Towards a Higher Education Profiler Alex Singleton, Paul Longley, Alan Wilson

Introduction Views on the transition from PhD to Post Doctorate ESRC First Grant Scheme Some new data sources Some new insights Beta Educational Profiler From description to prediction

Views on the transition from PhD to Post Doctorate

ESRC First Grant Scheme

Some new data sources

Some new insights

Beta Educational Profiler

From description to prediction

My transition from an unconventional PhD an unconventional Post Doc A Spatio-Temporal Analysis of Access to Higher Education (aprox. 1 year ago) Three Themes Momentum Unfinished Business Future Directions Unconventional PhD – Spent 2 years in Cheltenham at UCAS – KTP Post-Doc – It isn’t really one

A Spatio-Temporal Analysis of Access to Higher Education (aprox. 1 year ago)

Three Themes

Momentum

Unfinished Business

Future Directions

Unconventional

PhD – Spent 2 years in Cheltenham at UCAS – KTP

Post-Doc – It isn’t really one

Momentum Keep it up – you are used to writing lots Use this to write papers, grant / book proposals Disillusion with PhD topic Good to put it down for a while and do something else 2 Papers on E-Society Online validation Digital deprivation V material deprivation 2 Papers on Neo-Geography

Keep it up – you are used to writing lots

Use this to write papers, grant / book proposals

Disillusion with PhD topic

Good to put it down for a while and do something else

2 Papers on E-Society

Online validation

Digital deprivation V material deprivation

2 Papers on Neo-Geography

Unfinished Business There will be things in your thesis which you wanted to cover but didn’t have time / room Methodological Alternate algorithms to k- means in creation geodemographics Geographic representations of cluster instability – related to initial seed locations Alternate optimisation procedures –measures of spatial rather than social similarity Domain Specific Course Clusters Overarching Themes Future of area classification Real Time Geodemographics

There will be things in your thesis which you wanted to cover but didn’t have time / room

Methodological

Alternate algorithms to k- means in creation geodemographics

Geographic representations of cluster instability – related to initial seed locations

Alternate optimisation procedures –measures of spatial rather than social similarity

Domain Specific

Course Clusters

Overarching Themes

Future of area classification

Real Time Geodemographics

Future Directions Like it or not, your PhD is what you are known for! Chart hits matter: Brunsdon = GWR Dorling = Catograms Unless you start again, you will always return to you PhD themes It is what you know most about! Research is driven by funding Funding for PhD students Funding for research grants Building a research team enables You to do more Efforts shift from “doing” to “guiding” / “organising” Don’t drown in this – you still need to keep “doing” Doctoral Post - Doctoral Academics RA

Like it or not, your PhD is what you are known for!

Chart hits matter:

Brunsdon = GWR

Dorling = Catograms

Unless you start again, you will always return to you PhD themes

It is what you know most about!

Research is driven by funding

Funding for PhD students

Funding for research grants

Building a research team enables

You to do more

Efforts shift from “doing” to “guiding” / “organising”

Don’t drown in this – you still need to keep “doing”

ESRC First Grants Scheme: Enables early career researchers to apply for a small grant essentially 1 researcher @ FEC funding + expenses Very competitive Over 200 applications last year ~13% success Spatial interaction modelling, geodemographics and widening participation in the Higher Education sector?

Scheme: Enables early career researchers to apply for a small grant

essentially 1 researcher @ FEC funding + expenses

Very competitive

Over 200 applications last year

~13% success

Spatial interaction modelling, geodemographics and widening participation in the Higher Education sector?

Start Oct. 2008 End Sept. 2009 Feb2009 Time Plan Approximately ¼ Way Through Stage 1: Data Acquisition and Insight Stage 2: Higher Education Profiler Stage 3: Higher Education Modeller Pet Project....(Facebook)

The HE Problem: Acceptances 1962 - 2003

Occupational Group: 1968-1978

Social Class: 1980 - 2001

Socio-Economic Group: 2002 - 2007

End point of my thesis Investigated a variety of different aspects of HE participation, from a geodemographics / geographers perspective

Distance travelled to HE by Mosaic

HE Acceptances Mosaic Profile– UCAS Acceptances – 2004 (Base All Adults)

School Profiles – Selective Schools 2006 PLASC Data - DCSF

GCSE Grades 2006 PLASC Data - DCSF

School Catchment Areas

Stage 1: Data Acquisition and Insight Data Sources University and Colleges Admissions Service (UCAS) Higher Education Statistics Agency (HESA) Department for Children Schools and Families (DCSF) A-Level & Equiv (Key Stage 5) GCSE & Equiv (Key Stage 4) DCSF & HESA now link at individual level Map a student through time! Previously – had to consider each key stage separately Caveat – These data only arrived last week!

