Socio-Spatial Differentiation - People, Places and Interaction

Dr Alex Singleton

University College Londonwww.alex-singleton.com

Me

• 2000-03: Geography Degree – University of Manchester– Physical Geography / GIS– Dissertation – ‘Where do Manchester University

Students com from?’

• 2003-2005 - KTP – UCAS / UCL– Based in Cheltenham (CASA 1 day ever other week)

• 2005-this week! – SPLINT / CETL– HEFCE funded project: Nottingham / Leicester– PhD – Nov 2007

All publication titles and abstracts - to July 2010

Predicting Participation in Higher Education: a Comparative Evaluation of the Performance of Geodemographic Classifications

Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Grid-Enabling Geographically Weighted Regression: A Case Study of Participation in

Higher Education in England

Course Choice Behaviour and Target Marketing of Higher Education

Creating Open Source Geodemographics: Refining a National Classification of

Census Output Areas for Applications in Higher Education

Geodemographics, Visualization, and Social Networks in Applied Geography

Classification through Consultation: Public Views of the Geography of the e-

Society. Web Mapping 2.0: the Neogeography of the Geospatial Internet.

Exploratory Cartographic Visualisation of London using the Google Maps API

Lost in translation? Cross-Cultural Experiences in Teaching Geo-Genealogy

Uncertainty in the Analysis of Ethnicity Classifications. Issues of Size, Scale and

Aggregation of Groups

The Geodemographics of Educational Progression and their Implications for

Widening Participation in Higher Education

Linking Social Deprivation and Digital Exclusion in England

United Kingdom Surname Clusters

Domains

Higher Education

Digital Exclusion

GIS and Neogeography

Geo-Genealogy

Predicting Participation in Higher Education: a Comparative Evaluation of the Performance of Geodemographic Classifications

Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Grid-Enabling Geographically Weighted Regression: A Case Study of Participation in

Higher Education in England

Course Choice Behaviour and Target Marketing of Higher Education

Creating Open Source Geodemographics: Refining a National Classification of

Census Output Areas for Applications in Higher Education

Geodemographics, Visualization, and Social Networks in Applied Geography

Classification through Consultation: Public Views of the Geography of the e-

Society. Web Mapping 2.0: the Neogeography of the Geospatial Internet.

Exploratory Cartographic Visualisation of London using the Google Maps API

Lost in translation? Cross-Cultural Experiences in Teaching Geo-Genealogy

Uncertainty in the Analysis of Ethnicity Classifications. Issues of Size, Scale and

Aggregation of Groups

The Geodemographics of Educational Progression and their Implications for

Widening Participation in Higher Education

Linking Social Deprivation and Digital Exclusion in England

United Kingdom Surname Clusters

Methods

Geodemographics

Geoweb / Visualisation

Geocomputation

Network Analysis

“Socio-Spatial Differentiation”

DomainsCan mean many things, however, in my research, this has been ‘developing and refining models in a geodemographic tradition’

Methods

Consumption of Commercial Classification

Critique of Commercial Classification

Bespoke Geodemographics

Real-time Geodemographics

Network / Interaction Typologies

Integrating Geodemographics and spatial interaction models

Profiling HE Data

Profiling Schools Data

Educational Mosaic

Profiling education data with OAC

Decision Support Tool

Data Integration

Educational OAC

E-Society

HE Choice Sets

School-University Flows

School Catchment Models

2003

2010-

Linking Methods to Substantive Issues

• 3 Themes– Critical Geodemographics– Neogeography and Digital Exclusion– Widening Access to Higher Education

CRITICAL GEODEMOGRAPHICS

Theme 1

Critical Geodemographics

• What are geodemographics?– Brief history– How are they made?

• What are the potential problems for public sector users?

DescriptionBLACK: Lowest class. Vicious, semi-criminal.DARK BLUE: Very poor, casual. Chronic want.LIGHT BLUE: Poor. 18s. to 21s. a week for a moderate familyPURPLE: Mixed. Some comfortable others poorPINK: Fairly comfortable. Good ordinary earnings.RED: Middle class. Well-to-do.YELLOW: Upper-middle and Upper classes. Wealthy.

