Department of Geography

The analysis of residential
segregation
g gthroughg ppeople'sp
names
Pablo Mateos
Department of Geography
University College London, UK
University of Tokyo
5th March 2010

p.mateos@ucl.ac.uk
www.casa.ucl.ac.uk/pablo
www.pablomateos.com

Contents

1. Context: ethnicity and segregation
2 Methodology: Onomap classification
2.
3. Segregation in London
4. Segregation in European cities
5. Conclusion1Migration, Ethnicity & Religion in Britain• Growing debate in Britain on issues of: Migration policy
 Ethnic relations
 Religion & the State, specially Islam National identity• Since 2005, shift from multicultural toassimilationist policies
 Fear of ‘the other’ / ‘Identity crisis’ Latest buzzword: “Community
Community Cohesion
Cohesion”
 Segregation vs. integration debate
 Spatial integration =? Social Integration

London bomber video
with Yorkshire accent2

UK 2001 Census Ethnicity Classification
UK

LondonWhite
British
Irish
Other White

87.5%
1.2%
2.6%

59.8%
3.1%
8.3%

Mixed
White & Black Caribbean
White & Black African
White & Asian
Other Mixed

0.5%
0.2%
0.4%
0.3%

1.0%
0.5%
0.8%
0.9%

Black or Black-British
Black-Caribbean
Black-AfricanBlack-Other

1.1%
0.9%
0.2%

4.8%
5.3%
0.8%

Asian or Asian-British
Indian2.0%
Pakistani
1.4%
Bangladeshi
0.5%
Any other Asian backgroun 0.5%

6.1%
2.0%
2.1%
1.9%

Chinese or other group
ChineseAny other ethnic group

1.1%
1.6%

UK

LondonTotal Non- White British 12.5%

40.2%

Poorly Studied Groups

18.8%

4.9%

• 16 Categories• Based on a “skin colour problem”
mixed with 2 dimensions• Half of ethnic minority populations
fall in ‘Other’ or too broad groups

Source: ONS Census 2001

0.4%
0.4%

London’s ‘other’ ethnic groups (2001 Census)
1.2 million people (17%) stated ‘other’ ethnic identities
Ethnic Group
Population
185,690
Other white European, European Mixed
171,744
Other white, white unspecified
154,203
English53,307
Sri Lankan
46,348
Black British
37,827
Turkish35,252
Italian
35,027
Other Mixed, Mixed unspecified
29,469Any other group
23,340
Greek Cypriot
Middle Eastern (excluding Israeli, Iranian
20,537and 'Arab')
20,256
Arab
19,669Filipino
19,415
Japanese
19,239Other mixed white
18,334
Other Asian, Asian unspecified
17,888Greek
16,494
Iranian
15,952Multi-ethnic islands
15,928Polish15,607
South and Central American
14,625British Asian
Turkish Cypriot
14,074
Source: 2001 Census GLA commissioned tables

Ethnic Group
Vietnamese
Commonwealth of (Russian)
Independent States
North African
KurdishLatin American
Mixed Black
JewishOther Black, Black unspecified
Cypriot (part not stated)
Mixed: Irish and other white
Scottish
Kosovan
Welsh
SomaliEast African Asian
Chinese and White
Tamil
Black and White
MoroccanCaribbean AsianBlack and Asian
Malaysian
Albanian
Sikh

Population
11,71911,606
11,218
9,659
9,188
9,001
8,912
8,344
7,360
7,071
7,020
6,896
6,895
6,172
5,328
4,871
4,758
4,226
4,133
4,070
3,946
3,384
3,226
2,814

(.../...)

3

Problems with official ethnicity classifications• Lack of sufficient granularity• Low frequency of update• Lack of routine ethnicity data collection in surveys
and public service transactions• Lack of quality and comparability(e.g. UK hospital admissions & education)• Need for complementary methodologies to study
ethnicity beyond traditional proxies used
(country of birth and nationality of individual or parents)

Research on names and identity• Demography and epidemiology;
subdivision of populations by ethnicity– US Census Hispanic names list (Passel and Word since
1950s)– Asian surnames in US (Lauderdale, 2004)– South Asian names in UK (Nam Pechan & SANGRA)• Genetics; Population structure andgeography, endogamy and gene mutations• Economics; Name discrimination in labour,
housing, and credit markets• Geography and Sociology; culturaltransmission, migration and spatial diffusion4Decoding ethnicity from names• Names can potentially provide information about:
AspectSurname &Forename
Forename

