6 places of work to residential

1 : 62

52 /102

010

015

53 /102

1.1 +10

015

1:10 commercial to residential

010

SCENARIO: CHANGE TO RESIDENTIAL AGENT: STUDENT

1:9

1.1 +10

1:10 commercial to residential

SCENARIO: CHANGE TO RESIDENTIAL AGENT: RETIREE

1:9

1.2 +0

1:6 work to residential

+ +

1:6 work to residential

1.3 +10 62%

Curated public space every 100m Public space every 100m

1.2 +0

1.3 +15 86%

Curated Public public space every space 100m every 100m

1:9

10-15 houses per micro network

9m2 green space per resident

2.1 +10 13m2 +

Neighbourhood green spaces 400m

2.3 +10 99% 3.1

2.1

9m2 green space per resident

+0 4m2

2.2 +10 82%

Neighbourhood green spaces 400m

2.3 +10 99%

400m distance to local amenities

3.1

Max. 150m from residential to green spaces

Max. ratio of public:private

1:6 places of work to residential

1.4 +0 40% +

Maximised vehicle free zones

+5 99% 3.2 +5 99% 3.3 +1 42% 3.4 +0 1:9

10-15 houses per micro network

1.4 +10 60%

1:7

Maximised vehicle free zones

2.2 +3

61%

400m distance to local amenities Max. 150m from residential to green spaces

+5 95% 3.2 +5 99%

Max. ratio of public:private

Click on the buttons below to highlight typologies! commercial typologies

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base

ALL

3.3 +0 22%

3.4 +0

1:6 places of work to residential

1 : 62

54 /102

012

002

55 /102

1.1 +10

012

1:10 commercial to residential

002

SCENARIO: HIGH RISE RESIDENTIAL AGENT: STUDENT

1:8

1.1

SCENARIO: HIGH RISE RESIDENTIAL AGENT: ACADEMIC

1:10 commercial to residential

0:1

1.2 +0

1:6 work to residential

+ +

1:6 work to residential

1.3 +0 35%

Curated public space every 100m Public space every 100m

1.2 +5

1.3 +0 30%

Curated Public public space every space 100m every 100m

1:5

10-15 houses per micro network

2.1

9m2 green space per resident

Neighbourhood green spaces 400m

2.3 +10 99% 3.1

9m2 green space per resident

2.2 +7 75%

Neighbourhood green spaces 400m

2.3 +10 91%

400m distance to local amenities

3.1

Max. 150m from residential to green spaces

+5 99% 3.2 +5 99% 3.3 +0 29% 3.4 +5 1:5

0% Maximised vehicle free zones

2.2 +10 86%

400m distance to local amenities

Max. ratio of public:private

1:6 places of work to residential

1.4 +0

2.1 +10 126m2

Maximised vehicle free zones

10-15 houses per micro network

1.4 +10 50%

+3 5m2

0:1

Max. 150m from residential to green spaces

3.2 +5 99%

Max. ratio of public:private

Click on the buttons below to highlight typologies! commercial typologies

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ALL

3.3 +5 91%

3.4 +0

1:6 places of work to residential

0:1

56 /102

016

004

57 /102

1.1

016

1:10 commercial to residential

SCENARIO: BUSINESS DISTRICT AGENT: SINGLE PARENT

1 : 10

004

1.1

SCENARIO: BUSINESS DISTRICT AGENT: STUDENT

1:10 commercial to residential

1 : 13

1.2 +0

1:6 work to residential

+ +

1:6 work to residential

1.3 +10 71%

Curated public space every 100m Public space every 100m

1.2 +5

1.3 +5 42%

Curated Public public space every space 100m every 100m

1:6

10-15 houses per micro network

9m2 green space per resident

2.1 +10 16m2 + +

2.3 +10 99% 3.1

10-15 houses per micro network

1.4 +5 45%

Neighbourhood green spaces 400m

2.1 +10 15m2

2.2 +10 88%

Neighbourhood green spaces 400m

2.3 +10 99%

400m distance to local amenities

3.1

Max. 150m from residential to green spaces

Max. ratio of public:private

1:6 places of work to residential

+ 9m2 green space per resident

Maximised vehicle free zones

+5 99% 3.2 +5 99% 3.3 +1 45% 3.4 +1 1:6

1:2

+ 1.4 +0 20%

Maximised vehicle free zones

2.2 +7

68%

400m distance to local amenities Max. 150m from residential to green spaces

+5 98% 3.2 +5 99%

Max. ratio of public:private

Click on the buttons below to highlight typologies! commercial typologies

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ALL

3.3 +1 42%

3.4 +0

1:6 places of work to residential

1:2

58 /102

005

003

59 /102

1.1

005

1:10 commercial to residential

SCENARIO: PEDESTRIANISATION AGENT: STUDENT

1 : 13

003

1.1

SCENARIO: PEDESTRIANISATION AGENT: STUDENT

1:10 commercial to residential

0:1

1.2 +0

1:6 work to residential

+ +

1:6 work to residential

1.3 +0 30%

Curated public space every 100m Public space every 100m

1.2 +10 1 : 10

1.3 +0 11%

Curated Public public space every space 100m every 100m

10-15 houses per micro network

9m2 green space per resident

2.1 +10 8m2 +

2.3 +10 99% 3.1

9m2 green space per resident

2.1 +10 117m2

Maximised vehicle free zones

Neighbourhood green spaces 400m

10-15 houses per micro network

1.4 +0 35%

2.2 +10 85%

Neighbourhood green spaces 400m

2.3 +10 99%

400m distance to local amenities

3.1

Max. 150m from residential to green spaces

+5 98% 3.2 +5 99% 3.3 +0 32% 3.4 +2 1 : 10

+ 1.4 +0 27%

Maximised vehicle free zones

2.2 +10 83%

+ +

400m distance to local amenities

Max. ratio of public:private

1:6 places of work to residential

0:1

Max. 150m from residential to green spaces

3.2 +5 99%

Max. ratio of public:private

Click on the buttons below to highlight typologies! commercial typologies

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ALL

3.3 +2 64%

3.4 +0

1:6 places of work to residential

0:1

60/102

000: A CURATED HIGH SCORING ITERATION SCENARIO: PEDESTRIANISATION AGENT: ACADEMIC

1.1 +15

1:10 commercial to residential

106

1 : 11

1.2 +15 1 : 9

1:6 work to residential

1.3 +15 86%

Curated public space every 100m

10-15 houses per micro network

9m2 green space per resident

2.1 +10 33m2 +

Neighbourhood green spaces 400m

2.3 +10 99% 3.1 Click on the buttons below to highlight typologies! commercial typologies

low rise housing business typologies education typologies

residential typologies

base

ALL

+ 1.4 +15 83%

Maximised vehicle free zones

2.2 +10 93%

+ +

400m distance to local amenities

Max. 150m from residential to green spaces

+5 99% 3.2 +5 99%

Max. ratio of public:private

3.3 +1 57%

3.4 +5

1:6 places of work to residential

1:9

61 /102

62 /102

NO.: 017 SCENARIO: 3 OCCUPATION: DEVELOPER AGE: 45-54 LIVING STATUS: WITH FAMILY

RESULTS OF THE GAME

64/102

THE SCORES

O RE C S

017

018

001

002 00 3

4 00

SCENARIO: 3

015

115

014

006

3 01

00 7 00

009

01 2

010

O RE C S

008

SCENARIO: 1 OCCUPATION: DEVELOPER

011

AGE: 35-44 LIVING STATUS: WITH PARTNER

115

LIVING STATUS: WITH FAMILY

115

AGE: 45-54

005

OCCUPATION: DEVELOPER

LOWEST

7 01

01 6

HIGHEST

115

65 /102

0 5

Scenario

0 5

Scenario

Average of 1, Average of

OVERVIEW BY METRIC FAMILY

0 False

True Car

Scenario

Car

Max of Total Score

True

Scenario

45-54 False

65+

Car

Max of Total Score

40 30

No.

0 25-34 18-2445-54 35-4465+ Age 0

PUBLIC SPACE

45-54

65+

t r c 0 ee ivist rent pe den emi with tir o t a l e Scenario e R Ac le P Stu cad ev e family A t g D a Sin 0 im t r 25-34 18-24 35-44 Cl c ee ivist rent pe den emi tir o t a l e c u d Age P R A a ve St Ac te Occupation gle De a n i m S Cli

25-34 18-24 35-44 Age

45-54

Average of 1 30

5 single

Average of 1 30

Average of 2

Average of 3

with family

Average …

with with partner friends

0 Living Status

Average of 1, Average of 2 and Occupation Average of 3 by Age

Average of 1, Average of 2 and A…

A…

Average …

single

20 0 0 10 0 t r 25-34 18-24 35-44 t t tree ic ist r ic e e n nt s e n n No. i e ir em tiv Pare elop tudReeti demctiv Pare elop tude dwith c Age Ret a v A a A S v e S Ac family Acate ingl De te gle De a n i m S m S Cli Cli

Occupation

Average of 1, Average of 2 and Average of 2

Average of 3

True

False

wit with with fam partner frien

Living Status

80 70 60 50 40 30

Max of Total Score by No. Occupation

AVERAGE OF METRIC SCORES BY Average of 3 by Car CAR OWNERSHIP

Average of 1

single

No.

Car

with with partner friends

Average of 1, Average of 2 and Average of 3 by Occupation Average of 1 30

Scenario

Living Status

…

WALKABILITY

Average of 2

Average of 1, Average o…

Average of 1

Score AverageAverage of 1, Average o…

30 Average of 3

Average of 1, Average of 2 and A…

Average of 2

Average of 1, Average o…

320

Average of 1

Average of 1, Average o…

Average of 1, Average of 2 and A…

of 1,Average Averageofof1,2 Average and of 1, Average of 2 and Average of 1, Average of 2 and Average of 1, Average of 2Average and of 2Average and AVERAGE OF METRIC SCORES BY by Scenario Average of 3 by Scenario Average of 3 by Age Average of 3 by OccupationAverage of 3 Average of 3 by Living Status LIVING STATUS Average of 1, Average of 2 and Average of 1, Average of 2 and Average of 1, Average of 2 and Average…of 1 Average of 2of 1Average of 3 of 2 Average…of 1 Average of 2 Average of 3 Average of 1 Average of 2 Average of 3 Average of 1 Average of 2 Average Average Average Average Average of 3 by Age Average of 3 by Occupation Average of 3 by Living Status

Average of 1, Average o…

False

Average of 1, Average o…

Scenario 0

True 0

Average Average of 1,Score Average o…

Average of 1, Average o…

Scenario

Max of Total Score

Average Score Average of 1, Average of 2 and A…

Average of 2

Average …

Average of 1, Average of 2 and Average of 3 by Living Status Average of 1

…

70 of 1of 3Average of Average 2 Average Average of 1 …Average Average of 2 Average … Average of 1 Average 1 Average Average Average of 2 ofAverage of 3 of 2Average of 1of 3Average of 2 Average of 1 Average of 2 Averag 60 30 30 30 30 30 30 60 50 20 20 20 20 20 20 50

Average of 1

Average of 1, Average of 2 and A…

Average30 of 3

Score Average Average of 1, Average of 2 and A…

COMMUNITY 20

Average of 2

Average of 1, Average of 2 and A…

Average of 1

Average of 1, Average of 2 and A…

Average of30 3

Average Score Average of 1, Average of 2 and A…

1 20

Average of 2

Average of 1, Average of 2 and A…

Average of 1

Average of 1, Average of 2 and A…

Max of Total Score by No. Max of Total Score by No. Average of 1, Average of 2 and Average of 1, Average of 2 and Average of 1, Average of 2 and 80 of 1, Average of 2 andAverage ofAverage of 1, Average of 2 and Average of 2 and Average of 1, Average of 2 and Average of 1 AverageAverage of 3 by Scenario 3 by Scenario Average of 3Average by Car of 1, Average 80of 1, Average Averageof of2METRIC 1,and Average Average of 1, Average of 2 and Average of 1, Average of 2 and AVERAGE OF SCORESof BY2 and AVERAGE OF METRIC SCORES BY AVERAGE OF METRIC SCORES BY Average of 3 by Occupation Average Average of 3 by Scenario Average of 3 by Scenario Average of 3 byAGE Car Average of 3 by Average of 3 by Occupation Average of 3 by Living Statusof 3 70 AgeAverage of 3 by Age OCCUPATION Average of 1 Average of 2 Average of 3 Average of SCENARIO 1 Average of 2 Average of 3 Average of 1 Average of 2 Average of 3

