Socio-Spatial Differentiation - People, Places and Interaction

This is a summary talk of my current and future research.

Transcript

1. Socio-Spatial Differentiation - People, Places and Interaction Dr Alex Singleton University College London www.alex-singleton.com
2. Me • 2000-03: Geography Degree – University of Manchester – Physical Geography / GIS – Dissertation – ‘Where do Manchester University Students com from?’ • 2003-2005 - KTP – UCAS / UCL – Based in Cheltenham (CASA 1 day ever other week) • 2005-this week! – SPLINT / CETL – HEFCE funded project: Nottingham / Leicester – PhD – Nov 2007
3. All publication titles and abstracts - to July 2010
4. Predicting Participation in Higher Education: a Comparative Evaluation of the Performance of Geodemographic Classifications Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases Grid-Enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England Course Choice Behaviour and Target Marketing of Higher Education Creating Open Source Geodemographics: Refining a National Classification of Census Output Areas for Applications in Higher Education Geodemographics, Visualization, and Social Networks in Applied Geography Classification through Consultation: Public Views of the Geography of the e- Society. Web Mapping 2.0: the Neogeography of the Geospatial Internet. Exploratory Cartographic Visualisation of London using the Google Maps API Lost in translation? Cross-Cultural Experiences in Teaching Geo-Genealogy Uncertainty in the Analysis of Ethnicity Classifications. Issues of Size, Scale and Aggregation of Groups The Geodemographics of Educational Progression and their Implications for Widening Participation in Higher Education Linking Social Deprivation and Digital Exclusion in England United Kingdom Surname Clusters
5. Domains Higher Education Digital Exclusion GIS and Neogeography Geo-Genealogy
6. Predicting Participation in Higher Education: a Comparative Evaluation of the Performance of Geodemographic Classifications Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases Grid-Enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England Course Choice Behaviour and Target Marketing of Higher Education Creating Open Source Geodemographics: Refining a National Classification of Census Output Areas for Applications in Higher Education Geodemographics, Visualization, and Social Networks in Applied Geography Classification through Consultation: Public Views of the Geography of the e- Society. Web Mapping 2.0: the Neogeography of the Geospatial Internet. Exploratory Cartographic Visualisation of London using the Google Maps API Lost in translation? Cross-Cultural Experiences in Teaching Geo-Genealogy Uncertainty in the Analysis of Ethnicity Classifications. Issues of Size, Scale and Aggregation of Groups The Geodemographics of Educational Progression and their Implications for Widening Participation in Higher Education Linking Social Deprivation and Digital Exclusion in England United Kingdom Surname Clusters
7. Methods Geodemographics Geoweb / Visualisation Geocomputation Network Analysis
8. “Socio-Spatial 
 Differentiation” Domains Can mean many things, however, in my research, this has been ‘developing and refining models in a geodemographic tradition’ Methods Consumption of Commercial Classification Critique of Commercial Classification Bespoke Geodemographics Real-time Geodemographics Network / Interaction Typologies Integrating Geodemographics and spatial interaction models Profiling HE Data Profiling Schools Data Educational Mosaic Profiling education data with OAC Decision Support Tool Data Integration Educational OAC E-Society HE Choice Sets School-University Flows School Catchment Models 2003 2010-
9. Linking Methods to Substantive Issues • 3 Themes – Critical Geodemographics – Neogeography and Digital Exclusion – Widening Access to Higher Education
10. CRITICAL GEODEMOGRAPHICS Theme 1
11. Critical Geodemographics • What are geodemographics? – Brief history – How are they made? • What are the potential problems for public sector users?