Data Sources

University and Colleges Admissions Service (UCAS)

Higher Education Statistics Agency (HESA)

Department for Children Schools and Families (DCSF)

A-Level & Equiv (Key Stage 5)

GCSE & Equiv (Key Stage 4)

DCSF & HESA now link at individual level

Map a student through time!

Previously – had to consider each key stage separately

Insight 1: Entry Rates (DCFS & HESA) DCSF Key Stage 5 HESA (0) HESA (+1) HESA (+2) Direct Entry Gap Year Gap Years National Targets = 18-30 Age Range 2004 ~50% ~20% ~5%

Insight 2: Course Choice Behaviour (UCAS) Applicant C1 C2 C3 C4 C5 C6

Percentages – Row ∑100% Thus, for applicants with at least one choice in “A1 - Pre-Clinical Medicine”, 76.9% of applications from other applicants are within the same JACS Line. A1 is quite homogeneous! Extract of the full table

Insight 3: Model of Private Characteristics State / KS5 FE Colleges Private Demographics – inc. spatial reference x Higher Education (HESA)

Insight 4: Participation Flows (based on HESA data)

Stage 2: Higher Education Profiler Integrate insights from my thesis UK HE Atlas Platform for decision support for a range of stakeholders in HE

Integrate insights from my thesis

UK HE Atlas

Platform for decision support for a range of stakeholders in HE

Map Generation – Dependent Solution Issues – Dependent on Google API, Limited to Google Cartography, potential issues with data ownership

Original Site (Google)

Map Generation – Independent Solution Shuttle Radar Topography Mission ( SRTM ) ~100m resolution

Architecture Diagram OpenStreetMap (via Cloudmade) great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB PublicProfiler Schools Atlas DCSF.gov.uk EduBase and IDACI OpenLayers (.js) HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading AJAX Requests OSM Tiles PVCs (.kml) OAC Hawths Tools ArcGIS Google Chart API Tiles Chart Cache

Network Layer great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql Mapnik OSM Tiling Script (.py) Stylesheets(.xml) OpenStreetMap (via Cloudmade) Shapefiles Tiles PublicProfiler Schools Atlas

Hillshading Layer great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools Mapnik OSM Tiling Script (.py) Stylesheets(.xml) PerryGeo Hillshading Shapefiles PublicProfiler Schools Atlas Tiles OpenStreetMap (via Cloudmade)

Choropleth Layers great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading PublicProfiler Schools Atlas Tiles OpenStreetMap (via Cloudmade)

School Statistics great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB DCSF.gov.uk EduBase and IDACI HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading OAC PublicProfiler Schools Atlas Tiles OpenStreetMap (via Cloudmade)

School Catchment Areas great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB DCSF.gov.uk EduBase and IDACI HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading PVCs (.kml) OAC Hawths Tools ArcGIS PublicProfiler Schools Atlas Tiles OpenStreetMap (via Cloudmade)

Serving Tiles great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB DCSF.gov.uk EduBase and IDACI HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading PVCs (.kml) OAC Hawths Tools ArcGIS PublicProfiler Schools Atlas Tiles OpenLayers (.js) OpenStreetMap (via Cloudmade)

Other Tile Sources great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB DCSF.gov.uk EduBase and IDACI HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading PVCs (.kml) OAC Hawths Tools ArcGIS PublicProfiler Schools Atlas Tiles OSM Tiles OpenLayers (.js) OpenStreetMap (via Cloudmade)

Showing the Statistics great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB DCSF.gov.uk EduBase and IDACI HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading AJAX Requests PVCs (.kml) OAC Hawths Tools ArcGIS Google Chart API Chart Cache PublicProfiler Schools Atlas Tiles OSM Tiles OpenLayers (.js) OpenStreetMap (via Cloudmade)

Showing Catchments great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB DCSF.gov.uk EduBase and IDACI HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading AJAX Requests PVCs (.kml) OAC Hawths Tools ArcGIS Google Chart API Chart Cache OSM Tiles Tiles OpenLayers (.js) PublicProfiler Schools Atlas OpenStreetMap (via Cloudmade)

Production Systems great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql NASA’s SRTM DEMs GDAL Tools UKBorders English MSOAs and Postcodes DCSF. gov.uk National Pupil Database mySQL DB DCSF.gov.uk EduBase and IDACI HEFCE.ac.uk POLAR OAC Mapnik Shapefiles OSM Tiling Script (.py) Stylesheets(.xml) ArcGIS Color Brewer PerryGeo Hillshading AJAX Requests PVCs (.kml) OAC Hawths Tools ArcGIS Google Chart API Chart Cache PublicProfiler Schools Atlas OSM Tiles OpenLayers (.js) Tiles OpenStreetMap (via Cloudmade)

Stage 3: Higher Education Modeller m : geodemographic group i : area of residence j : university (or university location) a : attainment level n : school type x : subject group h : university type Potential student groupings

m : geodemographic group

i : area of residence

j : university (or university location)

a : attainment level

n : school type

x : subject group

h : university type

The flow array We write the array of interest as S ij (m, a, n, x, h) on the basis that we are always going to want to model S ij with some subset of (m, a, n, x, h). The challenge arises from the number of cells in this 7-dimensional array.