Average of 2

Average …

66 /102

OVERVIEW SCORES

COMMUNITY

METRIC

10 10 10

1.1 RULE: Ratio of 1:10 commercial blocks to residential 0

blocks 0 1:5

1:8

1:10

5 1:12

1:15

Scenario

5 5

0 0

5 0 0 0

1.2RULE: Ratio of 1:6 places of work to residential blocks

Average of 1.1 by Age 1:3

1:4

65+

100%

Average of 1.2 by Age

1:6

1:8

1:9

65+

10.00

45-54 10.00 1.3 RULE: Accommodating a public space every 100m

45-54 35-44 25-34

79%>x>60%

59%>x>40%

6.25 10

18-24

1.4

2.50

x>80%

35-44 25-34 0

18-24

4.44

RULE: 10-1544.4% houses per micro network x>80%

79%>x>60%

59%>x>40%

Academic

15 10 5

Student

20 15 20

10 10 10

0 Scenario Scenario Scenario

5 0 0 0

with family Retiree Scenario Average 2.1Average Average 2.2 of 2.3 of 2.1 Average of 2.2 Average of 2.3 Scenario Average of 1.1of Average of 1.2of of 1.3of 1.3Average of 1.4 of 1.1 of 1.2 of 1.3 Average of of 1.41.4 Average of 1.1 Average of 1.2 ofof1.3 Average ofof1.1 Average of 1.2Average Average ofAverage 1.3 Academic Average 1.1 Average 1.2Average Average Average of 1.4Average Average of 1.1 Average Average of 1.2 Average Average of 1.3 with Average family Retiree Retiree Average of 1.1 Average of Single 1.2 Average of 1.3 with family Parent with family Retiree AverageAverage of 1.1 of 1.1 Average of 1.2 of 1.2 Average of 1.3 of 1.3 Average Average

Average Score of Metric 1

15 15 15

30 20

Average Score Metric Average Score of Metric Average Score ofofMetric 12 2

Average Score of Metric 1 Average Score of Metric 1 Average Score of Metric 1

HIGHEST

Average Score of Metric 1

TOTAL Score POSSIBLE Average of Metric 1 by Average of 1.1, Average of 1.2, Average of 1.1, Average of 1.2, Average of 1.3 and Average of 1.1, Average of 1.2, Average of 1.3 and SCORE: 60 RE O Scenario Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of 1.4 by Living Status SC SCENARIO PLAYER ATTRIBUTES Scenario 1of 1.4 Average 20AVERAGE SCORE: 1 Average 006 Average Score ofofMetric 1 byof 1.2 Average of 1.1, Average of 1.2,of 1.1 Average Average 1.1, Average of 1.2, Average of 1.1, Average of 1.2, Average of 1.3 a AVERAGE OF METRIC CLUSTER SCENARIO Average 1.1 BY Average Average of 1.3 Average of 1.2 of Average of 1.3 Average of 1.4 Average of 1.3 and Score of Metric 1 by Average of 1.1, Average of 1.2, Average of 1.1, Average of 1.2, Average of 1.3 and Average 1.1, Average 1.2,Average Averageofofof1.3 1.3and and Average Score of Metric 1 by Average of 1.1, Average of 1.2, Average of 1.1, Average of 1.2, Average 1.3 and Average 1.1, Average 1.2, Ave 15 Score of Metric 1Average by Score Average of 21.1, Average ofof1.2, Average of 1.1, Average of 1.2, Average of 1.3 and Average ofofof 1.1, Average ofofof1.2, Scenario Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of 1.4 by Living Status of Metric by Average 2.1, Average of 2.2 and Average of 2.1, Average of 2.2 and Average of 2.3 by 2.1, 2.2 and 2.3 by Living Status Average of 1.1 Average of 1.2 Average Average of 1.3 with family Retireeof 1.3 Average Scenario Average and Average of 1.4 byAverage Average of 1.4 byOccupation Occupation Average of 1.4 by Living Status ScenarioAverage of 1.3 and Average 1.4 bybyofAverage of 1.4 by Occupation Average of 1.4 by AVERAGE OF METRIC SCORES AVERAGE METRIC SCORESofBY AVERAGE OF METRIC SCORES BY Living Status Score ofofMetric 21.3 by Average of 2.1, Average of 2.2 andofBY Average 2.1, Average ofOF 2.2 and Average 2.3 by 2.1, 2.2 and 2.3 by Status Scenario Average of and Average of 1.4 by of 1.4 Average of 1.4 by Living Status Scenario 2.3 by Scenario Occupation Scenario 20 Average Score Metric 1 by Average of 1.1, Average of 1.2, Average of 1.1, Average of 1.2, Average of 1.3 and Average of 1.1, ofLiving 1.2, Average 2.1of 2.2Average 2.3 Average Score of Metric 1 by Average of 1.1, Average of 1.2, of of of and Average 1.1, Average of 1.2, Average of1.3 1.3and and Average of 1.1Average Average of1.1, 1.2 Average Average of1.2, 1.3 Average Average of1.3 1.4 Average 1.1 Average of 1.2 Averageofof 1.3 Average of 1.4 15MEDIAN SCORE: SCENARIO OCCUPATION LIVING STATUS Scenario Scenario Scenario Average of 2.3 by Scenario Occupation 20 20 30 Scenario Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of 1.4 by Living Status Scenario Average of 1.1 Average of 1.2 Average of 1.3 Average of 1.4 Average of 1.1 Average of 1.2 Average of 1.3 of 1.4of 1.3 2.1 2.2 2.3 Average of 1.1 Average of 1.2 Average of 1.3 Average of 1.4 Average of 1.1 Average of 1.2Average Average 20 Average of 2.1 Average of 2.2 Average of 2.3 Average of 2.1 Average of 2.2 Average of 2.3 Scenario Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of 1.4 by Living Status 15 Average of 1.1 Average of 1.2 Average of 1.3 Average of 1.4 Average of 1.1 Average of 1.2 Average of 1.3 Average of 1.4 2 Average of 1.1 Average of 1.2 Average of 1.3

Academic Retiree Academic Academic Academic Retiree Academic Academic

with friends

4 4

0 0 0 0

5 5 5

Scenario 5 Scenario Scenario Scenario

5 5Developer Scenario

Average of 2.1 by Age Average ofof2.1 by Age Average of by Age 100% by1.1 Age AverageAverage of 1.3 by 1.1 Age

100% 100% 100%

AGE

5 55

Academic Retiree Retiree

2 2 4

single

Single Single Parent Parent Single Parent Single Parent Parent Single Single Single Parent

Developer Student Student

3 1

si...

with friends withwith friends wi... friends

with friends with friends 3

Single Parent wi...

Climate Act... 5

StudentDeveloper 1 Student with partner Student

100%

single si... single

with friends

singl single single

Climate Activist Climate Act...Act...

Student

Climate Act... Climate Activist Climate Act... Climate Act...

Climate Act...

with friends

with friends partner withwith partner

Developer Developer Student

Average of 2.2 by Age Average ofof2.2 Age Average ofby 1.2 byby Age 100% Average 1.2 by Age Average of 1.4 Age

with fami

withwith family family with family

Student

Developer Average of 2.3 by Age Developer Developer Developer Average of 2.3 by Age 100% Average of 1.3 by Age Average of 1.3 by Age

Average Age Average of of 1.41.4 byby Age

100% 100% 100% 65+ 10.00 AVERAGE SCORE FOR 3.3 BY AGE 65+ 10.00 65+ 12.50 65+45-54 65+65+ 12.50 10.00 100% 45-54 100% 10.00 100% 45-54 45-54 100% 15.00 35-44 45-54 45-54 10.00 15.00 35-44 10.00 35-44 35-44 5.00 65+ 12.50 25-34 35-44 35-44 5.0010.00 65+ 65+ 12.50 12.5065+ 25-34 10.00 12.50 25-34 25-34 6.25 18-24 25-34 25-34 8.33 6.25 45-54 15.00 18-24 45-54 45-54 15.00 8.33 1.11 15.0045-54 18-24 18-24 15.00 18-24 18-24 1.11 35-44 5.00 83.3% 35-44 35-44 5.00 83.3% 5.00 35-44 8.9% 5.00 8.9%

with partner with partner with partner

with partner

100% 100% Average of 1.1 by Age 65+ Average of 1.265+ by Age 5.00 6.50 Average of 1.3 by Age Average of 1.4SCORE by Age Average byAge Age Average by Age Average of 1.1AGE by AgeAverage Average of 1.2 by AVERAGE SCORE FOR 3.1 BY AVERAGE FOR 3.2 BY AGE AVERAGE FOR 3.4 BY AGE 65+ 65+ 5.00 10.00 6.50Age 65+ Average ofof1.11.1by ofof1.21.2bySCORE Age 0.00 Average of 1.3 Average 65+ 65+ 65+ by Age Average of 1.4 by Age of 1.3 by Age Average of 1.4 by Age 0.00 10.00 100% 5.00 Average of 1.3 by Age Average of 1.4 by Age 45-54 45-54 5.00 10.00 7.00

0.00

5.00 65+ 65+ 65+ 0.00 45-54 45-54 45-54 0.00 35-44 35-44 35-44 2.22 25-34 25-34 25-34 18-24 18-24 18-24

100%45-54 65+ 100% 45-54 45-54

35-44 35-44 45-54 10.0035-44 35-44 65+ 10.00 25-34 10.00 25-34 25-34 25-34 35-44 10.00 18-24 45-5410.0018-24 18-24 10.00 18-24

25-34 2.50 35-44 2.50 2.50 18-24 6.25 25-34 6.25 6.25 4.44 18-24 4.44 4.44

44.4% 44.4% 44.4%

100% 12.5010.00 10.00

10.00 5.00 65+ 15.005.00 2.50 2.50 65+ 10.00 10.00 65+ 10.00 6.25 6.25 45-54 5.00 6.67 45-54 10.00 45-54 6.67 4.44 4.44

35-44 6.25133.3% 35-44 2.50 35-44 133.3% 44.4% 44.4% 25-34 1.11 25-34 6.25 25-34 18-24 18-24 4.44 8.9% 18-24

44.4%

65+ 45-54 45-5445-54 35-44 35-44 35-44 45-54 0.00 35-44 25-34 0.00 65+ 0.00 25-34 25-34 25-34 35-44 18-24 5.00 45-54 5.00 18-24 18-24 5.00 18-24 25-340.00 35-44 0.00 0.00 18-240.00 25-34 0.00 0.00 2.22 18-24 2.22 2.22

7.00 5.005.00 5.00 7.00 7.00 0.000.00 0.00 10.00 0.00 65+ 65+ 65+ 10.00 0.00 0.00 2.50 8.67 45-54 8.67 5.00 2.22 45-54 2.22 45-54

1.25 133.3% 35-44 0.00 35-44 133.3% 35-44 3.89 25-34 0.00 25-34 25-34 18-24 2.22 77.8% 18-24 18-24

6.25 25-34 6.25 6.25 18-24

1.11 1.11 1.11

8.9% 8.9% 8.9%

25-34 25-34 6.2525-34 18-24 18-24 1.11 18-24

8.9%

100% 100%

100% 100% 100% 0.00 0.00

2.50 5.00 2.50 65+ 5.00 5.00 1.25 1.25 0.00 45-54 0.00 3.89 3.890.00 2.50 35-44 2.50 77.8% 2.50 77.8% 1.25 25-34 1.25 1.25 3.89 18-24 3.89 3.89

77.8% 77.8% 77.8%

100% 5.00 0.00 2.50 1.25 3.89

77.8%

67 /102

0 5

STUDENT

SINGLE PARENT 3

RETIREE DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

1.1 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 1.1 BY SCENARIO, OCCUPATION & AGE

RULE: Ratio of 1:10 commercial blocks to residential blocks 1:5

1:8

1:10

1:12

1:15

Average of 10 1.1 5

ACADEMIC

OCCUPATION HIGHEST

SINGLE PARENT

O RE SC

006 010 011 012 015 016 017

1.4

Average of 1.1

HIGHEST SCORING SCENARIO: Construction Increase Change to High Rise Residential

SCORE

0.00

5.56 5.56

0.00

15.00

15.00 AVERAGE SCORE

Average of 1.1 by Occupation and Living Status

7 0.6

with family

Climate Activist

.33

Student

single

Academic

Developer

RETIREE DEVELOPER

CLIMATE ACTIVIST ACADEMIC

LOWEST SCORING SCENARIO: Increase in pedestrianised zones 5 Players in Scenario 5 did not associate highly pedestrianised areas with having higher levels of commercial units. 4 LOWEST SCORING OCCUPATION: Academics

OCCUPATION

TOTAL POSSIBLE SCORE: 15

Severe Demographic Change to Residential

35 - 44

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 5.5

45 - 54

Site Becomes a Business District

65+

Increase in Pedestrianised Zones

25 - 34with family

Severe Demographic Change to Education

Players with academic profiles value commercial and amenity units less in their neighbourhood.

