Towards a Higher Education Profiler

University College London, London - 11/2/09

Transcript

1. Towards a Higher Education Profiler Alex Singleton, Paul Longley, Alan Wilson
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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?
8. 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)
9. The HE Problem: Acceptances 1962 - 2003

10. Occupational Group: 1968-1978
11. Social Class: 1980 - 2001
12. Socio-Economic Group: 2002 - 2007
13. End point of my thesis Investigated a variety of different aspects of HE participation, from a geodemographics / geographers perspective
14. Distance travelled to HE by Mosaic
15. Mosaic Profile– UCAS Acceptances – 2004 (Base All Adult HE Acceptances
16. 2006 PLASC Data - DCSF School Profiles – Selective Schools
17. 2006 PLASC Data - DCSF GCSE Grades
18. School Catchment Areas
19. 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!
20. Insight 1: Entry Rates (DCFS & HESA) DCSF Key Stage 5 HESA (0) HESA (+1) HESA (+2) 2004 ~50% ~20% ~5% Direct Entry Gap Year Gap Years National Targets = 18-30 Age Range
21. Insight 2: Course Choice Behaviour (UCAS) Applicant C1 C2 C3 C4 C5 C6
24. 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
28. Insight 3: Model of Private Characteristics State / KS5 FE Colleges Private Demographics – inc.! spatial reference x Higher Education (HESA)
29. Insight 4: Participation Flows (based on HESA data)
30. Stage 2: Higher Education Profiler • Integrate insights from my thesis • UK HE Atlas • Platform for decision support for a range of stakeholders in HE
31. Map Generation – Dependent Solution Issues – Dependent on Google API, Limited to Google Cartography, potential issues with data ownership
32. Original Site (Google)
33. Map Generation – Independent Solution Shuttle Radar Topography Mission (SRTM) ~100m resolution
35. 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 Architecture Diagram
36. great_britain.osm (.xml) PostgreSQL DB with PostGIS osm2pgsql Mapnik OSM Tiling Script (.py) Stylesheets(. xml) OpenStreetMap (via Cloudmade) Shapefiles Tiles ! PublicProfiler Schools Atlas Network Layer
37. 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 Hillshading Layer ! PublicProfiler Schools Atlas Tiles OpenStreetMap (via Cloudmade)
38. 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 Choropleth Layers Tiles OpenStreetMap (via Cloudmade)
39. 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 School Statistics ! PublicProfiler Schools Atlas Tiles OpenStreetMap (via Cloudmade)
40. 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 School Catchment ! PublicProfiler Schools Atlas Tiles OpenStreetMap (via Cloudmade)
41. 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 Serving Tiles ! PublicProfiler Schools Atlas Tiles OpenLayers (.js) OpenStreetMap (via Cloudmade)
42. 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 Other Tile Sources ! PublicProfiler Schools Atlas Tiles OSM Tiles OpenLayers (.js) OpenStreetMap (via Cloudmade)
43. 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) Showing the Statistics OpenStreetMap (via Cloudmade)
44. 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 Showing Catchments OpenStreetMap (via Cloudmade)
45. 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 Production Systems OpenStreetMap (via Cloudmade)
48. 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
49. 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.
50. 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?
51. 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.
52. 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)
53. 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
54. Comparing London Universities UCL (Flow size > 2 students) London Metropolitan University (Flow size > 2 students)
55. Comparing Leeds Universities University of Leeds (Flow size > 4 students) Leeds Metropolitan University (Flow size > 4 students)
56. OAC: Constrained By Circumstances 
 (Flow size > 5 students )
57. OAC: City Living 
 (Flow size > 5 students) London to University of Oxford
58. All flows out of Cornwall 
 (Flow size > 5 students)
59. Model Equation • The model would take the form: ! Sij (m, a, n, x, h) ! = Ai (m, a, n, x, h)Bj (a, x, h)ei (m, a, n, x, h)Oi (m)Dj (a, n, x, h)exp[- β(m)cij (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 Dj (a, n, x, h) by a set of attractiveness factors, Wj (a, n, x, h). This would provide a ‘What if?’ capability. The model could then be used to test various future policy options.