Etimology/
Onomastics

Space-time Distribution

Language

Geographic Origin

Religion

Migration flows

GenderAge…

• Review paper of name-based classifications of ethnicity;
Mateos (2007) Population, Space and Place• Primarily public health applications• Main groups: Hispanic, South Asian, Chinese, and Muslim

Welsh Surnames 1881-1998
1881

UCL Surname Profiler
www.spatial-literacy.org

1998

1 – Context

5Names in Kreuzberg, Berlin

Creating a name-based ethnicity classification• Objective:• To create a classification of forenames and surnames by fine ethnic
groups, covering the whole of the population in 26 countries• EU-21, North Am., AU & NZ, Japan, India, & Argentina• Data sources:••••

UK Electoral Register 2001-2006 (46 million adults)
Telephone directories (300 million subscribers)
Covering a total population of 1 billion people
Individual level data (full name and address) for:• 8 million unique surnames• 5 million unique forenames• Analysis:• Over 1.5 million unique names coded into “Onomap classification”• 80 Onomap Subgroups and 14 Groups

6

World map of Onomap subgroups

Onomap Subgroups

Onomap Groups
ONOMAP GROUP
1 AFRICAN
2 ANGLO-SAXON
3 BALTIC
4 CELTIC
5 EAST ASIAN & PACIFIC
6 EUROPEAN_OTHER EASTERN
7 EUROPEAN_OTHER WESTERN
8 HISPANIC
9 JAPANESE
10 JEWISH
11 MUSLIM
12 NORDIC
13 SOUTH ASIAN
14 UNCLASSIFIED

7

ONOMAPSUBGROUP_#
ONOMAP_GROUPCODEONOMAP_SUBGROUP
1 AFRICAN
eth
ETHIOPIAN
2 AFRICAN
gha
GHANAIAN
3 AFRICAN
ken
KENYAN
4 AFRICAN
nig
NIGERIAN
5 AFRICAN
afr
OTHER BLACK AFRICAN
6 AFRICAN
sle
SIERRA LEONIAN
7 ANGLO-SAXON
ami
AMERICAN INDIAN
8 ANGLO-SAXON
ame
AMERICAN OTHER
9 ANGLO-SAXON
bla
BLACK OTHER
10 ANGLO-SAXONeng
ENGLISH
11 BALTICbal
BALTIC
12 BALTIC
est
ESTONIAN
13 BALTIC
lat
LATVIAN
14 BALTIC
lit
LITHUANIAN
15 CELTIC
cor
CORNISH
16 CELTIC
iri
IRISH
17 CELTICcel
OTHER CELTIC
18 CELTIC
sco
SCOTTISH
19 CELTIC
wel
WELSH
20 EAST ASIAN & PACIFIC
mym
BURMESE
21 EAST ASIAN & PACIFIC
cam
CAMBODIAN
22 EAST ASIAN & PACIFIC
chi
CHINESE
23 EAST ASIAN & PACIFIC
haw
HAWAIIAN
24 EAST ASIAN & PACIFICkor
KOREAN
25 EAST ASIAN & PACIFIC
lao
LAOTIAN
26 EAST ASIAN & PACIFIC
mly
MALAYSIAN
27 EAST ASIAN & PACIFIC
asi
OTHER EAST ASIAN & PACIFIC
28 EAST ASIAN & PACIFIC
tai
THAI
29 EAST ASIAN & PACIFIC
vie
VIETNAMESE
30 EUROPEAN_OTHER EASTERN alb
ALBANIAN
31 EUROPEAN_OTHER EASTERN arm
ARMENIAN
32 EUROPEAN_OTHER EASTERN bul
BULGARIAN
33 EUROPEAN_OTHER EASTERN cro
CROATIAN
34 EUROPEAN_OTHER EASTERN cze
CZECH
35 EUROPEAN_OTHER EASTERN csl
CZECH & SLOVAK
36 EUROPEAN_OTHER EASTERN esl
EAST SLAVIC
37 EUROPEAN_OTHER EASTERN hun
HUNGARIAN
38 EUROPEAN_OTHER EASTERN pol
POLISH
39 EUROPEAN_OTHER EASTERN rom
ROMANIAN
40 EUROPEAN_OTHER EASTERN rus
RUSSIAN41 EUROPEAN_OTHER EASTERN srb
SERBIAN
42 EUROPEAN_OTHER EASTERN sla
SLAVIC
43 EUROPEAN_OTHER EASTERN slk
SLOVAKIAN
44 EUROPEAN_OTHER EASTERN ssl
SOUTH SLAVIC
45 EUROPEAN_OTHER EASTERN ukr
UKRANIAN#
ONOMAP_GROUP
46 EUROPEAN_OTHER WESTERN
47 EUROPEAN_OTHER WESTERN
48 EUROPEAN_OTHER WESTERN
49 EUROPEAN_OTHER WESTERN
50 EUROPEAN_OTHER WESTERN
51 EUROPEAN_OTHER WESTERN
52 EUROPEAN_OTHER WESTERN
53 EUROPEAN_OTHER WESTERN
54 HISPANIC
55 HISPANIC
56 HISPANIC
57 HISPANIC
58 HISPANIC
59 JAPANESE
60 JEWISH
61 JEWISH
62 MUSLIM
63 MUSLIM64 MUSLIM
65 MUSLIM66 MUSLIM67 MUSLIM
68 NORDIC
69 NORDIC
70 NORDIC
71 NORDIC
72 NORDIC
73 NORDIC
74 SOUTH ASIAN
75 SOUTH ASIAN
76 SOUTH ASIAN
77 SOUTH ASIAN
78 SOUTH ASIAN
79 SOUTH ASIAN
80 UNCLASSIFIEDONOMAPSUBGRO
UP_COD ONOMAP_SUBGROUE
P
bre
BRETON
dut
DUTCH
fre
FRENCH
fri
FRISIANger
GERMAN
gre
GREEK
ita
ITALIANpor
PORTUGUESE
bas
BASQUEQ
cat
CATALANgal
GALICIAN
his
HISPANIC OTHER
spa
SPANISH
jap
JAPANESE
jew
JEWISH
jse
SEPHARDIC JEWISH
afg
AFGANISTANI
ara
ARABICegy
EGYPTIANper
IRANIANmus
MUSLIM
tur
TURKISHdan
DANISH
fin
FINNISH
ice
ICELANDIC
sca
NORDICnor
NORWEGIAN
swe
SWEDISHben
BENGALIind
INDIAN
par
PARSI
sik
SIKH
sin
SINHALESE
tam
TAMIL
XXX
UNCLASSIFIED

