Decoding Gentrification

Decoding Gentrification Encoding Local Culture in King’s Cross Hao Zhang SN: 20149684 Pingyue Cui SN: 20138203 Zhiyu Liu SN: 19086473

The Bartlett School of Architecture, UCL RC15: Pervasive Urbanism

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification Encoding Local Culture in King’s Cross --RC15: Pervasive Urbanism Reprogramming the Urban Commons --Design Tutors Annarita Papeschi Alican Inal H&T Tutor Ilaria Di Carlo Skills Tutor Vincent Nowak

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Synopsis --In the last ten years, the area of Kings Cross has changed significantly with new and refurbished buildings leading to significant increase in house prices and population density as part of an overall process of gentrification. The study explores the effects of gentrification in the area with a focus on the correlation between increased land value, the dynamics of the cultural infrastructure and the emerging environmental inequalities.

With the aim of gathering granular environmental information, the team assembled an Arduino device, consisting of loudness and particulate matter sensors. This information was collated with housing prices and crime rates to trace correlations between areas of environmental hazard and perceived attractiveness. Further historical studies mapping art studios and exhibition spaces highlighted how artists and cultural events had been used in the area prior to regeneration to heighten desirability for profit purposes.

As a result the proposal explores the idea of reverting the dynamics of how cultural production has been used in the area for land value inflation, using agent-based design methods to strengthen the exiting cultural infrastructure with distributed craft and art production community to enable mechanisms of fairer growth and localized regeneration.

Table of Content --Chapter 01: Introduction Chapter 02: Kings Cross Chapter 03: Local Culture Chapter 04: Environment Data Collection Chapter 05: Datascapes Analysis Chapter 06: Design Methodology Chapter 07: Design Speculation

04 16 52 80 108 128 170

Decoding Gentrification | 3

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Chapter 1 Introduction

Decoding Gentrification | 5

1.1 The Concept of Gentrification The concept of “gentrification,” which can be traced back to sociologist Ruth Glass in 1964, was first used to define a complicated, but distinct pattern of social and spatial reshaping seen in urban areas of London in the 1960s (Yee, J. & Dennett, A., 2020). It related to the phenomenon that the emergence of new middleclass neighbourhoods were gradually displacing preexisting working-class community. Housing prices rose as a result of the redevelopment of aged residential buildings (Lees et al., 2008).

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As a means of neighbourhood transformation that usually seeks to pursue the aesthetic and lifestyle tastes of particular demographic classes (those at the top of the socio-economic pile) at the detriment of others (those at the bottom) (Slater, 2011), the core of gentrification’s divisive and politicised existence is systemically rooted in the stratifications caused by socioeconomic class (SES), ethnicity, and gender inequalities (Lees et al., 2008).

1.2 Index of Gentrification In recent years, there have been further attempts to quantify and visualise the effect of gentrification in urban environment(Trigg, K., 2017). The Center for Community Innovation gathered a wealth of data on neighbourhood incomes, transportation, and utilities in the San Francisco Bay Area, but the findings are tainted by a slew of subjective decisions (Center for Community Innovation 2009). The Voorhees Center used a similar approach in 2014 to reflect Chicago’s socioeconomic transition between 1970 and 2010, generating an index score and a population typology for the neighbourhood (Voorhees Center 2014).

Although the data was correlated in this case, the number of variables was limited, and the emphasis was mostly on demographic data.

London, like San Francisco and Chicago in North America, is often referred to as a “textbook” model of urban gentrification, and is frequently used as a case study area in gentrification studies. Although scholars have attempted to quantify gentrification, there is a lack of a standardised and universal method for assessing the phenomenon(Trigg, K., 2017). The thesis would use London as a test city for the study, as this is where Glass’ gentrification findings were first made and gave a basis for many other studies.

In order to understand the effects of gentrification, an iterative approach is used and an experiment index of London census data derived from London datastore (https:// data.london.gov.uk/) is created with potential to use data available in all cities.

The index of indicators has been filtered based on the key hypotheses of gentrification. For two-time intervals(2001 and 2011), indicators(population, health, education, employment, ethnicity, income and housing) resulted in a comprehensive analysis to determine how a region has changed and if gentrification has happened.

Deprivation

Gentrification

Polarization

Income Deprivation Employment Deprivation Education, Skills and Training Deprivation Health Deprivation and Disability Crime Barriers to Housing and Services Living Environment Deprivation

Population and demographics Household Place of birth Ethnicity Religion Car ownership Density and dwelling Tenure Dwelling type Income and economic activeness Occupation The National Statistics Socio-economic classification Work conditions Qualifications Travel to work House prices and sales Planning permissions Population churn

Income polarization Ethnic polarization Religious polarization Political polarization Public opinion polarization Affective polarization Geographic polarization Residential polarization Intergroup polarization Social polarization Health polarization

The English Indices of Deprivation 2019 (IoD2019) Ministry of Housing Communities & Local Government 26 September 2019

Conceptualizing and Measuring Polarization: a review

Paul C. Bauer, Mannheim Centre for European Social Research (MZES) 12 September 2019

Unpacking the Nuances of London’s Neighbourhood Change & Gentrification Trajectories UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS January 2020

Decoding Gentrification | 7

1.3 Research Scale

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Decoding Gentrification | 8

Decoding Gentrification | 9

1.4 London Census Data

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Decoding Gentrification | 11

Popul

Popu Den (Perso hect

1.4 London Census Data

Hea

Witho limit long-t illne

Educ level

2001

2011

London

London

Census

Census

Employ

Hig mana admini and prof occup

Ethni

Black/Af Caribbea ck Brit Afric

Incom

Data

Data

Total Me Annu Househ Incom estima

Housi Owned:

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Decoding Gentrification | 12

London

Census

Data

Analysis

lation

ulation nsity ons per tare)

Density: higher in city centre and lower in rural area, lower after the decade

alth

out a ting term ess

Health: healthy people are equally spread and there are more after the decade

cation 4/5

Education: more people with high level of qualification in west of city centre

yment

gher agerial istrative fessional pations

Employment: people in higher position are highly relevant to education level

icity

frican/ an/Bla tish: can

Ethnicity: not equally spread and developed marginally

me

edian ual hold me ate

Income: highly relevant to education and employment

ing Total

Housing: basically equally spread, more people in rural area living in their own house Decoding Gentrification | 13

Overlaying different layer of London’s census data to identify a site for research future intervention.

