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
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Chapter 2 King’s Cross
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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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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
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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
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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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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.
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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
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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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si on
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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Crim
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2020
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
0
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
g usin
Ho
n es o
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site
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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
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
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
e
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or an ist ce ng
Lo n ta dis ce
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Distance between Public Facilities and Housing Long Short The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism
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
Distance between Public Facilities and Housing Long Short
Decoding Gentrification | 103
4.9 Street Scale Analysis Data Overlay
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e ciliti
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The Bartlett, UCL | MArch Urban Design | RC15: Pervasive Urbanism
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
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Shape Connection Agent Simulation on Surface
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Group Name | 54
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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
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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.
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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
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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
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7.3 Landscape Generating
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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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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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