SCENARIO

18 - 24

MEDIAN SCORE: 5

1.1 single

SINGLE PARENT

Student

15.67

STUDENT

.33

15.67

1 AGE

Developer

Students and Retirees value commercial and amenity typologies within their neighbourhoods.

SCENARIO

DEVELOPER

with friends SCENARIO

RETIREE

Single Parent 0

SINGLE PARENT

19 0

STUDENT

with partner

with friends

Climate Activist

7 0.6

Single Parent

with partner

0 9 Retiree

HIGHEST SCORING OCCUPATION: Students, Retirees

CROSS REFERENCE OF AVERAGE SCORE FOR 1.1 BY LIVING STATUS Average of AND 1.1 byOCCUPATION Occupation and Living Status

Retiree

Players saw high density areas as multi-zonal and placed importance on having enough amenities for high populations of residents.

Academic

68/102

0 5

STUDENT

SINGLE PARENT

SINGLE PARENT 3

DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

1.2 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 1.2 BY SCENARIO, OCCUPATION & AGE

RULE: Ratio of 1:6 places of work to residential blocks 1:3

1:4

1:6

1:8

1:9

Average of 1.2

ACADEMIC

OCCUPATION HIGHEST

RETIREE

O RE SC

005

1.4

Average of 1.2

HIGHEST SCORING SCENARIO: Increase in Pedestrianised Zones

1.39 1.39

0.00

15.00

0.00

15.00

AVERAGE SCORE

5 CROSS REFERENCE OF AVERAGE SCORE FOR 1.2 BY LIVING Average of 1.2 by OCCUPATION Occupation and Living Status STATUS AND

Climate Activist

RETIREE

CLIMATE ACTIVIST 15 ACADEMIC

OCCUPATION Developer

Retiree

single

7 0.6 1.33

Student

DEVELOPER

Retiree

35 - 44

Construction Increase, Change to High Rise Residential

Site Becomes a Business District

Increase in Pedestrianised Zones

The majority of participants did not show that they valued workplaces in their neighbourhood, these players were mostly 25+.

TOTAL POSSIBLE SCORE: 15

Severe Demographic Change to Education



AVERAGE SCORE: 1.4

MEDIAN SCORE: 0 This visual does not support exporting.

1.2

SINGLE PARENT

Severe Demographic Change to Residential

7 0.6 1.33

Student

STUDENT

25 - 34

with family

OCCUPATION

This visual does not support exporting.

65+

1 3.

SCENARIO



ACADEMIC

18 - 24

45 - 54

RETIREE

1 AGE

Developer 17

CLIMATE ACTIVIST

SCENARIO

DEVELOPER

LOWEST SCORING AGE GROUP: 25+

RETIREE

with family

SCENARIO

this metric, suggesting that having workplaces in their 4 neighbourhood is not a high priority in their view.

SINGLE PARENT

with friends

DEVELOPER

LOWEST SCORING SCENARIO: Severe Change to Residential, Site Becomes a Business District The majority of players did not 5meet the ratios for

STUDENT

with partner

Young people were tended to value a workplace within their neighbourhood, this may be a sign of favouring convenience or shorter commutes.

with friends

SINGLE PARENT

HIGHEST SCORING AGE GROUP: 18 - 24

with partner

STUDENT

0 Average of 1.2 by Occupation and Living Status Climate Activist

SCORE

Player 005 saw a neighbourhood with increased pedestrianised zones as having higher numbers of workplaces.

single

69 /102

0 5

STUDENT

SINGLE PARENT

SINGLE PARENT 3

DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

1.3 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

O RE SC

59%>x>40%

79%>x>60%

Average of 1.3

ACADEMIC

SCORE FOR 1.3 BY SCENARIO, OCCUPATION & AGE

RULE: Accomodating a public space every 100m x>80%

OCCUPATION HIGHEST

RETIREE

010 011 017

1.4

Average of 1.3

HIGHEST SCORING SCENARIO: Severe Demographic Change to Residential

HIGHEST SCORING OCCUPATION: Climate Activists, Retirees

Climate Activists and Retirees seem to value public space most, potentially for social and re-greening reasons.

4.72 4.72

15.00

15.00 AVERAGE SCORE

5 CROSS REFERENCE OF AVERAGE SCORE FOR 1.3 BY LIVING STATUS AND Average of 1.3 by OCCUPATION Occupation and Living Status

Average of Scenario and Average of 1.3 by Occupation and Age

Average of 1.3 by Occupation and Living Status

RETIREE

24 0

Retiree

Single Parent 0

25 - 34 35 - 44

single

Student

45-54

65+

Average …

Developer 4

Average of Scenario and Average of 1.3 by Unexpectedly, players in Scenario 55 did not associate Occupation and Age

35-44

highly pedestrianised areas with having frequent public 4 spaces in their…neighbourhood. 65+ Average

45-54

RETIREE

LOWEST SCORING OCCUPATION: Student, Academic 3

Average of 1.2 by DEVELOPER CLIMATE ACTIVIST Occupation and Scenario 10 OCCUPATION

Although one student held a high score, the average student score for this metric was only 2pts suggesting that, alongside academics, they did not consider frequent public spaces to be a priority.

Scen…

ACADEMIC 1 2 3 4

SCENARIO

Severe Demographic Change to Education

5 Occupation Increase in Pedestrianised Zones

1.3

ree

ti Re

SCENARIO

TOTAL POSSIBLE SCORE: 15 5

AVERAGE SCORE: 4.7 MEDIAN SCORE: 0

Occupation 0

single

LOWEST SCORING SCENARIO: Increase in pedestrianised zones

STUDENT Age 18-24 25-34 SINGLE PARENT

5 0 r 2 Severe Demographic Change to Residential ic nt nt ist e pe r t t r c e de tiv em i … e en m elo Pa tu ire aren Ac ad v te lop u3 S t d e e c e a l e e Increase, Change A Rise Residential t g to High D R le P m ve St cadConstruction ma A Sin Cli De ing Cli 4 Site SBecomes a Business District

DEVELOPER

14 9

RETIREE

Student

35-44

65+

SINGLE PARENT

STUDENT

25-34

45 - 54 with family

Climate Activist

AGE

Developer

with friends

18 - 24 29

with family

SCENARIO

24 0

OCCUPATION Climate Activist

Scen…

CLIMATE ACTIVIST 15 ACADEMIC

with friends

Single Parent

DEVELOPER

4 Average of 1.2 by with partner and Scenario Occupation 3

18-24

with partner

Retiree STUDENT

SINGLE PARENT

Age

Average of Scenario

0.00

Average of Scenario

0.00

SCORE

Players of Scenario 2 included public spaces at more regular intervals, perhaps because more low rise typologies were used giving space for micro public spaces.

e ic … er nt nt te lop ude dem etire are a e P R le m v St ca A Cli De g Sin Occupation

Sin

are eP

e ad

Stu

ate

m Cli

vis

ti Ac

Occupation

lo ve

ree

ti Re

70 /102

0 5

STUDENT

SINGLE PARENT

SINGLE PARENT 3

DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

1.4 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 1.4 BY SCENARIO, OCCUPATION & AGE

RULE: 10-15 houses per micro network 59%>x>40%

79%>x>60%

x>80%

Average of 1.4 15

ACADEMIC

OCCUPATION HIGHEST

RETIREE

O RE SC

006

1.4

Average of 1.4

HIGHEST SCORING SCENARIO: Severe Demographic Change to Education Players saw low rise housing clusters as having value in an education context. This could be because it is a popular student housing typology.

SCORE 15

15.00

0.00

15.00

AVERAGE SCORE

Age

18-24

25-34

18 - 24

single 0 5.67

Single Parent

Academic

25 - 34with family

LOWEST SCORING SCENARIO: Increase in pedestrianised zones

STUDENT

Players in Scenario 5 did not associate highly pedestrianised areas with having low rise lower density housing.

SINGLE PARENT LOWEST SCORING OCCUPATION: Academics

5 DEVELOPER

CLIMATE ACTIVIST

ACADEMIC4

SCENARIO

Severe Demographic Change to Education 1

35 - 44

Construction Increase, Change to High Rise Residential

45 - 54

Site Becomes a Business District 1 2

65+

Increase in Pedestrianised Zones

1.4

Scenario

Players with academic profiles favoured high rise residential typologies.

OCCUPATION

TOTAL POSSIBLE SCORE: 15

AVERAGE SCORE: 3

MEDIAN SCORE: 0

5.67

15.6

Student

65+

RETIREE

Severe Demographic Change to Residential

single 0

SINGLE PARENT

45-54 5

STUDENT

35-44

0 AGE

DEVELOPER

Retiree

Developer

RETIREE

with family

Student

Average of 1.4

Developer

Age

with friends SCENARIO

15.6

with partner

with friends

Retiree

Average of 1.4 by Scenario and Age

65+

OCCUPATION

1.3

Climate Activist

Single Parent

45-54

SINGLE PARENT

CLIMATE ACTIVIST 15 ACADEMIC

with partner

STUDENT

DEVELOPER

35-44

Average of 1.4 by Occupation and Living Status

RETIREE

25-34

5 CROSS REFERENCE OF AVERAGE SCORE FOR 1.4 BY LIVING STATUS AND Average of 1.4 by OCCUPATION Occupation and Living Status 0 Climate Activist

18-24

Climate Activists and Retirees appear to value low-density low rise housing most.

Average of 1.4 by Scenario and Age

Average of 1.4

0.00

3.06 3.06

HIGHEST SCORING OCCUPATION: Climate Activists, Retirees

Academic

Scenario

71 /102

NO.: 013 SCENARIO: 3 OCCUPATION: CLIMATE ACTIVIST

AGE: 35-44 LIVING STATUS: WITH PARTNER

OVERVIEW OF PUBLIC SPACE METRIC SCORES

015

016 10

018

7m2

5m2

Average Score of Metric 2 by Average of 2.1, Average of 2.2 and Average of 2.1, Average of 2.2 and Average of 2.3 by 2.2 and 2.3 by Living Status Average Score of Metric 2ofby Average of 2.1, Average of 2.2 and Average of 2.1, Average of 2.2 and2.1,Average of 2.3 by 2.1, 2.2 and 2.3 by L Average 2.3 by Scenario Occupation Scenario Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of 1.4 by Living Status Scenario Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of 1.4 Living Status 2.1 2.2by 2.3 AVERAGE OF METRIC SCORES BY AVERAGE OF METRIC SCORES BY AVERAGE OF of METRIC SCORES BY Scenario Average 2.3 by Scenario Occupation 3020 2.1 2.2 2.3 Scenario Scenario Average 2.1 Average ofof 2.22.3 Average of 2.3 ofAverage 2.1 Average Averageof of 2.3 2.3 20 Average of 2.1 Average of of 2.2 Average Average 2.1 ofAverage of 2.2of 2.2 Average

Average Score ofofMetric Average of 1.1, Average Average of 1.1, Average of 1.2, Average of with 1.3 family and Average of 1.1, Average of 1.2, Average of 1.3 and Score of Metric 1...by 1 by Average of 1.1, Average of 1.2, of 1.2, Average of 1.1, Average of 1.2, Average of 1.3 and Average of 1.1, Average of 1.2, Average of 1.3 and Average of ... Average Average of ... Average Activist Academic Scenario Average of 2.3 by Scenario Climate Occupation 2.1 2.2 2.3 30

Scenario

15 20

10 10

5 0

RULE: Maximised vehicle free zones

80%>x>60%

60%>x>40%

Academic 4

5 0

5 1

Average of 3.2 by Age Average of 2.1 100% by Age

Average of 3.1 by Age 0

100%

5.00

65+ AGE Average of 2.1 by Age 65+

5.00 5.00

25-34

3.75

18-24

3.33 66.7%

65+

45-54

65+ 45-54 35-44 25-34 18-24

35-44 25-34

100%

4 2

Retiree

5.00 10.00

10.00

Single Parent

Retiree

5 1

Developer 3

Developer

3 Single Parent 1 Average ofof2.21.2 byby Average ofAge 1.2 Average Ageby Age

Average of 3.3 by100% Age Average of 2.2 by Age 100%

65+

6.50 0.00

0.00

Climate Act... Climate Act...