Onomap Subgroups

Onomap classification• Forename-Surname clustering(based on Hanks and Tucker, 2000)
UK El
Electoraltl RollR llMateos

Pablo

GarciaForenames

Juan

Pérez

Surnames

Rosa

...

Marta

Sánchez...

Rodríguez
...

–
–
–

Several iterations until self-contained cluster is exhausted
Cluster assigned a cultural, ethnic & linguistic Onomap type
Probability of ethnicity assigned to each name
Mateos et al (2007) CASA Working Paper 116

8‘Surname distance’ between forenames
A sample of 401 forenames

Social networks parallel• Granovetter (1973) The Strength of Weak Ties
• Weak ties play an essential role in the diffusion of
information and innovation• Cliques of highly related names are separated bybridges or weak links (sparse links)• Key: find those bridges to remove ‘the weak ties’in naming networks through weights• Issues of one-mode and two-mode networks• >> Current work with David O’Sullivan toautomate and improve the network clustering
process9‘Surname distance’ between forenames (II)Clustered cliques
Resulting clusters from sample of 401 forenames (328)

Onomap software
www.onomap.org

10Evaluation of Onomap at the individual level• Evaluated against individual’s ethnicity (hospital
admissions, birth register)• On average: 80% accuracy• Least indicated for:– Long established groups (i.e. Irish, Jewish)– Groups with non-unique names (i.e. Caribbeans)• Highly indicated for all other groups
Mateos et al (2007) and otherforthcoming publications

Current applications of Onomap• Cancer research– LSHTM ((5 m. ppatients))• Public Health– PCTs (Camden, Islington, Southwark)– University of Edinburgh & GROS (Onomap evaluation)– University of Essex• Political party representation– ANU, Australia; Princeton Univ.

