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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
• 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
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
• Continues until a certain proportion of all pupils who attend the school are enclosed…
• p = 0.40
• 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.
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
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
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
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
• 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
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.
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
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.
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
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
β
(global
value)
β (u,v)
Min
β (u,v)
1st
decile
β (u,v)
3rd
decile
β (u,v)
Median
β (u,v)
7th
decile
β (u,v)
9th
decile
β (u,v)
Max.
β (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
Geographical variation in the“Asian” coefficient
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
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!