1.5and Gentrification Index

Population 7.1%

Health 7.1%

Education 14.2%

Employment 14.2%

Ethnicity 14.2%

Income 21.4%

Housing 21.4%

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Decoding Gentrification | 14

G e n t r i f i c a t i o n

I n d e x

0.55

0.56

0.57

0.30

0.29

0.15

0.14

0.53

0.57

0.31

0.58

0.29

0.30

0.16

0.54

0.17

0.33

0.82

0.33

0.14

0.13

0.54

0.36

0.59

0.35

0.12

0.10

0.15

0.52

0.55

0.61

0.32

0.31

0.18

0.16

0.19

0.17

0.60

0.58

0.30

0.15

0.19

0.26

0.28

027

0.26

0.13

0.12

0.11

Decoding Gentrification | 15

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Chapter 2 King’s Cross

Decoding Gentrification | 17

2.1 History of King’s Cross King’s Cross once was open fields, With Regent’s Canal appeared, it have been turned from residential into railway use and industrial land, after the Second World War, it went into decline and become a series of disused warehouses and contaminated land. With an industrial history, Kings Cross has a rich heritage, which are now being refurbished and reused.

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Decoding Gentrification | 19

2.2 Privately Owned Public Space The regeneration of Kings Cross has transformed the area ownership into private houses and privately owned public spaces. Such kind of public spaces are seemingly accessible and have the look and feel of public land, however they are not subject to ordinary local authority by laws but rather governed by restrictions drawn up by the landowner and enforced by private security companies.

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Decoding Gentrification | 20

Decoding Gentrification | 21

2.3 Local Scale Analysis House Price

2000

2001

2002

2003

2007

2008

2009

2010

2014

2015

2016

2017

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House

Price

2004

2005

2006

2011

2012

2013

2018

2019

2020

2000-2020

Decoding Gentrification | 23

2.3 Local Scale Analysis House Price

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House

Price

2000-2020

Decoding Gentrification | 25

2.3 Local Scale Analysis House Price House Price Hotspot 2000 Sold house price from below £300,000 to above £900,000

Above £900,000

Below £300,000

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House Price Hotspot 2000 £300,000

£900,000 Decoding Gentrification | 27

2.3 Local Scale Analysis House Price House Price Hotspot 2010 Sold house price from below £300,000 to above £900,000

Above £900,000

Below £300,000

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House Price Hotspot 2010 £300,000

£900,000 Decoding Gentrification | 29

2.3 Local Scale Analysis House Price House Price Hotspot 2020 Sold house price from below £300,000 to above £900,000

Above £900,000

Below £300,000

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House Price Hotspot 2020 £300,000

£900,000 Decoding Gentrification | 31

2.3 Local Scale Analysis House Price The trends of the 20 years sold house price here, in the first decade the price is under a low level, below 500,000 pound, Then from 2013 to 2020 it rose dramatically to a higher level nearly 800,000 pounds

£800,000

£700,000

£600,000

£500,000

£400,000

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Decoding Gentrification | 33

2.4 Local Scale Analysis Crime Rate The crime location and crime type from 2017 to 2020 shows the proportion of anti-social behavior and violent and sexual offenses crime are the largest.

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Data source: Metropolitan Police https://www.met.police.uk/ Decoding Gentrification | 35

2.4 Local Scale Analysis Crime Rate Crime hotspot Nov 2017 - Oct 2018 Highest level crime type: Anti-social behaviour Violence and sexual offences

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Crime Rate Hotspot 2017-2018 Low High Decoding Gentrification | 37

2.4 Local Scale Analysis Crime Rate Crime hotspot Nov 2018 - Oct 2019 Highest level crime type: Anti-social behaviour Violence and sexual offences

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Crime Rate Hotspot 2018-2019 Low High Decoding Gentrification | 39

2.4 Local Scale Analysis Crime Rate Crime hotspot Nov 2019- Oct 2020 Highest level crime type: Anti-social behaviour Violence and sexual offences

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Crime Rate Hotspot 2019-2020 Low High Decoding Gentrification | 41

2.4 Local Scale Analysis Crime Rate Four types of crime: Anti-social behaviour Violence and sexual offences Theft Drug

• Violence and Sexual Offences

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• Anti-social Behaviour

• Theft

• Drug

Decoding Gentrification | 43

2.4 Local Scale Analysis Crime Rate

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Decoding Gentrification | 45

2.5 Consumption Activity The analysis of restaurants hygiene - the map indicates that most of the restaurants in site are of high hygiene grade. This can be inferred that most of them are high class restaurants. It means consumption here is getting higher, which is one of the embodiments of gentrification.

No data

Closed

Bad

Data source: Food Standards Agency https://ratings.food.gov.uk/ The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Not Good

Median

Good

Very Good

Restaurant hygiene Grade Low High Decoding Gentrification | 47

2.5 Consumption Activity

Commercial Categories

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Average Consumption

The Boutique Index

The Ethnic Index

Decoding Gentrification | 49

2.5 Consumption Activity

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Decoding Gentrification | 51

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Chapter 3 Local Culture

Decoding Gentrification | 53

3.1 Culutural Infrastructure Map Culture infrastructure map of London shows venues catering urban civic interests, that can be categorized into exhibition and producing spaces.

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• •

Archives

Artists workspaces •

Art Centres •

• • •

Community centres Commercial galleries

•

•

space

Creative workspace

Dance rehearsal studios

•

Fashion and design manufacturing

Individual studio

Jewellery design and manufacturing

• Exhibition spaces

Large scale screen based media production studios • •

Legal street art walls

•

LGBT+ night‐time venues •

•

artform centres

Libraries

Markerspaces

•

Making & manufacturing for the creative industries •

Museums and public galleries

•

Music office based businesses •

Music rehearsal studios •

•

•

space •

Commercial or

Music venues grassroots

open to public

• Producing units

Places of creative production, where creative work is made by artists

Music recording

Outdoor spaces for cultural use Prop & costume making and hiring Pubs

Set and exhibition design and building • •

community

private space

• •

Public &

Music venues all

• •

Multi‐use

Places of creative spaces, where creative work is exhibited

Live in artists’ workspace •

•

Co‐working

Dance performance venues •

•

•

Creative co‐working desk space •

•

Cinemas

•

Commercial organization serving creative industry

Skate parks

Textiles design and finishing services •

Theatre rehearsal studios •

Theatre Decoding Gentrification | 55

• Producing units

3.1 Culutural Infrastructure Map The dense of cultural infrastructure near King’s Cross area is lower than other places in London. Art and design studios were priced out from this area