Developer

133.3%

10.00 133.3% 10.00 35-44 44.4% 44.4% 5.00 5.00 6.67 25-34 100% 10.00 18-24 133.3% 6.67 133.3%

si... single

Student

with partner Climate Activist

Developer

Average of 2.3 by Age Average of byby Age Average of 1.3 by Age Average of1.3 3.4100% Age

100%

100% Student

45-54 25-34 7.00 18-24 35-44 18-24 10.00 25-34 8.67 18-24 133.3%

7.0010.00 133.3% 35-44 1.78 7.00 8.67 25-34 355.6% 10.00 18-24 133.3% 8.67 133.3%

18-24

Climate Activist

single

100%

Student 10.00 12.50 12.50AVERAGE 65+ 5.00 SCORE65+ FOR 2.3 BY AGE 10.00 0.00 65+ 100% 10.00 45-54 45-54 15.00 15.00 0.00 100% 10.00 5.00 35-44 35-44 5.00 5.00 45-54 2.50 10.00 10.00 10.00 0.00 25-34 25-34 6.25 6.25 1.25 65+ 35-44 10.0010.00 8.33 10.00 18-24 18-24 1.11 1.11 0.00 3.89

45-54 25-34 83.3% 10.00 0.89 8.9% 8.9% 35-44 18-24 10.00 25-34 8.33 18-24 83.3%

wit

Climate Activist

of 1.4 Average of 1.4 by Ageby Age Average of 2.3 byAverage Age 100%

wi...

friends with partner with with partner

Student

Average of 2.2 by Age Average of 2.3 by Age Average of 2.1 by Age Average of 2.2 by Age Average of 2.3 by Age

45-54 25-34 5.00 18-24 35-44 18-24 10.00 25-34 6.67 18-24

wi...

Single Parent

wi... Student Student Retiree

DeveloperDeveloper

65+ 65+

with family

si...

with friends wi... with friends

65+ 65+ 65+ AVERAGE SCORE FOR 2.1 BY AGE AVERAGE SCORE FOR 2.2 BY AGE 65+ 45-54 45-54 45-54 65+ 5.00 0.50 10.00 7.00 45-54 45-54 100% 45-54 45-54 5.00 10.00 5.00 10.00 5.00 65+ 65+ 100% 6.5045-54 45-54 35-44 35-44 35-44 100% 100% 5.00 7.001.00 45-54 45-54 35-44 35-44 35-44 35-445.00 0.00 2.50 0.00 2.50 35-44 35-44 45-54 45-54 10.00 7.00 25-34 25-34 25-34 10.00 10.00 65+ 65+ 5.00 25-34 25-34 25-34 25-34 6.50 6.25 0.00 6.25 35-44 5.00 0.50 0.00 25-34 25-34 35-44 65+ 35-44 65+ 35-44 5.00 5.00 6.50 7.00 18-24 18-24 18-24 6.67 8.67 18-24 18-24 2.22 4.44 2.22 4.44 45-54 45-54 10.0018-24 18-245.00 18-24 18-24 25-34 25-34 7.00 3.25

65+

Single Parent

Single Parent Single Parent Single Parent

wi...

with family withwith family family

Academic

Retiree

Average of 2.3

5 Student

5 ScenarioScenario Scenario Scenario 2

Average of 2.2 Academic Retiree Retiree Academic

Academic Retiree Academic

Average of 2.1

Developer

Average ofof2.1 Average ofAge 1.1 Average 1.1byby Ageby Age

40%>x

RULE: Distance to neighbourhood 45-54 green spaces not to exceed 400m 35-44 x>400

of 2.1 ofAverage Average of 2.3 of 1.1 of 1.2of 2.2 of 1.3 AverageAverage ofAverage 1.1 Average 1.2 Average ofAverage 1.3 Average

Scenario

65+

400>x

0 Scenario

STATUS of 1.1 ofAverage of 1.2 ofAverage of 1.3 ofAverage of 1.4 ofAverage of 1.1 of Average of 1.2 ofAverage of 1.3 ofAverage of 1.4 Average ofAverage 1.1OCCUPATION Average 1.2 Average 1.3 Average 1.4 Average 1.1 LIVING Average 1.2 Average 1.3 Average 1.4

SCENARIO

Average Score of Metric 1

Scenario

009

x>80%

2.3

011

RULE: Ratio of 9m2 green space per resident 9m2

2.2

002

005

Average Score of Metricof 2 Metric 2 Average Score Average Score of Metric 1

2.1

001

Average Score of Metric 2

MEDIAN SCORE: 27

Average Scoreofof3.2, MetricAverage 2 by of 3.1, Average of 2.1, Average of of 2.23.4and of and 2.1, Average of 2.2 and Average of 2.3 by 2.1, 2.2 and 2.3 b Average of 3.1, Average Average of 3.2, Average andAverage 3.1, 3.2, 3.3 3.4 by Living Status Scenario Average of 2.3 by Scenario Occupation Average of 3.3 and Average of 3.4 Average of 3.3 by Occupation 2.1 2.2 2.3 3.1 3.2 3.3 3.4 PLAYER ATTRIBUTES by Scenario 30 2of 3.1 Average Average 2.1 Average Average Average of 2.3 Average 2.1 Average Average of 2.2 Average AVERAGE OFScore METRICof CLUSTER Average 3.2 ofof 3.42.2ofAverage of 3.3 Average MetricBY2SCENARIO by Average of 2.1, Average of 2.2ofofand Average 2.1, Average of 2.2ofand of 2.3 by of 2.3 2.1, 2.2 and 2.3 by Living Status

Average Score of Metric 3 by Scenario SCENARIO

Average Score of Metric 2

AVERAGE SCORE: 25

O RE SC

Average Score of Metric 3

HIGHEST

TOTAL POSSIBLE SCORE: 30

10.0010.00 10.008.33

100%

with friends

with frien

5.00 0.00 2.50 1.25 3.89

77.8% 77.8%

10.00 83.3% 8.33 83.3%

73 /102

0 5 STUDENT

STUDENT

SINGLE PARENT 3

RETIREE

MATE ACTIVIST

RETIREE

SCENARIO

2.1 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 2.1 BY SCENARIO, OCCUPATION & AGE

RULE: Ratio of 9m green space per resident 2

9m2

7m2

5m2

Average of 2.17 10

ACADEMIC

OCCUPATION HIGHEST

SINGLE PARENT DEVELOPER

1.1

0.00

10.00

AVERAGE SCORE 10.00

Average of 2.1 by Occupation and Living Status

Average of 2.1 by Occupation and Living Status 9 0

with family

with friends

9. 33

SINGLE PARENT

with partner

Single Parent

Academic 0

STUDENT

with friends

9. 33

Single Parent

with partner

Academic

RETIREE

SCENARIO

Student

AGE

Student players consistently placed the most value on public space, but none reached the advised area per person.

with family

CLIMATE ACTIVIST ACADEMIC

LOWEST SCORING SCENARIO: Increase in pedestrianised zones, Site Becomes Business District 5 It is possible that in playing in the scenario of increased pedestrianised zones players placed more high rise 4 residential for their small footprint, but not enough public space to compensate for this. 3 LOWEST SCORING OCCUPATION: Developers, Retirees

OCCUPATION

By average score, Developer and Retiree players scored lower. Developers potentially because they valued a denser neighbourhood for financial reasons.

SCENARIO

TOTAL POSSIBLE SCORE: 10

Severe Demographic Change to Residential

35 - 44

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 7.2

45 - 54

Site Becomes a Business District

65+

Increase in Pedestrianised Zones

18 - 24

25 - 34

Severe Demographic Change to Education

MEDIAN SCORE: 10

2.1

Student

Retiree

single

26.67

Developer

HIGHEST SCORING OCCUPATION: Students

SCENARIO

DEVELOPER

2 1

Climate Activist

Players saw high density areas as needing higher levels of public space, perhaps due to the much increased number of residents.

CROSS REFERENCE OF AVERAGE SCORE FOR 2.1 BY LIVING STATUS AND OCCUPATION

018

HIGHEST SCORING SCENARIO: Construction Increase Change to High Rise Residential

7.22 7.22

014 015 016 017

1.4

SCORE

Climate Activist

001 002 004 005 009 011 013

Average of 2.1

0.00

O RE SC

DEVELOPER

Retiree

26.67

RETIREE

Developer

SINGLE PARENT

single

STUDENT

74 /102

1.2

5 STUDENT

STUDENT

SINGLE PARENT 3

RETIREE DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

2.2 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 2.2 BY SCENARIO, OCCUPATION & AGE

RULE: Maximised vehicle free zones x>80%

80%>x>60%

Average of 2.2 10

60%>x>40%

40%>x

ACADEMIC

OCCUPATION HIGHEST

SINGLE PARENT

O RE SC

001 002 003 005 006 009 011 015 016 018

1.4

Average of 2.2

HIGHEST SCORING SCENARIO: Increase in Pedestrianised Zones

SCORE 15

0.00

8.44 8.44 10

0.00

10.00

10.00SCORE AVERAGE

5 CROSS REFERENCE OF AVERAGE SCORE FOR 2.2 BY LIVING STATUS AND OCCUPATION

Average of 2.2 by Occupation and Living Status 16

OCCUPATION

ACADEMIC

SCENARIO

Student 0

0 Developer

0 0 12

AGE

Climate Activist

DEVELOPER

RETIREE

Retiree

ACADEMIC

OCCUPATION

Severe Demographic Change to Residential

35 - 44

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 8.4

45 - 54

Site Becomes a Business District

265+ 5

Increase in Pedestrianised Zones

25 - 34 with family

single

SINGLE PARENT

Developer

CLIMATE ACTIVIST

Severe Demographic Change to Education

Developers had higher numbers of roads, perhaps this was associated with denser levels of construction.

25.

LOWEST SCORING OCCUPATION: Developer

TOTAL POSSIBLE SCORE: 10

18 - 24

STUDENT

higher numbers of roads as part of this.

SCENARIO

single

Retiree

25.

LOWEST SCORING SCENARIO: Construction Increase, High Rise Residential 5 also implemented Players playing for increased construction

SCENARIO

DEVELOPER

2 1

RETIREE

18 0

with family

10 Climate Activist

3 with friends

9 0

CLIMATE ACTIVIST 15

By average score Single Parent players scored highest, but all occupations bar Developers had high scores.

SINGLE PARENT

with partner

with friends

18 0

Student

DEVELOPER

Single Parent

STUDENT

5. SINGLE PARENT 2Academic 25

RETIREE

with partner

STUDENT

Single Parent

HIGHEST SCORING OCCUPATION: Single Parents

Average of 2.2 by Occupation and Living Status 0 Academic

As might be expected, players in Scenario 5 valued vehicle free zones most. Also high scoring was Scenario 1: Severe Demographic Change to Education.

MEDIAN SCORE: 10

2.2

75 /102

0 5 STUDENT

STUDENT

SINGLE PARENT 3

RETIREE

MATE ACTIVIST

RETIREE

SCENARIO

2.3 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 2.3 BY SCENARIO, OCCUPATION & AGE

RULE: Distance to neighbourhood green spaces not to exceed 400m 400>x

ACADEMIC

OCCUPATION HIGHEST

SINGLE PARENT DEVELOPER

1.3

O RE SC

006 010 011 012 015 016 017

x>400

Average of 2.3

1.4

Average of 2.3

HIGHEST SCORING SCENARIO: Construction Increase Change to High Rise Residential

SCORE 15

0.00

9.17 9.17

0.00

10.00

AVERAGE SCORE 10.00

5 CROSS REFERENCE OF AVERAGE SCORE FOR 2.3 BY LIVING STATUS AND OCCUPATION

Average of 2.3 by Occupation and Living Status

.67

Student

Single Parent

DEVELOPER

.67

0 14

Student Climate Activist

CLIMATE ACTIVIST ACADEMIC

OCCUPATION

LOWEST SCORING AGE GROUP: 18 - 24 The players with the lowest scores had in common that they were both in the 18 - 24 age group.

SCENARIO

TOTAL POSSIBLE SCORE: 10

Severe Demographic Change to Residential

35 - 44

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 9.2

45 - 54

Site Becomes a Business District

65+

Increase in Pedestrianised Zones

18 - 24

25 - 34

with family

single

STUDENT

The players with the lowest scores were both playing in scenarios focusing on an increase in residential typologies.

DEVELOPER

with friends

AGE

single 29 0

RETIREE

67 10.