12. Description BLACK: Lowest class. Vicious, semi-criminal. DARK BLUE: Very poor, casual. Chronic want. LIGHT BLUE: Poor. 18s. to 21s. a week for a moderate family PURPLE: Mixed. Some comfortable others poor PINK: Fairly comfortable. Good ordinary earnings. RED: Middle class. Well-to-do. YELLOW: Upper-middle and Upper classes. Wealthy. Walk with Police Constable Robert Turner, 12 July 1898 Charles Booth Maps – 1889-1892
13. Marr, T.R. (1904) Housing Conditions in Manchester and Salford. Manchester, Manchester University Press.
14. Social Area Analysis – Shevky and Bell (1955)
15. Liverpool Area Study (1971) • Richard Webber et al – CACI (Acorn) – Experian (Mosaic)
16. Inputs Area V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 ... Area1 Area2 Area3 Area4 Area5 Area6 Area7 Area8 ...
17. Variable 1 Variable 2 Cluster 1 Cluster 2 Cluster 3 Cluster Analysis
21. Critique (important for the public sector!) • One size fit all? • Open? – Methods – Public Consultation (‘crowd sourcing’) • k-means optimisation • Is k-means the only option? (real-time)
22. One size fits all? Refined version of OAC for HE
23. Open? - Methods • ONS • 2001 Census • Vickers and Rees (2007)
24. Open? – Public Consultation 79,051 hits over the 13 day period 3,952 feedback responses
25. The percentages of unit postcodes within each CAS Ward that were searched during the study period Frequency of feedback by origin e-Society Type Frequency of destination e-Society Type
26. K-means optimisation 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 Run RSQ
27. K-means (100 runs of k-means on OAC data set for k=4)
28. 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 55 56 57 58 59 60 61 62 63 64 65 k RSQ
29. Is k-means the only option? (real-time) • Alternative algorithms / simplification – PAM; GA / PCA • Server based specification, creation, visualisation – Real time • Computationally • Dynamics – e.g. Daytime population estimates • GRID – GPU / CUDA Nvidia Tesla Server - 1920 CUDA cores ~£5k
30. NEOGEOGRAPHY AND DIGITAL EXCLUSION Theme 2
31. Neogeography and Digital Exclusion • Interested in ‘Neogeography’ at the margins – Position paper (with Muki, Chris Parker – OS) – Encyclopaedia entry (Barney Warf) – Couple of magazine articles • My view – The technology to make great maps exists – Next challenge is to link this with better analytical functionality • Utilise real-time data feeds • Generalisations on the fly • Make predictions
33. This is great... BUT!
34. Winners and the Losers
35. WIDENING ACCESS TO HIGHER EDUCATION Theme 3
36. Widening Access to Higher Education • ~250 HE Institutions in England & Wales (HEFCE) • 2008 – 396,630 Degree Acceptances UK (UCAS) • 50% Participation by 2010 - ~43% (07-08) • Fees – Top Up – Office of Fair Access – Access Agreements • Monitoring – WP Benchmarks • HECE allocated £141 million directly to institutions for widening participation in 2009-10
37. Occupational Group: 1968-1978 0% 25% 50% 75% 100% 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 Manual Clerical & Armed F Administrators & M Professional & Tech
38. Socio-Economic Group: 2002 - 2007 0% 25% 50% 75% 100% 2002 2003 2004 2005 2006 2007 Routine occupations Semi-routine occupati Lower supervisory and Small employers and Intermediate occupatio Lower managerial and Higher managerial and