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• Exhibition stages

62

Archives

13

4

Artists workspaces

2

0

4

Arts Centres

Cinemas

0

14

Jewellery design and manufacturing

Markerspaces

Large media production studios

Museums and public galleries

1

14

14

1

20

Dance rehearsal studios

10

Music office based businesses

20

Skate parks

Theatre rehearsal studios

Theatre

Prop&costume making&hiring

Commercial galleries

67

4

3

15

1

Community centres

Creative workspace

Creative co‐ working desk space

Dance performance venues

8

7

27 X

0

LGBT+ night‐ time venues

13

Music rehearsal studios

12

Textiles design and finishing services

Legal street art walls

1

Libraries

Fashion& Listed design Buildings manufacturing

Live in artists’ workspace

Music recording studios

40

10

0

0

Music venues all

Music venues grassroots

Making & manufacturing for the creative industries

Set and exhibition design and building

190 Pubs

Decoding Gentrification | 57

3.2 Art Events Activity

0 – 5000 5,000 – 10,000 10,000 – 50,000 50,000 – 100,000 100,000 – 500,000

500,000 – 2,000,000

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3.2 Art Events Activity Art council England granted projects annual attendance 2012 - 2017

2014

2012‐2013 2013‐2014

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4‐2015

2016‐2017 2015‐2016

Decoding Gentrification | 61

3.2 Art Events Activity Art council England granted projects annual attendance 2012 - 2013

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0 – 5000 Theatre 8%

5,000 – 10,000 10,000 – 50,000

Music 2%

Visual arts 0%

Combined arts 11%

50,000 – 100,000

Dance 53% Literature 26%

100,000 – 500,000

Thousands

Annual Attendance

500,000 – 2,000,000

800 700 600 500 400 300 200 100 0

Dance

Literature

Combined art Theatre

0

5

10

15

20

25

30

35

Decoding Gentrification | 63

3.2 Art Events Activity Art council England granted projects annual attendance 2016 - 2017

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0 – 5000 5,000 – 10,000

Music 7%

10,000 – 50,000

Literature 2% Visual arts 0%

Theatre 21%

50,000 – 100,000 Combined arts 0% Dance 70%

100,000 – 500,000

Thousands

Annual Attendance

500,000 – 2,000,000

700 600

Dance

500 400 300 200 100 0

Theatre Music

Dance

0

5

10

15

Theatre

20

25

30

Decoding Gentrification | 65

3.3 Art Events Activity

Art council England granted projects annual attendance in King’s Cross 2012 - 2017 Visual art

Theatre

Music

Combined ArtMusic

Literature

Museum

Dance

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launch CQ), in owl-

returns n events 00% Dendreds ver 200 entral

Fanmen House

2018 /Matter of Stuff Concept Gallery

Lewis Cubitt Park

2016 /a super façade structure by Satellite Architects 2017 /a stellar line-up of international design brands across furniture, lighting and accessories

Cubitt House Pavilion

2019 /cutting edge iconic furniture and lighting brands

cated on Unity archid.

The Greenhouse

2019 an immersive concept shop celebrating sustainable living in unit 5 at Coal Drops Yard.

he area itions, activihan 50 geous s at ars.

Coal Drops Yard

Central Saint Martins

2018 /Printed Leather Launch at Tom Dixon 2016 /take part in a series of short courses Studio /ELECTROANALOGUE 2020 /Unity / Tree Giveaway / The Misused presents The Hardware /‘Life’ Drawing Class The Crossing /OCTAGON at Tom Dixon 2017 /feature a variety of installations, The Canopy / 7 days of independent design including the launch of the Rado Star 2017 /The Shopping Destination / In conversation with The Misused Prize UK 2019 /A market of seventy pop-up shops and emerging design at Samsung KX labels / Pattern Portraits

Granary Square 2020 The Coal Office offer

2016 /10 monopoly-style houses 2017 headline projects including a new collaboration with Renault, a series of installations and the main designjunction Box Office.

2020 Planted

London design festival in King’s Cross 2018

Group Name | 46

Decoding Gentrification | 67

3.3 Art Events Activity Transition of public space usage in cultural perspective

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Decoding Gentrification | 69

3.4 Social Media Data Analysis Introduction

COLOUR ANALYSIS The colour of the images represents the perspective of users’ view of the city landscape.

Combining the extracted dominant colour and sentiment text information, we re-connect the function programs with these two materials.

NATURAL LANGUAGE ANALYSIS We use Word2vec to infer the crowd’s percepetion of King’s Cross, we plan to see the word relationship in specific perspective.

We set x,y,z as art, gallery small business, clubhouse.

Each word in its ‘word pairs’ has its own value in 3 dimensions. More relevant, higher value.

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Instagram posts analysis 30000+posts were scraped from instagrm In and around King’s Cross according its location.

Aim to find people’s needs and evaluation in King’s Cross. | 20000 Posts located in King’s Cross. | 5000 Posts located in Granary Square. | 5000 Posts located in Coal Drop Yard. | 2000 Posts located in Lewis Cubitt Square. | 1000 Posts located in Gasholders.

Decoding Gentrification | 71

3.5 Instagram Image Color Analysis

What is K-Means algorithm?

Instagram is a popular platform to share your ideas and your experience. By studying most liked posts geotagged in a place, we can know what attract people and how people perceive this specific place.

Dominant colors are the colors that are represented most in the image. Dominant colors can represent the most visible visual feature of a place.

K-Means clustering is not limited to the consumer information and population scientist. It can be used for Imagery analysis as well. Here we would use K-Means clustering to classify dominant colors of images from INS.

K-means clustering is an unsupervised learning algorithm which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest centroid. The algorithm aims to minimize the squared Euclidean distances between the observation and the centroid of cluster to which it belongs.

The first step is to find popular tagged places in King’s Cross and download 100 most liked and most recent images from each place.

What ‘k’ means in K-means color analysis?

The second step is to transform image into pixel to get their R,G,B value for clustering.

The third step is using K-means clustering to get 4 dominant colors of each image.

The fourth step is to map all dominant colors.

Original image

k=3

k=8

k=20

k=40

The last step is using K-means one more time to get 4 dominant colors of 3 places.

Finally we’ve got 4 dominant colors of King’s Cross.