29 0

Climate Activist

LOWEST SCORING SCENARIO: Severe Demographic Change to Residential, Change to High Rise Residential

RETIREE

Retiree

SCENARIO

0 with family

The majority of players scored well in this metric, this may change if the site was larger, providing more variance in maximum distances.

SINGLE PARENT

with partner

with friends

0 14

Single Parent

SINGLE PARENT

STUDENT

6 10.

OCCUPATION

Developer Retiree

CLIMATE ACTIVIST 15 ACADEMIC

DEVELOPER

RETIREE

SINGLE PARENT Academic

Developer

HIGHEST SCORING OCCUPATION: ALL

with partner

STUDENT

Average of 2.3 by Occupation and Living Status 0 Academic

Players saw need to compensate for high density areas, and placed importance on having more public space to balance the higher population of residents.

Severe Demographic Change to Education

MEDIAN SCORE: 10

2.3

76 /102

NO.: 010 SCENARIO: 2 OCCUPATION: RETIREE AGE: 65+

77 /102 LIVING STATUS: WITH PARTNER

OVERVIEW OF WALKABILITY METRIC SCORES Average Score of Metric 2 by Scenario 30

0 Scenario

5 5505 0

5 0 Scenario

0 00 0

3.2

150>x>130

130>x>110

60%>x>40%

10 10

0 00 0

Scenario Scenario Scenario Scenario

Academic

2 2 1 1 Scenario

Average of 2.2

2.2

2.3

Average of 2.3

Developer

Academic Academic 1

Academic Academic Academic

Single Parent Developer Single Parent Developer Developer

wi...

Academic Academic 4 Academic 4 Academic

wi...

wi... Developer Developer Developer Developer

Scenario Scenario Scenario Scenario 3 3 2

Average of 2.1 by 4 Age 44 4

Student

65+

5 55 5

3 3 33 3

333

Student

2 3

2 21

65+ Student

5.00

45-54

10.00 Single Parent

2 22 2

Student Student

Retiree

6.50

Developer Developer

45-54 7.00 Single Single Parent Parent Single Parent Student Student Student Single Parent

Student

35-44 5.00 Average of 1.2 by Age Average of 1.2 by Age

35-44

65+

40%>x

25-34

18-24

3.4

100%

65+

AGE

100%

x>2.0

2.0>x>1.5

1.5>x>1.0

1.0>x

100%

Average of 3.1 by Age 65+ 65+65+65+ AVERAGE SCORE FOR 3.1 BY65+ AGE 65+Age5.00 65+ 5.00 5.00 Average of 3.1 by 45-54 100% Average of 3.1 bybyAge Age Average of3.1 3.1 Age Average of by 45-54 45-54 45-54

45-54

5.00 45-54

5.00

100% 100% 100% 100% 45-54 45-54 35-44 35-44 5.00 35-44 35-44

35-44

5.00 35-44

5.00

25-34 25-34 25-34 25-34 35-44 5.00 35-44

65+

65+ 65+ 65+ 65+ 45-54

5.00

5.00 5.00 5.0018-24 5.00 5.00 18-24 18-24 18-24

100%

Climate Act...Act... Climate Retiree

wi... wi... wi... wi...

si..s

100%

Retiree Retiree

65+ Retiree with partner with partner 45-54 Retiree Retiree Retiree Retiree

7.00

10.00

with partner with partner with partner with with partner partner

10.00

35-44

10.00

25-34

100% 100% 10.00

35-44 3.75 3.756.256.25 5.00 65+ 5.00

25-34 25-34 25-34 25-34 35-44 5.00

0.0035-44 35-44 5.00 5.000.00 0.50

25-34 25-34 35-44 0.50 25-34 25-34 0.50

3.25 3.25 35-44 6.256.25

25-34 25-34 25-34 25-34 0.00 35-44 0.00

0.00 0.00 0.00 1.251.25

25-34 5.00

5.00 5.002.222.2225-34 25-34

18-24 25-34 3.25 3.2518-24

25-341.111.11 0.00

18-24 5.00

18-24 100% 100% 1.78 18-24

1.78

65+ 65+ 65+ 45-54 4.444.44

3.33

18-24 3.33

18-24

18-24 66.7% 44.4% 5.00 66.7% 5.00

3.33 3.33 3.33 3.33

66.7%

66.7% 66.7% 66.7% 66.7%

100%

5.00 0.00 0.00 2.502.50

3.33 18-24

18-24 18-24 18-24 18-24

100%

5.00 45-54 5.00 35-44 35-44 35-44 35-44

18-24

66.7% 3.75 3.75 3.75 3.75 3.33

100%

with partner with partner with partner with partner with partner

Single Parent Single Parent Single Parent Single Parent 18-24 8.67 65+ 65+ 0.50 0.5012.50 65+65+ 12.50

5.005.00

65+

3.33 25-34 5.00 3.33

18-24

100%

100% 100% 45-54 1.00 100% 45-54 1.00 35-44 35-44 0.50 0.50 35-44 35-44

25-34

5.00 5.00 5.00 5.00 3.75

100%

100% 100% 100% 100% 10.00

100% 100% 45-54 0.00 100% 45-54 35-44 35-44 5.00 0.0045-54 35-44 35-44 5.00 5.00 1.00

25-34 3.75

35-44 35-44 35-44 35-44 25-34

25-34

45-54 5.00 5.00 5.00 5.002.502.50

3.75

5.00 5.00 5.00 5.00 5.00

100%

100% 100% 10.00

18-24 18-24 6.67 8.33 65+65+ Average of 3.2 Age Average of 3.3 by Age Average of 0.00 3.4 by Age 65+ 65+by 65+ 65+ 5.00 5.000.000.00 0.00 65+ 65+ 5.00 5.00 AVERAGE SCORE FOR 3.2 BY AGE AVERAGE SCORE FOR 3.3 BY AGE AVERAGE SCORE FOR 3.4 BY AGE 65+ 65+ 65+ 65+ 65+ 5.00 0.50 5.00 Average 5.00 0.50 0.50 ofof ofof 3.2 by Age Average 3.3 by Age 0.00 0.00 65+ Average 0.00 ofof 100% 65+ 100% 3.4 by Age Average of 3.2 by Age Average of 3.3 by Age 45-54 Average of 3.2 by Age Average of 3.3 by Age 45-54 Average 3.2 by Age Average 3.3 by Age Average of 3.4 by Age 5.00 10.00 5.00 10.00 Average of 3.4 by Age Average 3.4 by Age 45-54 45-54 45-54 45-54 45-54 45-54 5.00 5.00 1.00 1.0015.00 5.00 5.00 45-54 45-54 5.00 5.00 45-54 45-54 133.3% 133.3% 83.3% 15.00 0.00 0.00

3.75 25-34

45-54 45-54 45-54 45-54 35-44

25-34

10.00 5.00 5.0010.00

25-34

25-34 66.7% 25-34 25-34 66.7% 25-34

RULE: Ratio of 1:6 places of work to residential blocks

100% 100%

5.00

45-54 45-54 45-54 45-54 35-44 44.4% 35-44 35-44 35-44 35-44 25-34

5.00

5.00 5.00 5.00 5.00

18-24 18-24 5.00 18-24 18-24

5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00

100%25-34 25-34 25-34100% 25-34

100% 5.00 5.00 5.00 5.00

18-24 18-24 18-24 18-24

5.00 5.00 5.00 5.00

18-24

5.00

100%

100% 100% 100% 100%

65+

3.25

65+ 65+ 65+ 65+ 45-54 18-24 18-24 45-54 45-54 45-54 45-54 35-44 35-44 35-44 35-44 35-44 25-34

355.6% 25-34 355.6% 25-34 25-34 25-34

18-24

18-24 18-24 18-24 18-24

0.50

0.50 0.50 0.50 0.50 1.00 1.78 1.78 1.00 1.00 1.00 1.00 0.50

65+

65+ 65+ 65+ 65+

18-24 18-2445-54 18-24 25-34 18-24

0.00

45-54 45-54 45-54 45-54 35-44

355.6% 18-24 1.78 355.6% 18-24 8.9%8.9% 0.89 18-24

0.50 0.50 0.50 0.50 3.25

355.6% 3.25 3.25 3.25 3.25

1.78

1.78 1.78 1.78 1.78

355.6%

with

si... si... single single

Average of 2.3 by Age

100% Retiree

Student Student

Student

si...

wi...

Average of 2.2 by Age

100%

si...

wi... wi... with friends with friends Student

5 55 5

Climate Activist

Developer

with family with family with family with family with family with family

Climate Activist Climate Activist Climate Activist Climate Activist

Developer

4 1 11 1

Climate Climate Activist Activist Retiree Retiree

Academic

5 5 55

Scenario

00 0 0

Scenario

Academic0

Average of 1.1 by Age Average of 1.1 by Age

110m>x

3.3RULE: Maximised ratio of public:private 35-44 space 80%>x>60%

Average of 2.1

Average Average ofof3.2 3.2by byAge AgeAge Average Average ofof 3.3 Age Age Average of of3.1 3.1 byof byAge Age Average Average 3.4 3.41.4 by by1.4 Age Age Average of3.3 1.3 by Age Average of by Age Average ofbyby 1.3 by Age Average of by Age Average of 3.2 by Age Average of 3.3 by Age of 3.2 Average by Age 3.2 by Age Average of 3.3 Average by of 3.3 by Age Average by AgeAverage Average ofofof3.4 by Age Average of 3.1 Average by Ageof 3.1 by Ageof 3.1Average Average of 3.4 Average by Ageof 3.4 by Age 100% 100% Single Parent

45-54

x>80%

RULE: Max. 150m distance from residential to urban green spaces x>150m

Average of 2.3

Average Score of Metric 2

Average of 1.1of 1.1Average of 1.2of 1.2Average of 1.3of 1.3 Average Average Average

Average of of ... ...... ... Average of of ... ...... ... Average of of ...ofof Average Average Average Average Average Average Average Average Average ...... ... 1 ofof 1 ofof

1010 15 15

1010 1010

Average Score of Metric 1

x>400

Average Score of Metric 1

Average Score of Metric 3 Average Score of Metric 3

3.1 RULE: 400m distance to local amenities

Average of 2.2

2.1

Scenario Average of 3.3 and Average of 3.4 Average of 3.3 by Occupation Average Score ofof Metric 3Average Average 3.1, Average of 3.2, Average ofAverage 3.1, of 3.2, Average 3.4 and 3.1, 3.2, 3.3 and 3.4 by Living Average Average Score Score ofScore ofMetric Metric 3Metric 3byby1 of Average Average of 3.1, Average Average of of3.2, 3.2, Average ofAverage of3.1, 3.1, Average Average of of3.2, 3.2, Average Average ofof of3.4 3.4 and and 3.1, 3.1, 3.2, 3.2, 3.3 3.3 and and 3.4 3.4 by byand Living Living Status Status Average Score of Metric 33Average by Average of 3.1, Average of 3.2, Average of 3.1, Average of 3.2, Average of 3.4 and 3.1, 3.2, 3.3 and 3.4 by Living Status Average Score ofMetric Metric by Average of 3.1, Average of 3.2, Average of 3.1, Average of 3.2, Average of 3.4 and 3.1, 3.2, 3.3 3.4 byAverage Living Status 3.1 3.2 3.3 3.4 Retiree Single Parent Average Score 3by by Average 3.1, Average of 3.2, Average of 3.1, Average of Average of 3.4 and 3.1, 3.2, 3.3 and by Living Status Score ofAverage Metric by1of Average ofof3.1, 1.1, Average of 1.2, of 1.1, Average of3.2, 1.2, Average of 1.3 and Average of 1.1, Average of3.4 1.2, Average ofStatus 1.3 andand Average of by Average of 1.1, Average of 1.2, Average Average of 1.1, Average of 1.2, Average of 1.3 and Average of 1.1, Average of 1.2, of 1.3 with family with family with family Average of ... Average of ... Average of ... Average of ... of ... of ... Average of ... Scenario Scenario Average Average of of 3.3 3.3 and and Average Average of of 3.4 3.4 Average Average of of 3.3 3.3 by by Occupation Occupation Climate Activist Climate Activist Climate Activist by Scenario Scenario ofof 3.3 and Average of 3.4 Average of 3.3 by Occupation 3.1 3.1 AVERAGE 3.23.2 3.3 3.3 1.4 3.4 3.4Living Scenario Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of 1.4 by Status Scenario Average Average of 1.3 and Average of 1.4 by Average of 1.4 by Occupation Average of by Living Status Scenario Average of 3.3 and Average of 3.4 Average of 3.3 by Occupation Scenario Average of3.3 3.3 and Average of 3.4 Average of 3.3 by Occupation Average of 3.1 Average of 3.2 AverageSCORES of 3.4 Average of 3.3 AVERAGE OF METRIC SCORES BY AVERAGE OF METRIC BY OF METRIC SCORES BY Scenario Average and Average of 3.4 Average of 3.3 by Occupation 3.13.1 10 3.13.13.23.2 3.23.23.33.3 3.33.33.43.4 3.43.4 by bySCENARIO Scenario Scenario 5 3.1 2 Average Average ofof 3.1 Average Average ofof 3.23.2 Average Average ofof 3.43.4 Average Average ofof 3.33.3 Scenario OCCUPATION LIVING STATUS Scenario 20 20 with family byby Scenario 10 by Scenario Average of 1.1 Average of 1.2 Average of 1.3 Average of 1.4 Average of 1.1 Average of 1.2 Average of 1.3of 1.3Average of 1.4 by Scenario Average of ... Average of ... Average of ... Average of 1.1 Average of 1.2 Average of 1.3 Average of 1.4 Average of 1.1 Average of 1.2 Average Average of 1.4 Scenario Average Average of 3.1 of of 3.2 of 3.4 Average of of 3.3 Climate Activist Average ofof 3.13.1Average Average ofof 3.23.2Average Average ofof 3.43.4 Average ofof 3.33.3 wi... Average Average Average Average with with family family Average 3.1 Average 3.2 Average of 3.4 Average 3.3 Average ofof ... ... Average Average ofof ... ... Average Average of of ... ... of