39. 2004 UCAS Profile (Total Base) 0 50 100 150 200 250 300 0 0 0 0 0 0 0 Global Connections Cultural Leadership Corporate Chieftains Golden Empty Nesters Provincial Privilege High Technologists Semi-Rural Seclusion Just Moving In Fledgling Nurseries Upscale New Owners Families Making Good Middle Rung Families Burdened Optimists In Military Quarters Close to Retirement Conservative Values Small Time Business Sprawling Subtopia Original Suburbs Asian Enterprise Respectable Rows Affluent Blue Collar Industrial Grit Coronation Street Town Centre Refuge South Asian Industry Settled Minorities Counter Cultural Mix City Adventurers New Urban Colonists Caring Professionals Dinky Developments Town Gown Transition University Challenge Bedsit Beneficiaries Metro Multiculture Upper Floor Families Tower Block Living Dignified Dependency Sharing a Staircase Families on Benefits Low Horizons Ex-industrial Legacy Rustbelt Resilience Older Right to Buy White Van Culture New Town Materialism Old People in Flats Low Income Elderly Cared for Pensioners Sepia Memories Childfree Serenity High Spending Elders Bungalow Retirement Small Town Seniors Tourist Attendants Summer Playgrounds Greenbelt Guardians Parochial Villagers Pastoral Symphony Upland Hill Farmers Mosaic Type / Group Symbols of Success Happy Families Suburban Comfort Ties of Community Urban Intelligence Welfare Borderline Municipal Dependency Blue Collar Enterprise Tw ilight Subsistence Grey Perspectives Rural Isolation Who goes to university? Symbols of Success Urban Intelligence - Higher Age Profile Welfare Borderline Municipal Dependency Twilight Subsistence Blue Collar Enterprise Metro Multiculture Key Widening Participation Groups
40. Who goes to university? Young Participation 2004 - UK 0 75 150 225 300 Blue Collar Communities City Living Countryside Prospering Suburbs Constrained by Circumstances Typical Traits Multicultural Young Participation 2004 - UK 0 75 150 225 300 Blue Coll City Livin Countrys Prosperin Constrain Typical T Multicultu Prospering Suburbs Countryside Aspiring Households 1 & 2 Asian Communities 3 Key WP Groups Blue Collar Communities Constrained by Circumstances
42. Can I recruit from anywhere? All 04 Acceptances Average Distance Miles 0.00 30.00 60.00 90.00 120.00 Geodemographic Sub-Type 1a1 1a2 1a3 1b1 1b2 1c1 1c2 1c3 2a1 2a2 2b1 2b2 3a1 3a2 3b1 3b2 3c1 3c2 4a1 4a2 4b1 4b2 4b3 4b4 4c1 4c2 4c3 4d1 4d2 5a1 5a2 5b1 5b2 5b3 5b4 5c1 5c2 5c3 6a1 6a2 6b1 6b2 6b3 6c1 6c2 6d1 6d2 7a1 7a2 7a3 7b1 7b2 Blue Collar Communities City Living Countryside Prospering Suburbs Constrained by Circumstances Typical Traits Multicultural Average Distance from applicant home to accepting institution
43. Different courses attract different people Chemistry - UK 0.000 0.000 0.000 0.000 0.000 Blue Collar Communities City Living Countryside Prospering Suburbs Constrained by Circumstances Typical Traits Multicultural Base - UK Chemistry
44. Different courses attract different people Music - UK 0.000 0.000 0.000 0.000 0.000 Blue Collar Communities City Living Countryside Prospering Suburbs Constrained by Circumstances Typical Traits Multicultural Base - UK Music
45. Different courses attract different people Physical Geography (02-04) - UK 0.000 0.000 0.000 0.000 0.000 Blue Collar Communities City Living Countryside Prospering Suburbs Constrained by Circumstances Typical Traits Multicultural Base - UK Physical Geography
46. Different courses attract different people Human Geography (02-04) - UK 0.000 0.000 0.000 0.000 0.000 Blue Collar Communities City Living Countryside Prospering Suburbs Constrained by Circumstances Typical Traits Multicultural Base - UK Human Geography
47. School Catchment Areas