They may be usd into design process in the future.

k=13

image from: http://datahacker.rs/007-color-quantization-using-k-means-clustering/

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Analysis Process Granary Square

Popular tagged places in King’s Cross 100 Posts from Instagram Most liked & Most recents

Coal Drops Yard Lewis Cubi� Park

1. 100 Images: Top Posts & Most Recent Posts

Transform image into pixels Get the R, G, B value of pixels

K-means clustering Get dominant colors of image

100 images

Map all dominant colors of each image 2. Pixel Colors in Order of RGB

4 dominant colors per image

1

11

21

31

41

51

61

71

81

91

2

12

22

32

42

52

62

72

82

92

3

13

23

33

43

53

63

73

83

93

4

14

24

34

44

54

64

74

84

94

5

15

25

35

45

55

65

75

85

95

6

16

26

36

46

56

66

76

86

96

7

17

27

37

47

57

67

77

87

97

8

18

28

38

48

58

68

78

88

98

9

19

29

39

49

59

69

79

89

99

10

20

30

40

50

60

70

80

90

100

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

2 7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97

3 8 13 18 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 98

4 9 14 19 24 29 34 39 44 49 54 59 64 69 74 79 84 89 94 79

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100

Dominant colors of each place K-means clustering Get dominant colors of 3 places 3. Color Clustering

Dominant colors of King’s Cross

? ? ? ?

Decoding Gentrification | 73

3.5 Instagram Image Color Analysis Granary Square

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Coal Drops Yard

Lewis Cubitt Park

Pixels of all dominant colors

4 Dominant Colors of INS Images

Decoding Gentrification | 75

3.6 Natural Language Analysis Word2vec is an iteration based method for creating word embedding for a given corpus. Word embedding is a way to represent a word with real valued vector such that it has the context of the adjoining words. Word2vec has two different models for word embedding, the continuous bag of words (CBOW) and the skip gram model. In the CBOW model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. The CBOW model architecture is as shown. The original model tries to predict the target word by trying to understand the context of the surrounding words. Modified CBOW is used to embed the word to space dimension and reveal word relationship in different catagories.

INPUT

PROJECTION

OUTPUT

w(t-2) w(t-1)

SUM w(t)

w(t+1) w(t+2)

In order to infer the crowd’s perception of King’s Cross, we plan to see the word relationship in specific perspective.

Each word in its ‘word pairs’ has its own value in 3 dimensions.

EXAMPLE: ‘It is a pleasant day’ word pairs in the form (contextword, targetword) set the window size as 2 word pairs : ([it, a], is), ([is, pleasant], a), ([a, day], pleasant). predict the target word Original CBOW

CBOW Model In the CBOW model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle.

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Places Set 3 types of places, art gallery, small business and clubbing as x,y and z.

64% data are highly relevant with art gallery.

57% data are highly relevant with small business.

53% data are highly relevant with clubbing.

Decoding Gentrification | 77

3.6 Natural Language Analysis

Activities Set 3 types of activities, workout, photoshoot and design as x,y and z.

54% data are highly relevant with workout.

55% data are highly relevant with photoshoot.

54% data are highly relevant with design.

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People Set 3 types of people, artist, influencer and designer as x,y and z.

64% data are highly relevant with artist.

57% data are highly relevant with influencer.

53% data are highly relevant with designer.

Decoding Gentrification | 79

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Chapter 4 Environment Data Collection

Decoding Gentrification | 81

S e n s o r s

f o r

D a t a

C o l l e c t i n g

2

4.1 Street Scale Data Collecting Arduino Sensors

S e n s o r s

Three different methods were experimented and finally a 75m*75m grids was utilized. A laptop was connected with arduino sensors to test each grid point for 1 minute.

5

f o r

D a t a

C o l l e c

2

3

Preparation Sensors for Data

I n - s i Collecting

1 Arduino Workshop pieces 1

2

6 2/3 Arduino and Sensors(GPS/ Dust/Loudness/PM2.5 ) 2 /3 . Arduino and Sensors(GPS/Dust/Loudness/PM2.5)

3

ieces

2 /3 . Arduino and Sensors(GPS/Dust/Loudness/PM2.5)

3

1. Arduino workshop pieces

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2 /3 . Arduino and Sensors(GPS/Dust/Loudness/PM2.5)

d a t a

g a t h e r i n g

Collecting

p r o c e s s

7 /8 Put the laptop in a shopping trolley..

7

Or put it into a cart.

Always keep the laptop open .

Befo

5 /6 Connect a with laptop dress up w

8

After 9 Deal with data.

5

B

g a t h e r i n g

Colle

p r o c e s s 9

7 Collection

Analysis 8

5 /6 Connect all sensors with laptop and dress up warmly.

9 Data visualization and Processing

7 /8 Put the laptop in a shopping trolley., or put it into a cart. Always keep the laptop open .

I n - s i t u

d a t a

9

g a t h e r i n g

5 /6 Co wit dre lap

7 /8 Put the shopping t

Or put it in Always kee laptop ope

C

p r o c e s s

7 /8 Pu sho

7 6

Or Alw lap

Afte

9 Deal withAd

9 De

Decoding Gentrification | 83

2020 DEC 15

Dust Sensor Detects the dust particle concentration in air by using optical sensing

Air Quality Sensor is designed for indoor air quality testing.

GPS a great choice for personal navigation projects and location services,

Dust Sensor Detects the dust particle concentration in air by using optical sensing

Loudness Sensor is designed to detect the sound of environment.

GPS a great choice for personal navigation projects and location services,

PM2.5 Sensor Real-time & continuous detection of dust concentration in the air

Loudness Sensor is designed to detect the sound of environment.

2021 JAN 25

2021 FEB 04

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decide a route by ourselves.

Walk along this route and collect sensing data.

Decide a route by ourselves.

Walk along this route and collect sensing data. Give up the indoor air quality sensor and buy a new loudness sensor to detect noise.

Make 75m*75m grids, collect data of each grid point. Record data for 1 minute. Use PM2.5 sensor instead of dust sensor.

Decoding Gentrification | 85

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 87

4.2 Street Scale Analysis Environment Loudness Loudness Sensor is designed to detect the sound of environment. Based on LM2904 amplifier and a built-in microphone, it amplifies and filters the high frequency signal that received from the microphone, and outputs a positive envelop. This is used for Arduino’s signal acquisition. The output value depends on the level of sound input. In order to avoid unnecessary signal disturbances, input signal will go through two times’ filtering inside the module. There is a screw potentiometer that enables manual adjustments to the output gain.

te

si s on

es

dn Lou 80dB

60dB

40dB

At each intersaction point of 75m*75m grid we have recorded 60 seconds of loudness data and then transfer the figure numbers into decibel(dB).