Average Score of Metric 3 Average of 33 Average Score Score of Metric Metric Average Average Score ofScore Metric Average Score of Metric 3 of 3Metric 3

MEDIAN SCORE: 11

Average Score of Metric 3

AVERAGE SCORE: 12

O RE SC

Average of 2.1

2.1, 2.2 and 2.3 b

Average Score of Metric by Average Average ofAverage 3.1, Average ofAverage 3.2, of 3.1, Average of 3.1, Average 3.2, Average of3.4 3.4by and 3.1,3.4 3.2, 3.3Living and 3.4 by Living Status Average Score Average of Metric Score 3 by of Metric 3Average by of 33.1, Average of 3.1, of 3.2, Average of 3.2, of 3.1, Average of 3.2, Average Average of 3.2, of 3.4 Average andof3.1, of 3.2, 3.4 and 3.3 and 3.1, 3.2, 3.3 Living and Status by Status Academic Scenario Average ofAverage 3.3 and Average 3.4 Average of 3.3 by Occupation Scenario Scenario SCENARIO Average of 3.3 Average and Average of 3.3 and of 3.4 Average of 3.4 of 3.3 Average by of Occupation of 3.3 by Occupation 3.1 of3.2 3.13.4 3.2 of3.3 ATTRIBUTES Average Score of Metric 3 by Average of 3.1, Average of 3.2, Average of 3.1, Average 3.2, 3.3 Average 3.43.13.4 and3.23.1,3.33.2,3.43.3 and 3.4 by Living Status 20 PLAYER 13 AVERAGE OF METRIC by Scenario by Scenario by Scenario 3 017 4 CLUSTER BY SCENARIO Averageofof3.2 3.1 Average Averageofof3.4 3.2 Average Averageofof3.3 3.4 Average of 3.3 Average of 3.1 Average of 3.1 3.2 Average 3.4 3.3 Average Score of Metric 3

HIGHEST

TOTAL POSSIBLE SCORE: 20

400>x

Average of 2.1, Average of 2.2 and Average of 2.1, Average of 2.2 and Average of 2.3 by Average of 2.3 by Scenario Occupation

0.89

35-44 35-44 35-44 35-44 25-34 25-34 25-34 25-34 25-34 18-24 18-24 18-24 18-24 18-24

0.00

0.00 0.00 0.00 0.00 5.00 0.89 0.89 3.89 0.00 3.89

5.00 5.00 5.00 5.00 0.00

77.8% 0.89 77.8%

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.89 0.89 0.89 0.89 0.89

355.6% 355.6% 355.6% 355.6%

78 /102

0 5

STUDENT

SINGLE PARENT 3

RETIREE DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

3.1 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

ACADEMIC

SCORE FOR 3.1 BY SCENARIO, OCCUPATION & AGE

RULE: 400m distance to local amenities x>400

400>x

Average of 3.1

5.00

AVERAGE SCORE 5.00

Average of 3.1 by Occupation and Living Status

5.67

Developer 9 Retiree

Retiree 9.4

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 3.9

Site Becomes a Business District

Increase in Pedestrianised Zones

35 - 44

3.1

MEDIAN SCORE: 5

.75

2 9.4

Student

DEVELOPER

OCCUPATION

Severe Demographic Change to Residential

Severe Demographic Change to Education

All players who did not score highly in this metric were in the 18 - 34 age groups.

25 - 34

65+

LOWEST SCORING AGE GROUP: 34 and under

TOTAL POSSIBLE SCORE: 5

with family

ACADEMIC

Scenario 3 had the highest number of50 scores, suggesting that for these players small scale ammenities are less valued in high rise neighbourhoods. 4

SCENARIO

18 - 24

45 - 54

STUDENT RETIREE

0AGE

single Academic

Single Parent

CLIMATE ACTIVIST

LOWEST SCORING SCENARIO: Construction Increase, Change to High Rise Residential (3)

SCENARIO

DEVELOPER

5.67

.75

SINGLE PARENT

Student

RETIREE

with friends

SCENARIO

with family

Having amenities within a convenient distance is valued by players of all ages. This metric may be better tested on a larger site to see if this remains true.

SINGLE PARENT

with partner with friends

HIGHEST SCORING AGE GROUP: All

STUDENT

Developer

with partner

Climate Activist 9

On average players in scenarios that did not prioritise residential typologies valued convenient amenities most.

Average of 3.1 by Occupation and Living Status

Climate Activist

SCORE

CROSS REFERENCE OF AVERAGE SCORE FOR 3.1 BY LIVING STATUS AND OCCUPATION

Single Parent

012 013 014 015 016 017 018

HIGHEST SCORING SCENARIO: Severe Demographic Change to Education (1), Site Becomes Business District (4)

0.00

004 005 006 007 009 010 011

1.4

3.89 3.89

O RE SC

Average of 3.1

0.00

OCCUPATION HIGHEST

SINGLE PARENT

single Academic

79 /102

0 5

STUDENT

SINGLE PARENT 3

RETIREE DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

3.2 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 3.2 BY SCENARIO, OCCUPATION & AGE

RULE: Max. 150m distance from residential to urban green spaces x>150m

150>x>130

130>x>110

110m>x

Average of 3.2 5

ACADEMIC

OCCUPATION HIGHEST

SINGLE PARENT

O RE SC

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018

1.4

Average of 3.2 HIGHEST SCORING PLAYERS: All

SCORE 15

5.00 5.00

10 0.00

0.00

5.00

AVERAGE 5.00SCORE

5 CROSS REFERENCE OF AVERAGE SCORE FOR 3.2 BY LIVING STATUS AND OCCUPATION Average of 3.2 by Occupation and Living Status 0

0 14

Single Parent

5 0

single

Student

OCCUPATION

Severe Demographic Change to Residential

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 5

Site Becomes a Business District

Increase in Pedestrianised Zones

35 - 44

Single Parent

ACADEMIC

TOTAL POSSIBLE SCORE: 5

25 - 34

65+

CLIMATE ACTIVIST

SCENARIO

with family

SCENARIO

DEVELOPER

2 1

18 - 24

45 - 54

RETIREE

single

SINGLE PARENT

STUDENT

9 0

Retiree

9 AGE 0

Student

with family

RETIREE

with friends

Developer 0

SCENARIO

Retiree

OCCUPATION

ACADEMIC

Climate Activist

SINGLE PARENT

with partner

with friends

CLIMATE ACTIVIST 15

DEVELOPER

Developer

DEVELOPER

Academic 0

STUDENT

SINGLE PARENT RETIREE

with partner

STUDENT

Climate Activist

It could be that this metric needed to be more complex to derive more varied responses, in terms of how close players would ideally have an urban green space to their home.

Average of 3.2 by Occupation and Living Status

Academic

All players scored full marks on this metric, suggesting that all players value having green space near their homes, or near residential areas in a neighbourhood.

Severe Demographic Change to Education

3.2

MEDIAN SCORE: 5

80/102

0 5

STUDENT

SINGLE PARENT 3

RETIREE DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

3.3 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 3.3 BY SCENARIO, OCCUPATION & AGE

RULE: Maximised ratio of public:private space x>80%

80%>x>60%

60%>x>40%

40%>x

Average of 3.3 5

ACADEMIC

OCCUPATION HIGHEST

SINGLE PARENT

O RE SC

001 002 007 009

1.4

Average of 3.3

HIGHEST SCORING SCENARIO: Construction Increase, High Rise Residential (3)

SCORE 15

1.78

1.78 10

0.00

5.00

AVERAGE 5.00SCORE

5 CROSS REFERENCE OF AVERAGE SCORE FOR 3.3 BY LIVING STATUS AND OCCUPATION Average of 3.3 by Occupation and Living Status 0

0 0.15 0.3 0.4 0. 65 0. 75 0 .9 0

Academic

5.3

DEVELOPER CLIMATE ACTIVIST ACADEMIC

OCCUPATION

Climate Activist

with family

Severe Demographic Change to Residential

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 1.8

45 - 54

Site Becomes a Business District

65+

Increase in Pedestrianised Zones

00 0 0.7.9 0.0.14..3565 05 00.07.59 . 00.456 .3 0.1 5 0 0.9 0.75

00 0 0.7.9 0.0.14..3565 05 0 0 .9 00.0.47.565 .5 3 0.1 0 0.9 0.75

single 5.33

Developer

with family

Severe Demographic Change to Education

3.3

MEDIAN SCORE: 1

0 15 0. 0.3 0.45 0.6

RETIREE

Retiree

OCCUPATION

Retiree players scored lowest on average, this could be due to focusing more on residential typologies which count as private land. TOTAL POSSIBLE SCORE: 5

335 - 44

single 5.33

STUDENT

ACADEMIC

LOWEST SCORING SCENARIO: Site Becomes Business District (4) 5 Players in scenario 4 did not prioritise public access land. This could be because places of business are not always 4 seen as areas to walk around, rather a destination for a sole purpose. 3 LOWEST SCORING OCCUPATION: Retiree

SCENARIO

25 - 34

Climate Activist

SINGLE PARENT

AGE

CLIMATE ACTIVIST

18 - 24

Younger players valued public space more, this could be because young people are more active and more likely to utilise public space for exercise or entertainment.

SCENARIO

DEVELOPER

0 5 0.1 0.3 0.45 0.6

Developer

RETIREE

DEVELOPER

5.3

Student

SCENARIO

SINGLE PARENT

with friends

0 Student

with partner

STUDENT

0 0.15 0.3 0. 45 0. 6 0 .7 5 0.9 0

RETIREE

with partner with friends

SINGLE PARENT

HIGHEST SCORING AGE GROUP: Under 34s

Average of 3.3 by Occupation and Living Status

STUDENT Academic

Players focusing on high rise residential valued public space in their neighbourhood, potentially to compensate for the high density of residents.

Retiree

81 /102

0 5

STUDENT

SINGLE PARENT 3

RETIREE DEVELOPER

MATE ACTIVIST

RETIREE

SCENARIO

3.4 OCCUPATION

DEVELOPER

CLIMATE ACTIVIST

SCORE FOR 3.4 BY SCENARIO, OCCUPATION & AGE

RULE: Ratio of 1:6 places of work to residential blocks x>2.0

2.0>x>1.5

1.5>x>1.0

1.0>x

Average of 3.4

ACADEMIC

OCCUPATION HIGHEST

SINGLE PARENT

HIGHEST SCORING SCENARIO: Construction Increase Change to High Rise Residential (3)

SCORE 15

0.72

0.72 10

0.00

5.00

AVERAGE SCORE 5.00

5 CROSS REFERENCE OF AVERAGE SCORE FOR 3.4 BY LIVING STATUS AND OCCUPATION Average of 3.4 by0 Occupation and Living Status

ACADEMIC

STUDENT

0 0 5 0..1 0 3 0 .45 0..765 0.9

SCENARIO

Developer

OCCUPATION 15

HIGHEST SCORING OCCUPATION: Students, Developer Developer players may have valued places of work in a high density residential neighbourhood to make the neighbourhood more attractive to prospective occupants.