Walk with Police Constable Robert Turner, 12 July 1898

Charles Booth Maps – 1889-1892

Marr, T.R. (1904) Housing Conditions in Manchester and Salford. Manchester, Manchester University Press.

Social Area Analysis – Shevky and Bell (1955)

Liverpool Area Study (1971)• Richard Webber et al

– CACI (Acorn)– Experian (Mosaic)

Inputs

Area V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 ...

Area1

Area2

Area3

Area4

Area5

Area6

Area7

Area8

...

Variable 1

Variable 2

Cluster 1Cluster 2

Cluster 3

Cluster Analysis

Critique (important for the public sector!)

• One size fit all?• Open?

– Methods– Public Consultation (‘crowd sourcing’)

• k-means optimisation• Is k-means the only option? (real-time)

One size fits all?

Refined version of OAC for HE

Open? - Methods

• ONS• 2001 Census• Vickers and

Rees (2007)

Open? – Public Consultation 79,051 hits over the 13 day period 3,952 feedback responses

The percentages of unit postcodes within each CAS Ward that were searched during the study period

Frequency of feedback by origin e-Society Type

Frequency of destination e-Society Type

K-means optimisation

0.46

0.47

0.48

0.49

0.5

0.51

0.52

0.53

0.54

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145

Run

RS

Q

K-means (100 runs of k-means on OAC data set for k=4)

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

55 56 57 58 59 60 61 62 63 64 65

k

RS

Q

Is k-means the only option? (real-time)

• Alternative algorithms / simplification– PAM; GA / PCA

• Server based specification, creation, visualisation– Real time

• Computationally• Dynamics – e.g. Daytime population estimates

• GRID– GPU / CUDA

Nvidia Tesla Server - 1920 CUDA cores ~£5k

NEOGEOGRAPHY AND DIGITAL EXCLUSION

Theme 2

Neogeography and Digital Exclusion

• Interested in ‘Neogeography’ at the margins– Position paper (with Muki, Chris Parker – OS)– Encyclopaedia entry (Barney Warf)– Couple of magazine articles

• My view– The technology to make great maps exists– Next challenge is to link this with better analytical

functionality• Utilise real-time data feeds• Generalisations on the fly• Make predictions

This is great... BUT!

Winners and the Losers

WIDENING ACCESS TO HIGHER EDUCATION

Theme 3

Widening Access to Higher Education

• ~250 HE Institutions in England & Wales (HEFCE)• 2008 – 396,630 Degree Acceptances UK (UCAS)• 50% Participation by 2010 - ~43% (07-08)• Fees

– Top Up– Office of Fair Access – Access Agreements

• Monitoring– WP Benchmarks

• HECE allocated £141 million directly to institutions for widening participation in 2009-10

Occupational Group: 1968-1978

1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 19790%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Professional & TechnicalAdministrators & ManagersClerical & Armed ForcesManual

Socio-Economic Group: 2002 - 2007

2002 2003 2004 2005 2006 20070%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Higher managerial and professional occupations

Lower managerial and professional occupations

Intermediate oc-cupations

Small employers and own account workers

Lower supervisory and technical oc-cupations

Semi-routine oc-cupations

Routine occupations

0

50

100

150

200

250

300

Average Distance Travelled to University 2004 (All Home Acceptances)