48. School Catchment Areas Cheltenham Kingsmead School Mosaic Profile KS4 Index (Base 100) 0 75 150 225 300 Symbols of Success Happy Families Suburban Comfort Ties of Community Urban Intelligence Welfare Borderline Municipal Dependenc Blue Collar Enterprise Twilight Subsistence Grey Perspectives Rural Isolation Pates Grammar School Mosaic Profile KS4 Index (Base 100) 0 100 200 300 400 Symbols of Success Happy Families Suburban Comfort Ties of Community Urban Intelligence Welfare Borderline Municipal Dependenc Blue Collar Enterprise Twilight Subsistence Grey Perspectives Rural Isolation A high performing school in Cheltenham A low performing school in Cheltenham
49. Data Integration
50. Data Integration 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
54. UCAS Subject Choice Associations Subject Description/ JACS Line Code A1 A2 B0 B1 B2 B3 B4 B5 B6 B7 A1 - Pre-Clinical Medicine 76.9 0.3 0.0 2.2 2.6 0.0 0.1 0.4 0.1 0.4 A2 - Pre-Clinical Dentistry 3.7 72.0 0.0 1.0 6.3 0.0 0.1 2.1 0.1 0.2 B0 - Subjects allied to Medicine: any area 0.0 0.0 8.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 B1 - Anatomy, Physiology and Pathology 6.7 0.4 0.0 49.3 1.7 0.1 0.4 0.5 0.3 0.7 B2 - Pharmacology, Toxicology and Pharmacy 11.5 3.3 0.0 2.5 49.4 0.4 0.3 1.8 0.1 0.4 B3 - Complementary Medicine 0.8 0.2 0.0 3.8 3.5 37.2 0.7 0.2 0.4 0.7 B4 – Nutrition 1.2 0.3 0.0 2.6 1.4 0.2 46.6 0.4 0.6 1.8 B5 – Ophthalmics 7.7 4.9 0.0 2.3 7.4 0.0 0.4 54.1 0.7 0.5 B6 - Aural and Oral Sciences 1.7 0.2 0.0 2.1 0.3 0.1 0.5 0.7 57.8 1.7 B7 – Nursing 1.2 0.1 0.0 0.9 0.3 0.1 0.4 0.1 0.4 78.2
55. 10 Most Homogenous Courses • Most – B7 - Nursing – A1 - Pre-clinical Medicine – A2 - Pre-clinical Dentistry – M1 - Law by Area – D1 - Pre-clinical Veterinary Medicine – K1 - Architecture – V1 - History by Period – Q8 - Classical studies – B8 - Medical Technology – B6 - Aural and Oral Sciences
56. M1 - Law by Area • Within Line = 67.6%
57. L7 - Human and Social Geography • Within Line = 50%
58. Standardised index scores for course choice behaviour by ethnic groups Frequency of JACS Lines Chosen/ Index Scores Ethnic Group 1 2 3 4 5 6 or more Asian – Bangladeshi 73 105 109 130 115 123 Asian – Indian 90 101 104 109 113 101 Asian - Other Asian background (ex. Chinese) 100 102 102 97 104 83 Asian – Pakistani 77 100 111 116 135 115 Black – African 88 103 106 106 104 121 Black – Caribbean 96 97 104 113 91 102 ... ... ... ... ... ... ...
59. Standardised Index Scores for course choice behaviour by NS-SEC Frequency of JACS Lines Chosen/ Index Scores NS-SEC 1 2 3 4 5 6 or more Higher managerial and professional occupations 108 105 97 89 86 78 Intermediate occupations 99 100 101 102 103 98 Lower managerial and professional occupations 97 103 103 101 99 99 Lower supervisory and technical occupations 103 97 99 98 100 100 Semi-routine occupations 87 98 104 115 122 125 Small employers and own account workers 90 100 105 110 110 118
60. Future Research Directions • Critical Geodemographics – Inclusion of relational data into classification • Geographic – Spatial weighting • Network Typologies – Social Flows / Interaction • Neogeography and Digital Exclusion – Updated small area estimates of digital differentiation – Socio-spatial implications of GPS routing • ‘Social Routing’ – Sociology of the OSM community • Implications for data quality (Spatial & Temporal) • Widening Access to Higher Education – Continual update to integrated data model – New HE & Schools Classifications – Decision Support Tools for WP / School Choice