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Environment Loudness Level Low High The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Suburban residential

Passenger

Heavy

Whisper

neighbourhood

car

truck

20dB

50dB

70dB

90dB

Human hearing range

0dB

30dB

40dB

60dB

80dB

100dB

130dB

Threshold

Quiet

Quiet

Normal

Medium

Jackhammer

Threshold

of hearing

rural

living room

conversation

truck

of pain

setting

Loudness on site

0

150

75

225

300

375

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Environment Loudness Level Low High Decoding Gentrification | 89

4.3 Street Scale Analysis Environment PM2.5 The Grove - Laser PM2.5 Sensor (HM3301) is a new generation of laser dust detection sensor, which is used for continuous and real-time detection of dust in the air.

n .5 o

2

PM

site

47 μg/m3

Different from the pumping dust detection sensor, the HM-3301 innovatively uses fan blades to drive air, and the air flowing through the detection chamber is used as a test sample to perform real-time and continuous test on dust of different particle sizes in the air.

23 μg/m3

0μg/m3

This module is suitable for dust detectors, intelligent air purifiers, intelligent air conditioners, intelligent ventilation fans, air quality testing, haze meters, environmental monitoring and relative products and applications PM2.5 – particles <2.5 μm in size. Examples: pollen, spoors and other organic particles.

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Environment LPM2.5 Level Low High The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Daily air quality band (Committee on the Medical Effects of Air Pollutants)

Low

Moderate

High

Very High

24-Hour PM2.5 levels(μg/m3)

23

11

0

35

41

58

53

47

64

70

71 or more

PM2.5 levels on site

0

150

75

225

300

375

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Environment PM2.5 Level Low High Decoding Gentrification | 91

4.4 Street Scale Analysis Environment PM1 PM1 – particles <1 μm in size. Examples: dust, combustion particles, bacteria and viruses. n 1o

PM

site

47 μg/m3

23 μg/m3

0μg/m3

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Environment PM1 Level Low High The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Daily air quality band (Committee on the Medical Effects of Air Pollutants)

Low

Moderate

High

Very High

24-Hour PM1 levels(μg/m3)

23

11

0

35

41

58

53

47

64

70

71 or more

PM1 levels on site

0

150

75

225

300

375

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Environment PM1 Level Low High Decoding Gentrification | 93

4.5 Street Scale Analysis Environment PM10 PM10 – particles <10 μm in size. Examples: coarser fine dust and organic particles.

10

PM

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58 μg/m3

33 μg/m3

0μg/m3

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Environment PM10 Level Low High The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Daily air quality band (Committee on the Medical Effects of Air Pollutants)

Low

Moderate

High

Very High

24-Hour PM10 levels(μg/m3)

33

16

0

50

58

83

75

66

91

100

101 or more

PM10 levels on site

0

150

75

225

300

375

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Environment PM10 Level Low High Decoding Gentrification | 95

4.6 Street Scale Analysis House Price

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House Price Level Low The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

High

UK Average House Price Index

Low

Moderate

High

Very High

UK House Price Average from January 2005 to December 2020 140,000£

160,000£

180,000£

200,000£

240,000£

300,000£

360,000£

480,000£

600,000£

720,000£

720,000£ or more

House Price on site

0

150

75

225

300

375

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

House Price Level Low

High Decoding Gentrification | 97

4.7 Street Scale Analysis Crime

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2019

2018

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Crime Type and Location Drug Theft Anti-social Behaviour

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Violence and Sexual Offences

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150

75

225

375

300

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Crime Type and Location Drug Theft Anti-social Behaviour

Violence and Sexual Offences

Decoding Gentrification | 99

4.8 Street Scale Analysis Public facilities and housing

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Housing Type and Location Council Housing Private Owned Property The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Housing Associated Property Student Accommodation

0

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300

375

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

Full bleed single page image

225

Camley Street 300

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Housing Type and Location Council Housing Private Owned Property

Housing Associated Property Student Accommodation Decoding Gentrification | 101

4.8 Street Scale Analysis Public facilities and housing

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Distance between Public Facilities and Housing Long Short The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

0

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600

525

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750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Distance between Public Facilities and Housing Long Short

Decoding Gentrification | 103

4.9 Street Scale Analysis Data Overlay

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0

150

75

225

300

375

450

600

525

675

750

825

900

75 York Way Railway Track Canal Reach 150

Regent’s Canal Rubican Court

225

Camley Street 300

Full bleed single page image

Railway Track

375

Copenhagen Street

Coal Drops Yard York Way 450 Regent’s Canal St Pancras 525

Old Church Regent’s Canal

600

Decoding Gentrification | 105

4.10 Street Scale Analysis Edge Condition The areas between gentrified and pre-gentrified land

0

150

75

225

450

375

300

600

525

675

750

825

Student accommodation

Theft 75 Drug

PM10 air pollution

PM1 air pollution 150

York Way

Railway Track

Housing associated property

Canal Reach

Violence and Sexual Offences

Regent’s Canal High profile artwork

Rubican Court

Anti social behaviour Council house

225

Private owned property High profile design architecture PM2.5 air pollution Camley Street

Passenger car noise

300

High house price Railway Track

375

Boutique shops Coal Drops Yard Ethnic restaurants

Industrial heriatage

Copenhagen Street York Way

450 Regent’s Canal

Institution organazed activity

St Pancras 525

Old Church Railway noise

600

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Regent’s Canal

Low house price

900

0

150

75

225

450

375

300

600

525

750

675

825

900

Street stall

75

Field workshop Briefing workshop

Design fest

York Way

Mobile unit Railway Track Canal Reach

150

Environment week

Regent’s Canal Development trust

Rubican Court Art workshop

Open space workshop

Full bleed single page image

225

Urban design studio

Camley Street 300

Design game

Community design center

Idea competition Railway Track

375

Artists and Activists

Copenhagen Street

Coal Drops Yard Community planning event

York Way

450 Community planning forum

Design workshop

Regent’s Canal

St Pancras 525

Old Church Regent’s Canal

600

Decoding Gentrification | 107

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Chapter 5 Datascapes Analysis

Decoding Gentrification | 109

5.1 Datascapes Analysis Environment Loudness

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 111

5.1 Datascapes Analysis Environment PM 1.0

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 113

5.1 Datascapes Analysis Environment PM 2.5

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 115

5.1 Datascapes Analysis Environment PM 10

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 117

5.1 Datascapes Analysis Crime

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 119

5.1 Datascapes Analysis House Price

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 121

5.2 Datascapes Analysis Environment Hazard

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 123

5.3 Datascapes Analysis Desirability

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 125

5.4 Datascapes Analysis All Data

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 127

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Chapter 6 Design Methodology

Decoding Gentrification | 129

6.1 Decoding Gentrification and Encoding Local Culture To conclude the mechanism of gentrification as a topdown process, the artists, creative units and culture events are used as temporary tools to increase the value of derelict land that owned by developer and landowners, who will then regenerate the land with luxury apartments, upgraded environment and chain stores, and displace the existing residents and creative groups with middle class, professionals and institutions in order to make sure of the profit.