• Residential Segregation- Europe, US, UK

11

12

13

14

Index of Dissimilarity – Onomap• Index of Dissimilarity in London, using Onomap
categories at four different geographical scales
CEL Dataset
IS(adj)British and other
Irish
W.Europe
Sikh

0.8

E.Europe

0.6

Bangladeshi
Somali

Hispanic

0.4
0.2

Other Muslim

0Japanese

ChineseSri Lankan

Pakistani

Black Caribbean

JewishGreek & G. Cypriot•3- Analysis

African
Hindu

Borough
Ward
LSOA
OA

Spatial Autocorrelation (LISA) by Onomap type15

Mapping names• The whole database is available on-line• Mapping historic and contemporary migrations

worldnames
www.publicprofiler.org/worldnames
www.onomap.org

The world names project
www.publicprofiler.org/worldnames

26 Mar 2009 - Statistics - UoA

What's in a (Kiwi) name? ‐ David O'Sullivan

32/29

16

26 Mar 2009 - Statistics - UoA

What's in a (Kiwi) name? ‐ David O'Sullivan

33/29

Ethnic group distribution• E.g. Turkish names in Germany17

Lack of ethnicity data in Europe• Incompatible definitions and measurements:– Migrant / Native– Foreigner / National– Migrant ancestry– Race / Skin colour– Cultural / National identity• Lack of ethnicity data collection, or poor quality andcomparability• Lack of sufficient geographical granularity• Low frequency of update• Need for complementary methodologies to study
segregation– There is a fragmented, nation-specific research perspective on
spatial segregation in Europe (Musterd, 2005)

Selection of Cities• Selection criteria– Largest cities ( population >400,000)– City defined as:• UK: Districts– (except London = GOR)

• Rest: ‘Phone Book Cities’– (except Antwerp & Barcelona = Metropolitan Area)– 8 cities left out due to geocoding or data quality issues• Final list of cities:– 31 Cities– 9 Countries (AT, BE, DE, DK, ES, FR, IT, NL, UK)• Geographical units of analysis:– UK: Wards– Rest: Postcodes/Postal Areas (as in telephone directories)

18

Cities Summary
Country
Austria
Belgium
Belgium
Denmark
FranceFranceFranceFranceGermany
Germany
Germany
Germany
Germany
Germany
Germany
Italy
Italy
Italy
Netherlands
Spain
Spain
Spain
Spain
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom

Code
AT
BE
BE
DK
FR
FR
FR
FR
DE
DE
DE
DE
DE
DE
DE
IT
IT
IT
NL
ES
ES
ES
ES
UK
UK
UK
UK
UK
UK
UK
UK

City
VIENNAANTWERP REGION
BRUSSELSCOPENHAGEN
LYON
MARSEILLEPARISTOULOUSEBERLINBREMENDRESDENFRANKFURTHAMBURG
HANNOVER
STUTTGARTNAPOLIPALERMOROMA
AMSTERDAM
BARCELONA METROMADRID
SEVILLEVALENCIABIRMINGHAM
GLASGOWLEEDSLIVERPOOLLONDON
MANCHESTERNEWCASTLESHEFFIELD%
City_Pop Study_Pop City_Pop Nr.Areas
1,651,365
561,20534%
23
1,547,776
599,84539%
112
1,031,215
329,94932%
44503,699
294,18458%
129
466,400
165,01435%
9820,900
226,74828%
16
2,153,600
868,25340%
21435,000
142,775
33%
63,395,189
1,078,206
32%
341
546,852
212,41039%74495,181
145,14629%
47667,468
261,36339%
142
1,743,627
645,63237%
249
515,729
200,87739%
67
592,569
235,24140%
109984,242225,08623%
26
670,820
159,80724%
29
2 547 677
2,547,677
788 252
788,25231%
79
743,070
190,80826%
60
2,805,602
895,686
32%
80
3,128,600
805,76626%
53
704,414168,27524%
20
605,304
121,26020%
24
994,900
739,13674%
40
632,000
485,47477%
21
741,543553,39075%
33
460,500
348,40576%
30
7,517,700
5,277,443
70%
631
442,800
313,24171%
32
276,400
202,88173%
26
446,300
389,574
87%
28