0

20

40

60

80

100

120

Global Co

nnections

Cultural L

eadership

Corporate

Chieftains

Golden E

mpty Nes

tersProv

incial Priv

ilegeHigh

Technolo

gistsSem

i-Rural Se

clusion

Just Mov

ing InFled

gling Nur

series

Upscale N

ew Owner

sFam

ilies Mak

ing Good

Middle Ru

ng Famili

esBurd

ened Opt

imists

In Military

Quarters

Close to

Retireme

ntCons

ervative V

aluesSma

ll Time Bu

siness

Sprawling

Subtopia

Original S

uburbs

Asian En

terprise

Respecta

ble Rows

Affluent B

lue Collar

Industria

l GritCoro

nation Str

eetTow

n Centre

Refuge

South As

ian Indust

rySettl

ed Minorit

iesCoun

ter Cultur

al Mix

City Adve

nturers

New Urb

an Coloni

stsCarin

g Profess

ionals

Dinky De

velopmen

tsTow

n Gown T

ransition

Universit

y Challen

geBeds

it Benefic

iariesMetr

o Multicul

tureUppe

r Floor Fa

milies

Tower Bl

ock Livin

gDign

ified Depe

ndency

Sharing a

Staircas

eFam

ilies on B

enefits Low

Horizons

Ex-indust

rial Legac

yRust

belt Resilie

nceOlde

r Right to

BuyWhit

e Van Cu

ltureNew

Town Ma

terialism Old P

eople in F

latsLow

Income E

lderlyCare

d for Pen

sioners Sepi

a Memori

esChild

free Sere

nityHigh

Spending

Elders

Bungalow

Retireme

ntSma

ll Town Se

niorsTour

ist Attend

antsSum

mer Play

grounds

Greenbelt

Guardian

sParo

chial Villa

gersPast

oral Sym

phony

Upland H

ill Farmer

s

Mosaic Type / Group

Miles - Ho

me to Ins

titution

Symbols of Success

Happy Families

Suburban Comfort

Ties of Community

Urban Intelligence

Welfare Borderline

Municipal Dependency

Blue Collar Enterprise

Tw ilight Subsistence

Grey Perspectives

Rural Isolation

Who goes to university?

Symbols of Success

Urban Intelligence

- Higher Age Profile

Welfare Borderline

Municipal Dependency

Twilight Subsistence

Blue Collar Enterprise

Metro Multiculture

Key Widening Participation Groups

Who goes to university?

Young Participation 2004 - UK

0

50

100

150

200

250

300

Blue Collar Communities

City Living

Countryside

Prospering Suburbs

Constrained by Circumstances

Typical Traits

Multicultural

Young Participation 2004 - UK

0

50

100

150

200

250

300

Blue Collar Communities

City Living

Countryside

Prospering Suburbs

Constrained by Circumstances

Typical Traits

Multicultural

Prospering Suburbs

Countryside

Aspiring Households 1 & 2

Asian Communities 3

Key WP Groups

Blue Collar Communities

Constrained by Circumstances

Can I recruit from anywhere?

All 04 Acceptances Average Distance

0

20

40

60

80

100

120

1a1

1a2

1a3

1b1

1b2

1c1

1c2

1c3

2a1

2a2

2b1

2b2

3a1

3a2

3b1

3b2

3c1

3c2

4a1

4a2

4b1

4b2

4b3

4b4

4c1

4c2

4c3

4d1

4d2

5a1

5a2

5b1

5b2

5b3

5b4

5c1

5c2

5c3

6a1

6a2

6b1

6b2

6b3

6c1

6c2

6d1

6d2

7a1

7a2

7a3

7b1

7b2

Geodemographic Sub-Type

Mile

s

Blue Collar Communities

City Living

Countryside

Prospering Suburbs

Constrained by Circumstances

Typical Traits

Multicultural

Average Distance from applicant home to accepting institution

Different courses attract different people

Chemistry - UK

50

70

90

110

130

150

170

Blue Collar Communities

City Living

Countryside

Prospering Suburbs

Constrained by Circumstances

Typical Traits

Multicultural

Base - UK

Chemistry

Different courses attract different people

Music - UK

50

70

90

110

130

150

170

Blue Collar Communities

City Living

Countryside

Prospering Suburbs

Constrained by Circumstances

Typical Traits

Multicultural

Base - UK

Music

Different courses attract different people

Physical Geography (02-04) - UK

50

70

90

110

130

150

170

Blue Collar Communities

City Living

Countryside

Prospering Suburbs

Constrained by Circumstances

Typical Traits

Multicultural

Base - UK

Physical Geography

Different courses attract different people

Human Geography (02-04) - UK

50

70

90

110

130

150

170

Blue Collar Communities

City Living

Countryside

Prospering Suburbs

Constrained by Circumstances

Typical Traits

Multicultural

Base - UK

Human Geography

School Catchment Areas

School Catchment Areas

Cheltenham Kingsmead School Mosaic Profile KS4

0

50

100

150

200

250

300

Ind

ex (

Base 1

00)

Symbols of Success

Happy Families

Suburban Comfort

Ties of Community

Urban Intelligence

Welfare Borderline

Municipal Dependency

Blue Collar Enterprise

Twilight Subsistence

Grey Perspectives

Rural Isolation

Pates Grammar School Mosaic Profile KS4

0

50

100

150

200

250

300

350

400

Ind

ex (

Base 1

00)