How to intervene the trend of displacement and homogenisation into cohesion and diversity? Is it possible to change the negative side of gentrification?

Answering these questions, a starting point can be promoting a bottom-up network, which including a collective of diverse groups. The purpose of this network is to develop the quality of life in the community instead of the quantity of increased land value.

Residents living in a top-down gentrified site can be motivated to set up a bottom-up system to counteract gentrification by taking participatory process in cultural activities and infrastructure.

The traditional community participation system (Wates 2014) is a workshop-based process engaging local interests by events and steering group meetings, facilitated by professionals and supporting bodies (councils and development organisations).

A hybrid method of networked system is proposed to incorporate diverse elements - local residents, artists and creative workers, machine network, neighbourhood environment and social media in a systemic design. This design system is established in an ecological way. First, using networks of extended human cognition (sensors) to collect the data of environment dynamics. Second, the data will be processed by machine networks such as agent-based models of swarm intelligence and Internet of Things.

Developer Landowner

Profit

Ownership

Middle‐class

Professionals

Artists Gentrified Land

Institutions Displacement

Derelict Land

Creative Units

Regeneration

Culture Events

Increased Land Value

Luxury Apartments

Upgraded Environment

Chain Stores

Figure 24. The mechanism of gentrification

Local 2.3 The Mechanism of Interests Gentrification Getting Started

Neighbourhood

How to intervene the trend of displacement Environment and homogenisation into cohesion and diversity? Is it possible to change the negative Quantitative Dynamics Processing Data side of gentrification? Answering these questions, a starting point can be promoting a bottom‐up network, which including a collective of diverse groups.

The purpose of Networked this network is to develop the quality of life in Machine Community the community instead of the quantity of Network Participation increased land value.

Social

Preparation

To conclude the mechanism of gentrification as a top‐down process, the artists, creative Traditional units and culture events are used as Community temporary tools to increase the valueSupport of Participation derelict land that owned by developer Bodies and Steering Group/ landowners, who System will then regenerate the Host/Organiser land with luxury apartments, upgraded environment and chain stores, andThe Event displace the existing residents and creative groups Follow‐up with middle class, professionals and institutions inFacilitators and order to make sure of the Event Team profit.

System

Media

Qualitative Dynamics

Real‐time Interaction

Local Residents/Artists/ Creative Workers

Local Residents

The traditional community participation neighbourhood environment to generate Artists system (Wates 2014) is a workshop‐based real‐time interaction between groups of users Community process engaging local interests by events (local residents, artists and creative workers).

Profit Ownership Groups and steering group meetings, facilitated by The interacted discussion and response (qualitative data) will be put onCo‐design social media Civic professionals and supporting bodies (councils Society and development organisations). A hybrid as data dynamics to be processed and 24 fed method of networked system is proposed to back to the neighbourhood environment.

In Derelict Diversity Co‐build Local Culture incorporate diverse elements ‐ local this way, Land an interactive and closed‐loop residents, artists and creative workers, system is established and operational.

Community machine network, neighbourhood Culture Cohesion To test this theoretical method, the environment and social media in a systemic Events Regeneration Foster environment data will be collected by design. This design system is established in an extended cognition devices such as the ecological way. First, using networks of Arduino computer and sensors, the agent‐ extended human cognition (sensors, Increased Land IoT) to Value based model of swarm intelligence will be collect the data of environment dynamics. simulated on the site of King’s Cross area in Second, the data will be processed by order to run a ecology of participation against machine networks such as agent‐based the mechanism of gentrification. models of swarm intelligence and Internet of Culture Infrastructure Upgraded Environment Things. The result of this process as the Figure 32. substance dynamics will be fed back to the Figure 30. A participatory process

Social media analysis by natural language

4.1 Participatory Process The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Two methods, swarm intelligence and Internet of Things from previous discussions

based on their availabilities and willingness.

Through regenerating culture infrastructure and upgrading the environment in the community, the land value will increase but the increased value is directly returned to local residents, artists and community groups.

Moreover, instead of developers using

6.2 Agent-based Design To form the agent-based model, swarm intelligence as the algorithm and the plugin of Culebra in Rhino Grasshopper is selected to run the multi-behavioural system. Several types of behaviours are defined in the simulation, flocking, self-organization, wandering and forces.

Flocking behaviour is exhibited when a flock of birds are feeding or flying. As a result of basic rules followed by individuals, it does not require any coordination from the central level, thus it is considered as an emergent phenomenon. Three rules, alignment, separation, cohesion, are controlling the basic behaviour of flocking.

The flock moves in a highly realistic manner due to these three basic rules, resulting in intricate motion and interaction that would be extremely difficult to construct otherwise.

Self-organization is the phenomenon through which some kind of general order or coherence emerges from the dynamic interaction of smaller component elements of an originally chaotic system. The operation of self-organization is spontaneous, and it is not driven by any additional agency outside of the process.

Wandering is a form of random navigation that has a long-term order: the steering orientation on one frame is connected to the heading path on the following frame, and so forth. As a result, the car moves in a far more unique manner than if it were just steered randomly at each frame.

A force is a push or pull on an object caused by its contact with the other. When two objects interact, a force is exerted on each of the items. When the interaction is terminated, the two objects no longer apply the force.

Decoding Gentrification | 131

6.2 Gentrification A Complex System After overlapping data we can draw a conclusion that Gentrification is a complex concept, with positive and negative effects. The very cause of it is the change of land ownership, mainly from public to private. It affects not only economic numbers, but also the environment and people’s behaviour.

Community and their characteristic are becoming more mainstream and less diverse, which means continuous displacement of marginalized groups, lack of accommodation for grass root activities.

How to intervene the trend of displacement and keep a diverse and balanced community? It can start with promoting participation between different groups, fostering a diverse and inclusive community, build trust and connection.