We write the array of interest as

S ij (m, a, n, x, h)

on the basis that we are always going to want to model S ij with some subset of (m, a, n, x, h).

The challenge arises from the number of cells in this 7-dimensional array.

Numbers in each category m : 7 (Output Area Classification) i : 30 (NUTS2 areas) j : 171 HEIs; may be reduced to 30 locations a : 3 attainment levels; or a continuum of UCAS points n : ideally 5 school/college types: independent, state selective, state non-selective, Sixth Form College, FE College; reduce to 3? x : 8, or 4? h : 5 – Oxbridge/UCL/Imperial, major civic, other research, large other, small other; or reduce to 3?

m : 7 (Output Area Classification)

i : 30 (NUTS2 areas)

j : 171 HEIs; may be reduced to 30 locations

a : 3 attainment levels; or a continuum of UCAS points

n : ideally 5 school/college types: independent, state selective, state non-selective, Sixth Form College, FE College; reduce to 3?

x : 8, or 4?

h : 5 – Oxbridge/UCL/Imperial, major civic, other research, large other, small other; or reduce to 3?

Flow array cells If we take the largest suggested numbers, the number of cells in the array would be: 7x30x171x3x5x8x5 = 21,546,000 which is a ludicrously large number given that we are handling roughly 500,000 students in a year. Most of the cells would have zero entries.

If we take the largest suggested numbers, the number of cells in the array would be:

7x30x171x3x5x8x5 = 21,546,000

which is a ludicrously large number given that we are handling roughly 500,000 students in a year. Most of the cells would have zero entries.

Revised flow array cells If we take the lower category numbers, we get 7x30x30x3x3x4x3 = 680,400 which is still too large. It is useful to look at this as 30x30 = 900 geographic dimensions and 7x3x3x4x3 = 756 other dimensions. (900x756 = 680,400)

If we take the lower category numbers, we get

7x30x30x3x3x4x3 = 680,400

which is still too large. It is useful to look at this as 30x30 = 900 geographic dimensions and 7x3x3x4x3 = 756 other dimensions. (900x756 = 680,400)

Visualising the real data The next step is to visualise the data to guide us towards further aggregation. School type Output Area Classification Attainment University NUTS2 Area Flow of student(s) Height = flow size

The next step is to visualise the data to guide us towards further aggregation.

Comparing London Universities UCL (Flow size > 2 students) London Metropolitan University (Flow size > 2 students)

UCL

(Flow size > 2 students)

London Metropolitan University

(Flow size > 2 students)

Comparing Leeds Universities University of Leeds (Flow size > 4 students) Leeds Metropolitan University (Flow size > 4 students)

University of Leeds

(Flow size > 4 students)

Leeds Metropolitan University

(Flow size > 4 students)

OAC: Constrained By Circumstances (Flow size > 5 students )

OAC: City Living (Flow size > 5 students) London to University of Oxford

All flows out of Cornwall (Flow size > 5 students)

Model Equation The model would take the form: S ij (m, a, n, x, h) = A i (m, a, n, x, h)B j (a, x, h)e i (m, a, n, x, h)O i (m)D j (a, n, x, h)exp[-β(m)c ij (m)] This conceptual model would suffer from having too many cells, but we will use the experience of examining the data to find ways of aggregating. Initially, the model would be run on a doubly constrained basis for calibration purposes. It would then be possible to replace D j (a, n, x, h) by a set of attractiveness factors, W j (a, n, x, h). This would provide a ‘What if?’ capability. The model could then be used to test various future policy options.

The model would take the form:

S ij (m, a, n, x, h)

= A i (m, a, n, x, h)B j (a, x, h)e i (m, a, n, x, h)O i (m)D j (a, n, x, h)exp[-β(m)c ij (m)]

This conceptual model would suffer from having too many cells, but we will use the experience of examining the data to find ways of aggregating.

Initially, the model would be run on a doubly constrained basis for calibration purposes.

It would then be possible to replace D j (a, n, x, h) by a set of attractiveness factors, W j (a, n, x, h). This would provide a ‘What if?’ capability. The model could then be used to test various future policy options.