Symbols of Success

Happy Families

Suburban Comfort

Ties of Community

Urban Intelligence

Welfare Borderline

Municipal Dependency

Blue Collar Enterprise

Twilight Subsistence

Grey Perspectives

Rural Isolation

A high performing school in Cheltenham

A low performing school in Cheltenham

Data Integration

Data Integration

DCSFKey Stage 5

HESA (0)

HESA (+1)

HESA (+2)

2004 ~50%

~20%

~5%

Direct Entry

Gap Year

Gap Years

National Targets = 18-30 Age Range

UCAS Subject Choice Associations

Subject Description/ JACS Line Code A1 A2 B0 B1 B2 B3 B4 B5 B6 B7

A1 - Pre-Clinical Medicine 76.9 0.3 0.0 2.2 2.6 0.0 0.1 0.4 0.1 0.4

A2 - Pre-Clinical Dentistry 3.7 72.0 0.0 1.0 6.3 0.0 0.1 2.1 0.1 0.2

B0 - Subjects allied to Medicine: any area 0.0 0.0 8.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0

B1 - Anatomy, Physiology and Pathology 6.7 0.4 0.0 49.3 1.7 0.1 0.4 0.5 0.3 0.7

B2 - Pharmacology, Toxicology and

Pharmacy

11.5 3.3 0.0 2.5 49.4 0.4 0.3 1.8 0.1 0.4

B3 - Complementary Medicine 0.8 0.2 0.0 3.8 3.5 37.2 0.7 0.2 0.4 0.7

B4 – Nutrition 1.2 0.3 0.0 2.6 1.4 0.2 46.6 0.4 0.6 1.8

B5 – Ophthalmics 7.7 4.9 0.0 2.3 7.4 0.0 0.4 54.1 0.7 0.5

B6 - Aural and Oral Sciences 1.7 0.2 0.0 2.1 0.3 0.1 0.5 0.7 57.8 1.7

B7 – Nursing 1.2 0.1 0.0 0.9 0.3 0.1 0.4 0.1 0.4 78.2

10 Most Homogenous Courses

• Most– B7 - Nursing– A1 - Pre-clinical Medicine– A2 - Pre-clinical Dentistry– M1 - Law by Area– D1 - Pre-clinical Veterinary Medicine– K1 - Architecture– V1 - History by Period– Q8 - Classical studies– B8 - Medical Technology– B6 - Aural and Oral Sciences

M1 - Law by Area

• Within Line = 67.6%

L7 - Human and Social Geography

• Within Line = 50%

Standardised index scores for course choice behaviour by ethnic groups

Frequency of JACS Lines Chosen/ Index Scores

Ethnic Group 1 2 3 4 5 6 or more

Asian – Bangladeshi 73 105 109 130 115 123

Asian – Indian 90 101 104 109 113 101

Asian - Other Asian background (ex. Chinese) 100 102 102 97 104 83

Asian – Pakistani 77 100 111 116 135 115

Black – African 88 103 106 106 104 121

Black – Caribbean 96 97 104 113 91 102

... ... ... ... ... ... ...

Standardised Index Scores for course choice behaviour by NS-SEC

Frequency of JACS Lines Chosen/ Index Scores

NS-SEC 1 2 3 4 5 6 or more

Higher managerial and professional

occupations108 105 97 89 86 78

Intermediate occupations 99 100 101 102 103 98

Lower managerial and professional

occupations97 103 103 101 99 99

Lower supervisory and technical

occupations103 97 99 98 100 100

Semi-routine occupations 87 98 104 115 122 125

Small employers and own account workers 90 100 105 110 110 118

Future Research Directions

• Critical Geodemographics– Inclusion of relational data into classification

• Geographic– Spatial weighting

• Network Typologies– Social Flows / Interaction

• Neogeography and Digital Exclusion– Updated small area estimates of digital differentiation– Socio-spatial implications of GPS routing

• ‘Social Routing’

– Sociology of the OSM community• Implications for data quality (Spatial & Temporal)

• Widening Access to Higher Education– Continual update to integrated data model– New HE & Schools Classifications– Decision Support Tools for WP / School Choice