LOWEST SCORING SCENARIO: Severe Change Residential (2), Site Becomes a Business District (4)

In Scenario 4 players may not have scored well in this metric due to housing being valued less in a business district.

SINGLE PARENT

with partner

Developer

DEVELOPER CLIMATE ACTIVIST

with partner

SINGLE PARENT

0 0.15 0.3 0.4 0 5 0 .6 0..975

STUDENT

In terms of walkability, it is logical that a high density residential area would have more places of work nearby, as there would be high numbers of people needing work.

Average of 3.4 by Occupation and Living Status

RETIREE

012 017

1.4

Average of 3.4

0.00

O RE SC

RETIREE DEVELOPER

with family

CLIMATE ACTIVIST

ACADEMIC

OCCUPATION

LOWEST SCORING OCCUPATION: Academic, Retiree, Single Parent These player profiles likely value other typologies over places of work due to their occupations.

10 Student

0 0 0.70.9 5 0.04.56 0.01.53 0

0.6

Student

1.33

Retiree

.17

AGE

4.5

single Single Parent

with family

TOTAL POSSIBLE SCORE: 5

Severe Demographic Change to Residential

Construction Increase, Change to High Rise Residential

AVERAGE SCORE: 0.7

Site Becomes a Business District

Increase in Pedestrianised Zones

25 - 34 35 - 44

4.5 45 - 54 65+

1.1

SCENARIO

18 - 24

Severe Demographic Change to Education

3.4

MEDIAN SCORE: 0

0.70.9 5 0.04.56 0 0.1.53 0

DEVELOPER

0.6

RETIREE

Retiree

1.33

SINGLE PARENT

STUDENT

single Single Parent

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NO.: 017 SCENARIO: 3 OCCUPATION: DEVELOPER AGE: 45-54 LIVING STATUS: WITH FAMILY

COMPARING RESULTS AND RESEARCH

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SCOREBOARD

HIGHEST SCORING SCENARIO Site Becomes Business District

HIGHEST SCORING AGE GROUP 45 - 54

HIGHEST SCORING OCCUPATION Climate Activist

HIGHEST SCORING LIVING STATUS With Family

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PUBLIC SPACE EVERY 100m

CLOSER DISTANCE TO WORK

COMMUNITY FAMILY

HIGHEST SCORING OCCUPATION: RETIREE HIGHEST SCORE BY LIVING STATUS: WITH PARTNER

WORK

“The balance of activity to living spaces, i.e. ensuring there is a balance of social amenities and ensuring it is a walkable distance.”

KEY

“Safety within the community; avoid creating dark spaces and streets.” PILLARS OF HAPPINESS

Older generations may feel less safe on longer, uninterrupted streets. Younger people are more capable and willing to walk further.

COMMERCIAL IN PEDESTRIAN SCENARIO

Overall, players did not score highly, suggesting what the players value does not reflect the research.

PILLARS OF HAPPINESS

EVALUATION

Players placed more commercial typologies in areas of high density residential. From a business perspective, this increase could be a result of accommodating for more people.

18-24

PILLARS OF HAPPINESS

“Knowing your neighbour.”

RESEARCH

LIVING

This suggests older communities prefer to have work and living separate.

HIGHEST SCORE BY AGE: 45+

OBSERVATION

This is more evident in the older communities.

25+

HIGHEST SCORING SCENARIO: Change to residential [2]

100m

25+

18-24

EVALUATION AGAINST THE RESEARCH OF HAPPINESS IN CITIES THROUGH THE PILLARS OF HAPPINESS

SCORE BOARD

COMMERCIAL TO HIGH RISE RESIDENTIAL

*High Rise typologies placed would not score for the “10-15 houses per micro network” metric because it measured community within low rise housing typologies.

Players used more high rise than low rise in Scenario 5 to compensate for more public spaces.

Commercial spaces and frequent, smaller public spaces were not favoured in a pedestrianised scenario. Players valued the larger public spaces instead.

The opposite to the high-density/more amenities, more low density residential was associated with reduced local commercial. It could be suggested the increased likelihood of vehicle ownership in low-density residential areas results in trips to larger commercial units.

“Amenities in the public space provide convenience but also interesting interactions.”

“Regular visits by people creates familiarity.” SUCCESSFUL PUBLIC SPACE RESEARCH

Local people in the higher density areas are more likely to go to their local amenity stores for convenience, increasing familiarity and creating small connections between people.

Create connections through familiar interactions at commercial/amenity stores, because act as a public social space.

SUCCESSFUL PUBLIC SPACE RESEARCH

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w DEVELOPER VALUES

Developers valued public space least, including valuing vehicle free zones least, contradicting the rest of the players.

PUBLIC SPACES EVALUATION AGAINST THE RESEARCH OF HAPPINESS IN CITIES THROUGH THE PILLARS OF HAPPINESS +

“To aid in improving the neighbourhood health, creating more vehicle free zone and prioritising cycles and walking as the main modes of transport.”

HIGHEST SCORING SCENARIO: change to business district [4] HIGHEST SCORING OCCUPATION: ACADEMIC/SINGLE PARENT HIGHEST SCORE BY LIVING STATUS: WITH FAMILY HIGHEST SCORE BY AGE: 25-34

KEY

OBSERVATION

A developer aims to accommodate for residents while reaching maximum capacity possible for financial opportunities. Including more vehicle free spaces could result in less dense neighbourhoods, which could result in a decline for the developers.

Caveat: for the public space being within 400m of a residence all players scored well, but the site was not much larger so it could be argued this was easy to achieve. We propose this should be tested on a larger scale site to see how the scores are affected. However, on average the players placed the macro public spaces within 66.6m of a residence, ranging from 39m - 159m. This is significantly lower than the suggested 400m, suggesting that residents of the neighbourhood would be happier if they had public spaces closer to where they live.

“Seeing other people regularly, creating familiarity amongst people.” SUCCESSFUL PUBLIC SPACE RESEARCH

RESEARCH EVALUATION

PUBLIC SPACE PER PERSON

9m2

“Accommodating different activities for varied experiences.” SUCCESSFUL PUBLIC SPACE RESEARCH

“Active public spaces, sports facilities and cycle lanes.”

PILLARS OF HAPPINESS

SCORE BOARD

No one reached the suggested value of 9m2 of public space per person.

HIGH DENSITY VALUE PUBLIC SPACE

SUCCESSFUL PUBLIC SPACE RESEARCH

Although no one achieved the recommended amount, the average score was over half the total points showing public space was highly valued.

PUBLIC SPACE VALUE

Public space demand was highest for high density areas. This could be due to the close proximity to one another and the increase in residents. Public spaces give the residents an alternative place to escape to than the confinement of their apartments, as not all residents will have access to balconies. This is a positive response to the wellbeing of the people.

DISTANCE TO PUBLIC SPACES

25+

“The balance of activity to living spaces, i.e. ensuring there is a balance of social amenities and ensuring it is a walkable distance.” PILLARS OF HAPPINESS

Similar to when evaluating distance to amenities, younger players valued the immediacy of public space less than other age groups, placing it further away on average.

9m22

“Opportunity for socialising in groups and not just spaces for individuals.”

Players valued the immediacy of public space more than they valued it meeting the suggested area of public space per person.

SUCCESSFUL PUBLIC SPACE RESEARCH

Players favouring more public spaces in closer proximities may result in shortage if the public spaces are not able to accommodate all the residents. Providing spaces for bigger groups than individual spaces may be valued more and can be better from a socialising and comfort aspect.

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AMENITIES IN HIGH DENSITY ZONES

WALKABILITY EVALUATION AGAINST THE RESEARCH OF HAPPINESS IN CITIES THROUGH THE PILLARS OF HAPPINESS

Players using high rise residential typologies valued having local amenities but did not place them as close as suggested by the research.

+ SCORE BOARD

PUBLIC SPACE PLACEMENT

Overall players valued smaller, more frequent pockets of green space being close to their homes, placing larger public space further away.

This could be because residents of high rise apartments are often of a younger demographic, so can more comfortably walk further.

Older players did value having both small and larger scale public space in close proximity to residential areas.

PILLARS OF HAPPINESS

“Pedestrianisation: increased walkable routes.”

“The walkability and the convenience within the neighbourhood ensuring spaces are connected to one another.”

PILLARS OF HAPPINESS

HIGHEST SCORING SCENARIO: change to education [1] + change to highrise [3] HIGHEST SCORING OCCUPATION: RETIREE HIGHEST SCORE BY LIVING STATUS: WITH family HIGHEST SCORE BY AGE: 45-54

“Regular visits by people creates familiarity.”

ALLOCATION OF AMENITIES

This pattern also supports the research from the book Happy Cities (Montgomery, 2015). Where they noted people had positive reactions to public spaces in built up areas.

ALLOCATION OF AMENITIES

< Players valued having small amenities less in areas of low rise residential typologies than high density residential.

This could be due to residents in low rise residential areas typically being more likely to own a car and therefore be more able to travel further to amenities.

Players valued having local amenities in non-residential zones, such as business and education zones, more than in low rise residential zones.

“Amenities in the public space provide convenience but also interesting interactions.” SUCCESSFUL PUBLIC SPACE RESEARCH

KEY

OBSERVATION RESEARCH

“Accommodating different activities for varied experiences.” SUCCESSFUL PUBLIC SPACE RESEARCH

EVALUATION

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URBAN PRIORITIES: CATEGORISING THE PILLARS OF HAPPINESS INTO SPATIAL ELEMENTS

PUBLIC SPACE

PILLARS:

WALKABILITY

PILLARS:

FREEDOM

TOGETHERNESS

FREEDOM

COMMUNITY

PILLARS: TRUST

HEALTH

SAFETY

TOGETHERNESS

TRUST

MONEY

ST VALUED

1st 2nd

3rd

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FREEDOM

CONCLUSIONS TOGETHERNESS

• Players valued public space most, showing higher value placed on the pillar of ‘Freedom’. • Players did not value having their place of work within walkable distance, especially if that meant a higher density of business typologies in the neighbourhood, placing less value in walkability related metrics. • Players did not value metrics associated with community highly. They valued the pillar of ‘togetherness’ in terms of having places to see people and be familiar with people, but not what might be considered more ‘forced’ community. • The values of the Developer profile players contradicted the majority of other players, suggesting that, for residents to be happier, developers should have less influence on design. In this proof of concept players showed that they did not value the signifiers of happiness in the way the research suggested they should, indicating that designers could prioritise these factors differently in order to increase the satisfaction with the neighbourhood.

MONEY

HEALTH

TRUST

SAFETY

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EVALUATING THE METHOD OF THE GAME AND IN PARTICIPATORY PLANNING. DATA •

By using a rule-based approach, we can understand what residents value, and the patterns of what they value can be abstracted to be applied into design.

•

Applying data from top down research may not always work for developing a neighbourhood. The adaptability of the game means that it can get responses for any urban area in order to inform design decisions.

•

Following this proof of concept, we would progress to distribute the game to the residents of Hulme to test the game on the public. This will enable us to analyse real data and see how it compares to our trial, as we would have done had the year proceeded as normal without the Covid-19 lockdown.

PLATFORM •

When played multiple times players gain a better idea of what they value in a neighbourhood, as they see how their choices change their neighbourhood layout, as opposed to making a verbal statement.

•

The virtual platform may give the designers the opportunity to get feedback from a larger audience and enable more open discourse.

DESIGN •

The game produces unbiased information that is not influenced by the presence of an industry professional or the potential concern of giving a socially acceptable answer.

•

As each neighbourhood is different, what may be valued on one neighbourhood may not be valued in others. This brings us back to designing for people, understanding what they value, rather than assuming one size fits all.

•

Our prototype may not fully express what the users want and may not follow the research. Therefore the platform can be used in a broader circumstance to better understand what people value.

•

We should make clear that in using a blank site we are not advocating that the neighbourhood of Hulme should be flattened and rebuilt. The game used a blank site in order to gain totally abstracted information from the players, without the barrier or influence of the existing buildings. The information abstracted regarding what the players valued can then be applied to the design development of any site or master plan within the existing neighbourhood.

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NO.: 000 SCENARIO: 5 OCCUPATION: ACADEMIC AGE: 25-34

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EVALUATING THE GAME

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EVALUATING THE GAME AS A TOOL [CLICK ON THE ICONS]

ACCOMPLISHMENTS

WHAT DID WE LEARN?

WHAT WENT WRONG?

WHAT COULD BE IMPROVED?