•

LOUDNESS 60 SECONDS Quiet

•

LOUDNESS AVERAGE LEVEL High

•

Violence and Sexual Offences Anti-social Behaviour Theft Drug

PM2.5 AVERAGE LEVEL High

•

Property transaction higher than £50,0000 Property transaction lower than £50,0000

PRIVATE OWNED PROPERTY AND PUBLIC FACILITIES Strong

•

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Low

HOUSE PRICE 2017-2020  

•

Low

CRIME TYPES • • • •

•

Noisy

Weak

HOUSING TYPES • Council Housing • Housing Associated Property • Private Owned Property • Student Accommodation

Hygiene

Privately Owned Public Spaces

Popularity

Safety

Accessibility

Price

Cause

Economy

G E N T R I F I C AT I O N Environment

Trend

Inclusivity

Community

Safety

Diversity

Gentrified Community

Displacement

Activity

Participation Intervene

Inclusivity

Diversity

Participated Community

Displacement

Activity

Participation

Decoding Gentrification | 133

6.3 Cellular Automata How to create a self-organized community and predict the dynamic of crowd? We chose Cellular automata as part of our design system, with rules that are best for generating crowd. Studied several rules such as famous Convays game of life, brian’s brain and Fireballs, we simulated and found the birth from 2 alive neighbour is better to form groups, the cells survive when there are 3, 4 or 5 alive cells around, they have 4 status of generation at the most.

Rule - S/B/G

Conway’s Game of Life – 23/3/2

The game is played on a 2-dimensional grid. Each cell can be either “on” or “off”. Each cell has eight neighbours, adjacent across the sides and corners of the square.

The Life rule can be simply expressed (in terms of the way it affects a cell’s behaviour from one generation to the next) as follows: If a cell is off and has B living neighbours (out of 8), it will become alive in the next generation.

If a cell is on and has S1 or S2 living neighbours, it survives; otherwise, it dies in the G3th generation.

Brian’s Brain – /2/3

Fireballs – 346/2/4

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Design Investigation – Crowd Generator

• Iteration 4

• Iteration 3

• Iteration 2

• Iteration 1

Crowd Generator– 345/2/4

RULE: Survival 345/ Birth 2/ Generations 4 RULE: Survival 345/ Birth 2/ Generations 4

Decoding Gentrification | 135

6.4 Design System In the design system we firstly input multiple data including social media, artists, influencer, the machine processes and output self organized crowd structures according to iteration time. Then overlay the structure with data collected from the site by sensor and from real time social media in order to form an adaptive system, with both data and substance dynamics. The output from cellular automata provides the scale and location of newly generated space, the keywords are extracted to represent function, environment data drive the space to be open or enclosed, feedbacks from social media will input to system to keep the circle running.

Feedback

Input

Cellular Automata Crowd Generator

Agent

Artists/Vlogger /Influencer

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Design Investigation – Crowd Generator

#behaviour

House price

#function #perception #group

n iteration times

Crime rate

Air quality #behaviour

Output

#function #perception #group

Noise level

n+1 iteration times

Housing type

Public facility

#behaviour #function #perception

#group n+2 iteration times

Decoding Gentrification | 137

6.5 Crowd Generator

Cell unit 3*3m on site open area

Cell unit 1.5*1.5m on site open area

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Cell unit 15*15m

Cell unit 5*5m

Cell unit 10*10m

Cell unit 2*2m

Decoding Gentrification | 139

6.6 Agent-based Model Simulation

Attraction: Desirable Area

Repulsion: Undesirable Area

Attraction and Repulsion set on site

Creepers Generating

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Trails Simulating

Connectivity between Creepers

Trails Generating

Trails and Connectivity

Decoding Gentrification | 141

6.6 Agent-based Model Simulation

Attraction: Desirable Area

Repulsion: Undesirable Area

Attraction and Repulsion set on site

Creepers Generating

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Trails Simulating

Connectivity between Creepers

Trails Generating

Trails and Connectivity

Decoding Gentrification | 143

6.6 Agent-based Model Simulation

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Iteration 6

Iteration 7

Iteration 8

Iteration 9

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Iteration 10

Iteration 11

Iteration 12

Iteration 13

Iteration 14

Iteration 15

Iteration 16

Iteration 17

Iteration 18

Decoding Gentrification | 145

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Decoding Gentrification | 147

6.7 Space Generating

1st Generation Connectivity generated at hazard area

2nd Generation Agent creepers from joint connections

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

3rd Generation Filtered trails of agent creepers

4th Generation Space and strucutre forming

Decoding Gentrification | 149

6.7 Space Generating Exhibition spaces Medium units near workspaces in low house price area Producing workspaces Small units near exhibition spaces in low and high house price area Activity spaces Large units, near living units and workspaces Education spaces Medium units, in high crime area

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Platform Theatre

Central Saint Martins

Kings Place

Central Saint Martins Lafayette Gallery

Decoding Gentrification | 151

Function Growth: From Points To Spaces 6.7 Space Generating

Attracted Points

Space Generation

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Point Connectivity

Space Shaping

1 unit

3 units

6 units

9 units

12 units

15 units

18 units

21 units

24 units

27 units

30 units

33 units

Decoding Gentrification | 153

Function Growth: From Points To Spaces

6.7 Space Generating

1 Unit Size: 5m*5m*5m Function: Affordable Workspace for one creative worker

3 Units Size: 5m*5m*5m*3 Function: Affordable Workspace for creative workers

6 Units Size: 5m*5m*5m*6 Function: Exhibition spaces for creative products/ Education spaces for young people

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

15 Units Size: 5m*5m*5m*15 Function: Exhibition spaces for creative products/ Education spaces for young people/ Activity spaces for long term residents

30 Units Size: 5m*5m*5m*30 Function: Activity spaces for long term residents

Decoding Gentrification | 155

6.8 Surface Adaption Analysis

3 Levels of Pollution : High Middium Low

3 Types of Facade : Openess

Agent Based Simulation: Mesh Crawling agent count= 30 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 40 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 50 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 60 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 70 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 80 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 90 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 100 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 110 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 120 align= 0.43 separation= 0.39 cohhesion= 0.17

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Facade Simulation for Low Polluted Area

Facade Simulation for Medium Polluted Area

Facade Simulation for High Polluted Area

agent count= 50 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 70 align= 0.43 separation= 0.39 cohhesion= 0.17

agent count= 100 align= 0.43 separation= 0.39 cohhesion= 0.17

From Simulation to Facade

Simulation

Sunshine

Trails to Facade

Noise PM2.5

Original Space

Final Facade

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Agent based surface growth 6.9 Surface Generating

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Shape Space Agent Simulation on Surface

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Group

p Name | 50

Decoding Gentrification Group Name | 161| 51

Shape Connection Agent Simulation on Surface

Group Name The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

e | 52 Decoding Gentrification Group Name| |163 53

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Group Name | 54

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Group Name | 56

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Group Name | 58

Group Name| |169 59 Decoding Gentrification

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Chapter 7 Design Speculation

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NEWLY DEVELOPED AREA

7.1 Site Selection Tiber Gardens

After the master plan for the site, we select a typical area to d bad environmental conditions and high crime rate. This com This community is full of council houses while the cultural in ple for our design.