Avg.
Avg.
Study_Pop
City_Pop
/Area
/Area
24,40071,7985,356
13,8197,499
23,437
2,280
3,905
18,33551,822
14,17251,306
41,345 102,552
23,79672,500
3,162
9,957
2,870
7,390
3,088
10,536
1,841
4,700
2,593
7,003
2,998
7,697
2,158
5,436
8,657
37,855
5,511
23,1329 9789,97832 24932,2493,180
12,385
11,19635,07015,20359,0308,414
35,221
5,053
25,221
18,47824,873
23,11830,095
16,76922,471
11,614
15,3508,364
11,914
9,789
13,838
7,803
10,631
13,913
15,939European cities segregation analysis• Names from telephone directories coded by
Onomap Subgroup• 31 largest cities selected• Counts per Onomap Group and small area
(postcodes) calculated within each city
• Index of Dissimilarity calculated for each cityD

1 n xi ti  xi 
2 i 1 X T  X(Duncan and Duncan, 1955)

xi = Population of group X in area i
X = Total population of group X in the cityti = Total population of area i
T = Total population of the city19

Precautions taken• To prevent small groups driving results• Analysis done at Onomap Group level (14 groups)
not Onomap Subgroup (80)• Presentation of results filtered, after ID was
calculated:– Remove Onomap Groups with average population perareaaea in a ccity
ty < 15
5 individuals
d dua s– Remove Onomap Groups with ID <0.1

Results: Index of Dissimilarity per City
DE - BERLIN
IT - NAPOLI 0.60
ES - SEVILLA
DE - HANNOVER

UK - LEEDS
UK - LONDON

0.50

UK - BIRMINGHAM

0 40
0.40

DE - STUTTGART

ES - BARCELONA

0.30

FR - TOULOUSE

UK - SHEFFIELDAFRICANANGLO-SAXONCELTIC

0.20

DK - COPENHAGEN

UK - NEWCASTLE

EAST ASIAN & PACIFIC

UK - LIVERPOOLEUROPEAN_OTHER
EASTERNEUROPEAN_OTHERWESTERNHISPANIC

0.10
ES - MADRID

0.00

IT - ROMA

UK - GLASGOW

NL - AMSTERDAM

UK - MANCHESTERJAPANESEJEWISHMUSLIM

AT - WIEN

BE - BRUSSELSNORDICSOUTH ASIAN

DE - FRANKFURT
ES - VALENCIA
FR - MARSEILLE
DE - BREMEN

BE - ANTWERPEN
DE - HAMBURG
FR - PARIS
FR - LYON

20

DE BERLIN
UK LEEDS
UK LONDONUK BIRMINGHAM
ES BARCELONAUK SHEFFIELD
UK NEWCASTLE
UK LIVERPOOLUK GLASGOW
UK MANCHESTER
BE BRUSSELS
BE ANTWERPEN
DE HAMBURG
FR PARISO
FR LYON
DE BREMEN
FR MARSEILLEES VALENCIADE FRANKFURTAT WIENNL AMSTERDAM
IT ROMA
ES MADRIDDK COPENHAGENFR TOULOUSE
DE STUTTGARTDE HANNOVER
ES SEVILLA
IT NAPOLI

AF
R
IC
AN
AN
G
LO
C
EL SA
XO
TI
C
N
EA
ST
EU ASIANRO&PAEU PEA
C
N
R
IF
_O
O
P
IC
T
EA
H
H
IS
PA N_ ER
O
E
N
TH
A
JA
IC
ER STE
PA
R
N
W
JE ES
ES N
E
W
TE
IS
R
H
N
M
U
SL
IM
N
O
RD
SO IC
U
TH
AS
IA
N
AV
G

Y
C
IT

C
O

UN
TR

Y_
C
O

D
E

Results

0 37
0.37
0.30 0.22 0.22 0.19
0.50 0.54
0.24 0.23 0.24 0.24
0.48 0.35 0.32
0.29 0.14 0.17 0.18
0.27 0.63
0.31 0.29
0.43 0.14 0.23 0.14
0.63
0.33 0.20 0.25 0.24
0.47
0.24 0.24 0.15 0.19
0.36 0.41 0.14
0.30 0.16 0.14 0.13
0.30 0.50 0.27
0.16
0.30 0.20 0.19 0.18
0.36 0.47
0.19 0.17
0.13 0.42 0.20 0.27 0.30
0.14
0.12
0.20
0.43 0.17
0.11
0.25
0.20
0.23
0.13 0.12
0.12
0
0.25
0
5
0.15
0.13
0.22
0.11 0.15 0.11 0.13
0.14 0.33
0.17
0.17
0.34
0.18 0.11
0.15 0.12
0.13 0.27
0.16 0.17
0.18 0.21
0.15
0.10
0.12
0.20
0.14
0.10 0.22 0.16
0.14
0.17
0.11
0.20
0.14
0.11

