Geographical Ways of looking at segregation

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Geographical Ways oflooking at segregation

• Rich Harris, University of Bristol, UK
• School of Geographical Sciences Centre for
  Market and Public Organisation

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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/

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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

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School choice Social segregation?
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Ethnic polarization?
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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

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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)?

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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

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• 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

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• Continues until a certain proportion of all
  pupils who attend the school are enclosed
• p 0.30

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• Continues until a certain proportion of all
  pupils who attend the school are enclosed
• p 0.40

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• Continues until a certain proportion of all
  pupils who attend the school are enclosed
• p 0.50
• Catchment is then defined as the convex hull for
  pupils of school within the search area.

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London
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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

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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

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Defining Near

• Define as being near to a pupil any primary
  school that has a core catchment that includes
  the pupils residential postcode
• Here the pupil has three near schools

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Proportion attending any near school(target
catchment p0.50) LONDON
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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

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Pairwise Comparisons

• Look inside the catchments
• Expected intake VsActual ethnic profile of each
  school
• Compare the profiles of locally competing
  schools
• ones that overlap (strongly) in terms of their
  core catchment areas

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Visual Summary (LONDON)

• Consider those schools with highest expected
  Black Caribbean

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Visual Summary (LONDON)

• Consider those schools with highest expected
  Bangladeshi

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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

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Example
Data Numerator/Denominator Source
Y Higher education participation Successful entrants under 21 in UCAS data, for 20022005/ Census population 1417 2007 Index of Multiple Deprivation
X1 No qualifications Adults aged 2554 in the area with no qualifications or with qualifications below NVQ Level 2, for 2001 /All adults aged 2554. 2007 Index of Multiple Deprivation
X2 No post 16 qualifications Those aged 17 still receiving Child Benefit in 2006/ Those aged 15 receiving Child Benefit in 2004. 2007 Index of Multiple 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. 2007 Index of Multiple Deprivation
X4 Four or more cars Four or more cars in household / total households 2001 Census
X5 Asian Total Indian, Pakistani, Bangladeshi people / total people 2001 Census
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Example
Data Numerator/Denominator Source
Y Higher education participation Successful entrants under 21 in UCAS data, for 20022005/ Census population 1417 2007 Index of Multiple Deprivation
X1 No qualifications Adults aged 2554 in the area with no qualifications or with qualifications below NVQ Level 2, for 2001 /All adults aged 2554. 2007 Index of Multiple Deprivation
X2 No post 16 qualifications Those aged 17 still receiving Child Benefit in 2006/ Those aged 15 receiving Child Benefit in 2004. 2007 Index of Multiple 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. 2007 Index of Multiple Deprivation
X4 Four or more cars Four or more cars in household / total households 2001 Census
X5 Asian Total Indian, Pakistani, Bangladeshi people / total people 2001 Census
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Global regression model (n 31 378 )
ß Standard error t value Significant at a0.01?
(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
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But Geographical variation in theAsian
coefficient
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Geographically Weighted Regression

• What is it?
• Extension of regression model
• Allows model to vary over space
• How it works...

Regression Point
Data Points
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Summary of GWR model
ß(globalvalue) ß (u,v) Min ß (u,v) 1stdecile ß (u,v) 3rddecile ß (u,v) Median ß (u,v) 7thdecile ß (u,v) 9thdecile ß (u,v) Max. ß (u,v) IQR
(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 cars 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
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Geographical variation in theAsian coefficient
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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

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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/

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Thank you!