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EVALUATING THE EXECUTION RESPONSE TO THE GAME

AND EVALUATING USER AGENTS

EVALUATING DISTRIBUTION TO MSA NETWORK

In light of the current pandemic, the game became a proof of concept as ZJD could not distribute the game to Hulme and get the residents feedback. As a result of this, rather than the responses reflecting the residents of Hulme’s values, it became a prototype where we tested the game as a tool for urban planning.

CLICK ON NUMBER BUTTONS

USER AGENT PROFILES (PLAYING THE GAME OURSELVES)

DISTRIBUTION TO THE SURROUNDING MSA NETWORK

Five agents were created; student, single parent, retiree, climate activist and developer. These were based on the current demographics in Manchester and the potential future scenarios. Each agent was given a characteristic profile which the player used to complete the game.

AMONGST

The methods chosen were the best we could take in the situation, the people approached had the software installed and familiarity with the interface. This however restricted the number of respondents, further scaling down the selected pool of testers. Creating the game on an online platform could have helped players without the software to have access, including getting in touch with the residents of Hulme. However, unknowing he future extent of the crisis, decisions which were made prior have been reflected upon to help us for future developments and distribution.

WAS THIS SUCCESSFUL?

DISADVANTAGES?

POTENTIAL CHANGES

Playing using the agents removed personal bias’ that could have been imposed on the choices made while playing the game. It also diversified the responses by giving alternative perspectives of residents in a city.

Our own design knowledge will have inevitably come through when playing using the agent profiles. As we were the only players using the agent profiles, the outputs benefited from our proficiency with the software and familiarity with the game.

As only five different agents were used to represent the population, it did not cover the whole demographic of the area; there could be potential to explore further using additional agents in this prototype. Introducing these agents within the game for all the players to perform under would allow for a different perspective of the game and not just from the player themselves.

Nevertheless, the back-up methods provided us with a range of responses to what the player valued and aided in our exploration into understanding what they want. This allowed us to test to prototype and explore how it could operate in a planning environment.

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EVALUATING HOW THE GAME CAN SIT WITHIN THE URBAN REALM

“I think that the idea of experimenting with urbanism through games is one good way to assess scenarios of a city/ neighbourhood before taking actions and spending government money on infrastructure that is not needed by many - or simply not used by many. Generally nice idea and I think it should be investigated more by councils.”

ITY RES I

S EN

ITY RES I

LOW

E ITI

CO M

HIGH

We set out to create a game which invited the public to get involved in an interactive way. The process differed from the common methods, such as surveys and consultations as it enabled people in their own time and space to truthfully make decisions without being swayed by voiced opinions and structured conversations.

CIAL ER

Using the game for urban planning provides a forward-thinking and interactive way to get residents involved in the development of their neighbourhood. Bringing a virtual gaming experience, that is familiar to many, to the urban development process, provides a visual understanding and common ground between people. The interface portrayed is a stylised replica of Hulme with a pixel aesthetic. As the goal was to understand what the players value, we produced an abstract context so the players could focus on their own ideas, rather than designing in the context of reality. In addition, as this was restricted to a 2D plan view, the lack of 3D visual elements removed the link to a realistic context. A level of realism through 3D aspects and simulations could provide a better sense of the spaces once the players have completed the game, taking the player from the game to real life.

“The road network I really enjoyed - why? Because it was free hand - I felt like was designing!”

“It felt like the game expected you to know certain rules of master/ urban planning. So if you didn’t it would be hard to play because that is what you’re a marked on. However, at the same time, that’s what made it interesting. I thought it was well put together but my comment would be to have a short description of what each typology is.”

“It was really easy to use, obviously it was computer power that stopped it from being truly smooth but it was simple to follow. Only thing I’d say is that, in a perfect world, the grid orientation would be fixed to a certain number of angles because when I was playing around with it it was making me itch when I made the grid out of place with the context.”

FEEDBACK FROM RESPONDENTS

GAME vs. REALISM

“Thinking from a participant’s point of view, if the participant does not have any software knowledge I am assuming that the input value could not be as helpful. Furthermore, I think parametric master planning has a limitation towards the socio-economic aspect of the area, as sufficient data needs to be gathered and input into the algorithm rather than users’ thought on what they value whether or not its green space or residential building”

“The coordinates are hard to tell since the houses never move where I want them to. It would be better for the general public if its like a little stamp-to-stamp on so at the last bit where you’re supposed to fill in one block by one block it’ll be much easier!”

“The concept of the game is good as it provides a systematic design process with a broad range of options and typologies for the player/ designer’s consideration. The interface was clear and takes the player through the process step by step, only criticism would be the selection/move sliders, it was very difficult to use especially if you’re trying to micro down on the placement... In the end, it got frustrating after placing a few tiles and forced me to proceed to the next step (and repeat it again). Of course, this is a quick prototype and it comes with many improvements, rather than criticism it should be taken as future development. The game was very heavy on my laptop which didn’t let me finish the final step of the game and I didn’t get to see the final output which was a shame.”

VIE

PIX

INT

FA C

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A LOOK AT EXISITING PLATFORMS AND THEIR PLACE WITHIN THE IMMEDIATE FUTURE

CHANGES IN URBAN PLANNING IN RESPONSE TO COVID-19

CASE STUDY

MVRDV - GRASBROOK MAKER: A platform that allows the people in the neighbourhood to explore different possibilities by drawing on their knowledge and experience of the place.

DESIGNERS: Design and test the framework

PARTICIPANTS: (Residents) define and place the public projects

DEVELOPERS: Including the entrepreneurs develop the urban blocks

ACTIVATORS = Special elements developed as a direct response to the site

MVRDV METHOD

SIMULATION = Fast answers

ZJD METHOD

PARTICIPANTS: Place the typologies they need/want for their neighbourhood.

Comparing both games, there are some differences in the method. Although Grasbrook had the same intentions to get the residents involved in what is developed in their neighbourhood, the level of freedom and flexibility within it was in fact limited. The players are restricted to the placement of public projects while other other areas have been outlined prior from the designers, these ideas are placed and tested to see the residents responses through simulation. This brings us back to the fact the residents may not have design knowledge and they are not designing the spaces.

DEVELOPERS & DESIGNERS: Developers extract what the users value in the neighbourhood and create the framework. The designers translate this information when designing the proposals.

This similarity between the games is the importance of the voice of the residents (in game form) and this is taken into consideration in Grasbrook but is the basis for MANC SKYLINES. Our method was to create a game that is more abstract in order to understand the players values rather than actually extract a design from the residents. Within this the users are given more freedom with the type of buildings and the placement.

Mvrdv.nl. 2019. MVRDV - Werkstadt Grasbrook. [online] Available at: <https://www.mvrdv.nl/projects/427/werkstadtgrasbrook> [Accessed 13 May 2020].

In city planning many decisions made are top-down. Due to COVID-19, many urban development decisions previously regarded as standard are now being debated, and ideas for improvement of city planning postlockdown are being explored. The Covid-19 lockdown has massively affected how people live, work and socialise, bringing to light what could be longer term disruptions to how people act in public spaces, and in turn how public spaces are designed.

LACK OF BASIC NEEDS

Poor access to amenities have caused people in lock-downs to struggle. Questions have been raised as to how urban density design can be improved to better accommodate basic the spatial needs that affect the health of the residents.

AFFORDABLE HOUSING AND PUBLIC SPACE

High density urban areas will see problems now more than ever if no proper living standards and public spaces are provided. This includes the way homes are designed. During this crisis working from home has been implemented, which has highlighted the minimal space allocation provided in many living situations.

AVAILABILITY OF GREEN SPACES

In the UK in particular, the rise of foot traffic in parks is highlighting the necessity for exercise and outdoor spaces especially in dense cities. This poses the question of whether people will value green space in cities more highly once the lockdown measures have been lifted.

CITY LEVEL DATA Smart cities are already developing across the world, it is this data being used at local level which can be used to regularly update data streams and provide evidence for decisionmaking. For example, South Korea’s transparency application informing residents of places where they have been infected and alerts are sent out when people approach these spaces. Safe and secure data sharing can help take the right measures and prepare for future challenges.

The event of Covid-19 has highlighted that cities need to be better designed with people in mind, suggesting that current planning methods need to change to incorporate new strategies for design. Understanding what the human requirements are for satisfaction and wellbeing are explored in our game. Through the use of the data that is extracted from the responses, designers can plan neighbourhoods that incorporate the values of the people. The adaptable nature of the game can allow it to be applied to projects varying in both scale and location. At present the game bases the focuses on building and spatial layouts to analyse player values. This could be built upon to add further levels of detail, such as monetary factors.

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REFLECTING ON LONG TERM DEVELOPMENT AND POTENTIAL AREAS OF PROGRESS

FUTURE DEVELOPMENT Currently the game stands at a 2D stylised experience with a linear progress map. It is important for us to understand where the game could improve the user experience and get more valuable responses. Taking on board feedback from the respondents and doing a self reflection, we found there were multiple areas of progression.

ECONOMIC FACTORS IMPROVING ACCESSIBILITY

COMPLEX MECHANICS We could look to explore more complex mechanics within the game to improve the user experience but also add other levels of detail and flexibility. Functions such as: UNDO: this function can allow the user to redo the steps they made DEMOLISH: the option to remove buildings they no longer need STREET VIEW SIMULATION: seeing the choices made at street level can help the users decide if their choices are what they really want them to be. USE OF 3D TYPOLOGIES: to add to the realistic element, being able to see and move the 3D typologies

To make it more accessible for all, moving the platform online could allow players who struggled with software compatibility. In addition, an online platform can reach a wider community.

Including an economic aspect to the game can influence the decision making further, giving a real-life element to what it would mean to incorporate these typologies within the scheme. Your goal could envision a low budget development; the players can see what typologies would benefit them and add to their hierarchy of needs and values.

BUILDING TYPOLOGIES Giving the player the ability to choose more complex typologies. For example, the choice to create dual typologies using the current catalogue of buildings to create a lower ground commercial and upper residential block.* - Potential to incorporate this alongside the levels; the higher you get the more freedom you have with the selection of building

ADAPTABLE GLOBALLY An important goal is the adaptability of the game to future scenarios and other cities.

*It is crucial to remember this would not be giving the players the ability to design the typologies but simply use the existing forms to bring together typologies i.e. to save space for other buildings or create a more active front on ground level etc.

INTRODUCING LEVELS By incorporating levels into the game allows room for further flexible actions to take place. Example: Each level unlocks a new action the player can use, “you’ve reached level 2, you can now demolish buildings” this could increase player interactivity and ambition, like playing a regular game, within this platform to encourage more engagement and detail.

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PLEASE CLICK THE BUTTON BELOW TO VIEW THE VIDEO RENDERED WALKTHROUGH

THE VIDEO CAN ALSO BE FOUND IN THE ADDITIONAL UPLOADS SECTION

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BIBLIOGRAPHY

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BIBLIOGRAPHY

Pg. 10 Aguilar, M., 2015. PARTICIPATORY DESIGN FOR PUBLIC URBAN SPACES. Melbourne: UN Global-Compact Cities Programme. 2015. PARTICIPATORY URBAN PLANNING. Montréal: Montréal Urban Ecology Centre (MUEC). Pg. 86-88 ArchDaily. (2020). What Makes a Great Public Place?. [online] Available at: https:// www.archdaily.com/914616/what-makes-a-great-public-place [Accessed 1 Mar. 2020]. Montgomery, C. (2015). Happy city. London: Penguin Books. Pg. 97 Berg, R., 2020. How Will COVID-19 Affect Urban Planning? |. [online] TheCityFix. Available at: <https://thecityfix.com/blog/will-covid-19-affect-urban-planningrogier-van-den-berg/> [Accessed 13 May 2020]. Kimmelman, M., 2020. Can City Life Survive Coronavirus?. [online] Nytimes. com. Available at: <https://www.nytimes.com/2020/03/17/world/europe/ coronavirus-city-life.html> [Accessed 13 May 2020]. Null, S. and Smith, H., 2020. COVID-19 Could Affect Cities For Years. Here Are 4 Ways They’Re Coping Now. |. [online] TheCityFix. Available at: <https:// thecityfix.com/blog/covid-19-affect-cities-years-4-ways-theyre-coping-nowschuyler-null-hillary-smith/> [Accessed 13 May 2020]. Mvrdv.nl. 2019. MVRDV - Werkstadt Grasbrook. [online] Available at: <https:// www.mvrdv.nl/projects/427/werkstadt-grasbrook> [Accessed 13 May 2020].

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