0.46 0.17
0.42 0.23
0.36 0.29
0.32
0.19
0.49
0.38
0.34
0.16

0.31
0.18
0.24

0.11

0.17

0.35
0.48
0.45
0.32
0.26
0.34
0.37
0.33
0.12

0.10

0.10

0.10

Index of
Dissimilarity
(D)

03
0.37
0.33
0.32
0.31
0.31
0.30
0.29
0.28
0.28
0.28
0.22
0.21
0.18
0.18
0.18
0.17
0.17
0.17
0.17
0.17
0.17
0.16
0.16
0.16
0.16
0.15
0.15
0.13
0.10

Key
<=0.300.31-0.4
0.41-0.6>=0.6

Conclusions: European Segregation• UK cities much more segregated than rest of countries– could be a result of finer small area geography– followed by cities in Belgium and France• Muslim group most segregated across Europe– followed by South Asian, and African– at larger distance by Japanese, Jewish & East Asian• Issues:– High variability between cities: population sizes,
sizes extent and number ofareas– Differential population coverage (tel. dir. & elec. roll)– Onomap Group level may not reflect the full reality in each country(i.e. historic minority groups)– Dissimilarity index on its own not ideal (Simpson, 2005; Massey &Denton, 1988)

21

Conclusions: Onomap methodology• Advantages
Facilitates ethnicity analysis using finer spatial, temporal, andnominal granularity
Cost-efficient alternative when ethnicity data is missing/ low quality
Ethnicity categories can be re-aggregated in different ways
Probability scores; tailor classification to specific applications• Disadvantages
Only reflects patrilineal heritage (problem of mixed ethnicity)
Diff
Differentt hihistoriest i off surname adoption,d tinamingi conventionsti& namechange rules in each language and country Name normalisation decisions are required Publicly available registers of names have biases
 Not appropriate for reporting ethnicity at individual level
 Ethical considerations and privacy issues
5- Conclusions

Future research: European cities• Adapt Onomap groupings to reflect most common
ethnic ggroupsp across Europep• Improve geocoding, definition of small areas andcity boundaries• Calculate several segregation indices22

New Zealand: Scottish names

New Zealand: Māori names

23

Auckland, N: Pasifika names

26 Mar 2009 - Statistics - UoA

What's in a (Kiwi) name? ‐ David O'Sullivan

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New Zealand: ‘Eastern European’
names

24

References
•
•

•

•
•
•
•
•

Mateos P (2007) 'A Review of Name-based Ethnicity Classification Methods and theirPotential in Population Studies', Population Space and Place, 13(4): 243-263
Mateos P, Webber R, Longley PA (2007) The Cultural, Ethnic and Linguistic
Classification of Populations and Neighbourhoods using Personal Names. CASA
Working Paper 116. Rep. ISSN 1467-1298, Centre for Advanced Spatial Analysis.
University College London. London. Available at:
http://www.casa.ucl.ac.uk/working_papers/paper116.pdf.
Mateos P, Singleton A, Longley PA (2009) 'Uncertainty in the analysis of ethnicity
classifications: some issues of extent and aggregation of ethnic groups', Journal of
Ethnic and Migration Studies, 35(9): (in press)
Musterd S (2005) 'Social and Ethnic Segregation in Europe: Levels, Causes, andEffects', Journal of Urban Affairs, 27(3): 331-348
Musterd S (2008) Segregation, Integration and Neighbourhood Effects, paper presented
at CCSR Seminar on Segregation, University of Manchester, June 3
Peach, C (ed) (1996) Ethnicity in the 1991 Census. Vol 2: Ethnic minority populations ofGreat Britain HMSO, London
Peach C (2006) 'Islam, ethnicity and South Asian religions in the London 2001 census',
Transactions of the Institute of British Geographers, 31(3): 353-370
Phillips D. (2007). Ethnic and Racial Segregation: A Critical Perspective. Geography
Compass 1(5): 1138-1159 [paper]Thanks for listening
Pablo Mateos
p.mateos@ucl.ac.uk

www.onomap.org
www.publicprofiler.org/worldnames
www londonprofiler org
www.londonprofiler.org

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