Council House

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Private Owned House

TIBER GARDEN

do more detailed design. Tiber Garden is a good choice. It is located in hazard area with mmunity is surrounded by newly developed buildings. There is just a road between them. nfrastructure, schools and retail are all in the newly developed areas. So it is a typical exam-

Housing Associated Property

Cultural Infrastructure

Retail

NEWLY DEVELOPED AREA

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Site Selection and Edge Analysis Tiber Garden

7.1 Site Selection Tiber Gardens

Tiber Garden is located in hazard area with negative environmental conditions and high crime rate.

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Desirable and environmentally friendly area Neutral area Hazard area Decoding Gentrification | 175

7.2 Demography Analysis

•

Teenagers

•

Teachers

•

Full-time Students Self Employed Unemployed Long‐Term Sick or Disabled Part-Time Employee Full-Time Employee Looking After Home or Family Retired

Economic Activity(220)

No GCSEs

•

Professionals

5-13 14-18 19-29 30-64 65+

Age Groups(287)

•

•

Artists

Black

•

LGB

Christian No religion Buddhist Hindu Muslim

Religion(223)

Manuf Constr Retail Transpo Profess Scientif Technic

Employm The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

BTQs

facturing ruction

ortation sionals, fic and cal

•

Seniors

•

Children

•

Asian

•

Muslin

•

Students

•

Self-employed

Degree or professional qualification HNC, HND or 2+ A Levels 1-5 GCSEs or an A-Level No GCSEs or Equivalent

Education & Qualifications(231) Education Accommodation and Food Information and Communication Financial Services Administration Arts and creative industry

White Indian/Pakistani/Bangladeshi Chinese/Other Asian Black Africa/Caribbean Mixed ethnicity

Ethnic Group(287)

ment Industry(129) Decoding Gentrification | 177

White Indian/Pakistani/Bangladeshi Chinese/Other Asian

Children (5‐13 year‐old) Young adults(19‐29 year‐old)

Black Africa/Caribbean Mixed ethnicity

7.2 Demography Analysis

Asian

EXHIBITION

No GCSEs

Degree or professional qualification HNC, HND or 2+ A Levels 1‐5 GCSEs or an A‐Level

The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism

Children

Black/Caribbean

ACTIVITIES

Employees in Education

Accommodation and Food Information and Communication Financial Services

Teenag

Cultu Infrastr Func Fo Tiber G 287 Res

Professionals

Administration Arts and creative industry Manufacturing

Middle age(30‐64 year‐old) Seniors(65 and older)

Christian No religion

Seniors

gers

ural ructure ction or Gardens sidents

LGBTQs

Muslim

EDUCATION

Arts and Creative Industry

Construction Retail Transportation

Buddhist Hindu

PRODUCING

Students

Self Employed Full‐time students Unemployed Long‐Term Sick or Disabled

Self-employed

Part‐Time Employee Full‐Time Employee Looking After Home or Family Retired

Decoding Gentrification | 179

7.3 Landscape Generating

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Decoding Gentrification | 183

7.4 Architecture Generating

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7.4 Architecture Generating

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7.4 Architecture Generating

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7.5 Section Diagram

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7.6 Rendering

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Decoding Gentrification | 211

Reference

Trigg, K., (2017) Quantifying Urban Inequality: An Investigation of the Wicked Problems of Gentrification.

Master thesis in Sustainable Development at Uppsala University, No. 2017/1, 52 pp, 30 ECTS/

Driscoll D., Appiah-Yeboah A., Salib P, and Rupert D. (2007). Merging Qualitative and Quantitative Data in Mixed Methods Research: How To and Why Not. Ecological and Environmental Anthropology (University of Georgia). 18.

Voorhees Center (2014) The Socioeconomic Change of Chicago’s Community Areas (1970-2010). Available at: https://www.voorheescenter.com/gentrification-index (Accessed: 30 December 2016).

Lees L, Slater T, and Wyly E (2008) Gentrification. New York: Routledge.

Wates, N. and Thompson, J., (2008). The Community Planning Event Manual. 1st ed. London: Earthscan Publications Ltd.

Liu, Y., Batty, M., Wang, S. and Corcoran, J., 2019.

Modelling urban change with cellular automata: Contemporary issues and future research directions.

Progress in Human Geography, 45(1), pp.3-24.

Weinstock, M. (2010). The architecture of emergence: The evolution of form in nature and civilisation /Michael Weinstock. Chichester: Wiley

Malafouris L., (2013) How things shape the mind. A theory of material engagement, Cambridge, MA & London, The MIT Press,

Wates, N., (2014). COMMUNITY PLANNING HANDBOOK: How People Can Shape Their Cities, Towns and Villages in Any Part of the World- Earthscan Tools for Community Planning. 2nd ed. Taylor & Francis Ltd.

Martin, Richard W. A Quantitative Approach to Gentrification: Determinants of Gentrification in U.S. Cities, 1970-2010. Department of Insurance, Legal, Studies, and Real Estate, Terry College of Business, University of Georgia.

Yee, J. & Dennett, A., (2020). Unpacking the Nuances of London’s Neighbourhood Change & Gentrification Trajectories. Centre for Advanced Spatial Analysis, University College London

Pratt, A.(2017) The rise of the quasi-public space and its consequences for cities and culture, PALGRAVE COMMUNICATIONS Ratti, C. Claudel M, (2016), The City of Tomorrow: Sensors, Networks, Hackers, and the Future of Urban Life. Yale University Press. New York Schlichtman, John J., Patch, J. and Hill, Marc L., 2017.

Gentrifier. Toronto: University of Toronto Press. ISBN: 9781442650459 Slater, T. (2011). Gentrification of the City. In G. Bridge & S. Watson (Eds.), The new Blackwell companion to the city (pp. 571–585). Malden, MA: Wiley-Blackwell.

Song, Y., Wang, R., Fernandez, J. and Li, D., 2021.

Investigating sense of place of the Las Vegas Strip using online reviews and machine learning approaches.

Landscape and Urban Planning, 205, p.103956.

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