Geographical Ways of looking at segregation Rich Harris, University of Bristol, UK School of...

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Geographical Ways oflooking at segregationGeographical Ways oflooking at segregation

Rich Harris, University of Bristol, UKSchool of Geographical Sciences &

Centre for Market and Public Organisation

Rich Harris, University of Bristol, UKSchool of Geographical Sciences &

Centre for Market and Public Organisation

Outline

• Focus on two opportunities• Modelling micro data geographically

– Mapping school catchment areas to identify polarization

• Building geographical models– Example of Geographically Weighted Regression

• Common framework for analysis– “R”

• Open source software for computing and statistics• http://cran.r-project.org/

Outline

• Common framework for analysis

School “choice” & Social segregation?

Ethnic polarization?

Geographical perspective

• Economic theory and government policy suggest schools operate within local markets to attract pupils and funding.

• However, there is a deficit of understanding about the scales and configurations of those admission spaces.

• Whilst competition for pupils and for school places is assumed to operate at some localised scale, the actual geographies of the markets, where they overlap and where they might be changing are generally unknown.

• Aim: To understand processes of polarization in the context of the local markets within which schools operate.

• Task: To use micro-data to model those markets

The data

• PLASC– Pupil Level Annual Census Returns

• Data on all pupils in primary (and secondary) schools in England

• 2005/6 data– Information on state educated primary school students (5-

11 years old)– 'Self-identified' ethnic category collected from parents

when students enrol– Also records postcode unit of pupils' homes– Which they school they attend– School type (selective? Faith school?)– Measure of deprivation (take a free school meal)?

Defining ‘core catchments’

• Imagine centring a polygon at (mid-x, mid-y) based on the residential postcodes of pupils attending a school– Let the polygon grow

outwards

• The direction of growth is determined as that which returns highest n1 / n2– where n1 is number of

pupils in area going to the school

– n2 is all pupils in the area (go to any school)

– Measuring prevalence

• Continues until a certain proportion of all pupils who attend the school are enclosed…

• p = 0.30

• p = 0.40

• p = 0.50• Catchment is then defined

as the convex hull for pupils of school within the search area.

London

Evidence of polarisation

• Are particular social (ethnic) groups travelling further to school ‘than they need to’?

• Are there (primary) schools with an intake not representative of the local community?

• Are there (primary) schools with shared admission spaces but where one has a very different intake to the other?

• Study region: London

Defining ‘Near’

• Define as being near to a pupil any primary school that has a core catchment that includes the pupil’s residential postcode

• Here the pupil has three near schools

Proportion attending any near school(target catchment p=0.50) LONDON

Pairwise Comparisons

• Look inside the catchments– Expected intake Vs

Actual ethnic profile of each school

• Compare the profiles of locally ‘competing schools’– ones that overlap (strongly) in terms of their core

catchment areas

Visual Summary (LONDON)

• Consider those schools with highest expected % Black Caribbean

Visual Summary (LONDON)

• Consider those schools with highest expected % Bangladeshi

Outline

Example

Data Numerator/Denominator Source

Y Higher education

participation

Successful entrants under 21 in UCAS data, for 2002–2005/

Census population 14–17

2007 Index of Multiple

Deprivation

X1 No qualifications Adults aged 25–54 in the area with no qualifications or with

qualifications below NVQ Level 2, for 2001 /All adults aged

25–54.

Deprivation

X2 No post 16

qualifications

Those aged 17 still receiving Child Benefit in 2006/ Those aged

15 receiving Child Benefit in 2004.

Deprivation

X3 Average KS4

Points

Total score of pupils taking KS4 in 2004 and 2005 in

maintained schools from the NPD / All pupils in their final year

of compulsory schooling in maintained schools for 2004 and

2005 from PLASC.

Deprivation

X4 Four or more

Four or more cars in household / total households 2001 Census

X5 Asian Total Indian, Pakistani, Bangladeshi people / total people 2001 Census

Example

Data Numerator/Denominator Source

Y Higher education

participation

Successful entrants under 21 in UCAS data, for 2002–2005/

Census population 14–17

Deprivation

X1 No qualifications Adults aged 25–54 in the area with no qualifications or with

qualifications below NVQ Level 2, for 2001 /All adults aged

25–54.

Deprivation

X2 No post 16

qualifications

Those aged 17 still receiving Child Benefit in 2006/ Those aged

15 receiving Child Benefit in 2004.

Deprivation

X3 Average KS4

Points

Total score of pupils taking KS4 in 2004 and 2005 in

maintained schools from the NPD / All pupils in their final year

of compulsory schooling in maintained schools for 2004 and

2005 from PLASC.

Deprivation

X4 Four or more

Four or more cars in household / total households 2001 Census

X5 Asian Total Indian, Pakistani, Bangladeshi people / total people 2001 Census

Global regression model (n = 31 378 )

β Standard error t value Significan

t at α0.01?

(Intercept) 3.620 0.0213 170.2 Yes

X1: No Qualifications -0.027 0.0002 -152.5 Yes

X2: No Post 16 Qualifications -0.002 0.0001 -15.1 Yes

X3: Average KS4 attainment 0.003 0.0002 52.6 Yes

X4: Four or more cars 0.018 0.0005 35.9 Yes

X5: Asian 0.012 0.0002 68.1 Yes

But… Geographical variation in the“Asian” coefficient

• What is it?– Extension of

regression model– Allows model to vary

over space

• How it works...

Geographically Weighted Regression

Regression Point

Data Points

Summary of GWR model

(global

value)

β (u,v)

decile

β (u,v)

decile

β (u,v)

Median

β (u,v)

decile

β (u,v)

decile

β (u,v)

(Intercept) 3.620

X1: No

Qualifications

-0.027 -0.047 -0.036 -0.032 -0.030 -0.027 -0.023 -0.014 0.006

X2: No Post 16

Qualifications

-0.002 -0.008 -0.003 -0.002 -0.001 -0.001 0.000 0.005 0.002

X3: Average KS4

attainment

0.003 0.000 0.001 0.002 0.003 0.003 0.004 0.006 0.001

X4: Four or more

0.018 -0.013 0.011 0.016 0.021 0.027 0.040 0.101 0.014

X5: Asian 0.012 -0.156 -0.006 0.009 0.012 0.015 0.020 0.217 0.008

Geographical variation in the“Asian” coefficient

Outline

Framework for analysis

• “R”– Open source software

for statistical computing

– Available at CRAN• http://cran.r-project.org/

– WUN GIS Academy• eSeminars about

Spatial analysis in ‘R’• http://www.wun.ac.uk/

ggisa/

Thank you!Thank you!

Geographical Ways of looking at segregation Rich Harris, University of Bristol, UK School of...

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