Socio-Spatial Differentiation - People, Places and Interaction
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Socio-Spatial Differentiation - People, Places and Interaction

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

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

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Socio-Spatial Differentiation - People, Places and Interaction Socio-Spatial Differentiation - People, Places and Interaction Presentation Transcript

  • Socio-Spatial Differentiation - People, Places and Interaction
    Dr Alex Singleton
    University College London
    www.alex-singleton.com
  • 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
  • All publication titles and abstracts - to July 2010
  • Predicting Participation in Higher Education: a Comparative Evaluation of the Performance of Geodemographic Classifications
    Geodemographics, Visualization, and Social Networks in Applied Geography
    The Geodemographics of Educational Progression and their Implications for Widening Participation in Higher Education
    Course Choice Behaviour and Target Marketing of Higher Education
    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
    Classification through Consultation: Public Views of the Geography of the e-Society.
    Web Mapping 2.0: the Neogeography of the Geospatial Internet.
    Creating Open Source Geodemographics: Refining a National Classification of Census Output Areas for Applications in Higher Education
    Exploratory Cartographic Visualisation of London using the Google Maps API
    Uncertainty in the Analysis of Ethnicity Classifications. Issues of Size, Scale and Aggregation of Groups
    Lost in translation? Cross-Cultural Experiences in Teaching Geo-Genealogy
    United Kingdom Surname Clusters
    Linking Social Deprivation and Digital Exclusion in England
  • Higher Education
    GIS and Neogeography
    Domains
    Digital Exclusion
    Geo-Genealogy
  • Predicting Participation in Higher Education: a Comparative Evaluation of the Performance of Geodemographic Classifications
    Geodemographics, Visualization, and Social Networks in Applied Geography
    The Geodemographics of Educational Progression and their Implications for Widening Participation in Higher Education
    Course Choice Behaviour and Target Marketing of Higher Education
    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
    Classification through Consultation: Public Views of the Geography of the e-Society.
    Web Mapping 2.0: the Neogeography of the Geospatial Internet.
    Creating Open Source Geodemographics: Refining a National Classification of Census Output Areas for Applications in Higher Education
    Exploratory Cartographic Visualisation of London using the Google Maps API
    Uncertainty in the Analysis of Ethnicity Classifications. Issues of Size, Scale and Aggregation of Groups
    Lost in translation? Cross-Cultural Experiences in Teaching Geo-Genealogy
    United Kingdom Surname Clusters
    Linking Social Deprivation and Digital Exclusion in England
  • Geodemographics
    Geocomputation
    Methods
    Geoweb / Visualisation
    Network Analysis
  • “Socio-Spatial Differentiation”
    2010-
    School-University Flows
    HE Choice Sets
    Network / Interaction
    Typologies
    Can mean many things, however, in my research, this has been ‘developing and refining models in a geodemographic tradition’
    E-Society
    Domains
    Decision Support Tool
    Real-time Geodemographics
    Educational OAC
    Profiling education data with OAC
    Integrating Geodemographics and spatial interaction models
    Data Integration
    Profiling Schools Data
    Methods
    School Catchment Models
    Bespoke Geodemographics
    Educational Mosaic
    Critique of Commercial Classification
    Profiling HE Data
    Consumption of Commercial Classification
    2003
  • Linking Methods to Substantive Issues
    3 Themes
    Critical Geodemographics
    Neogeography and Digital Exclusion
    Widening Access to Higher Education
  • Critical Geodemographics
    Theme 1
  • Critical Geodemographics
    What are geodemographics?
    Brief history
    How are they made?
    What are the potential problems for public sector users?
  • Charles Booth Maps – 1889-1892
    Walk with Police Constable Robert Turner, 12 July 1898
  • Marr, T.R. (1904) Housing Conditions in Manchester and Salford. Manchester, Manchester University Press.
  • Social Area Analysis – Shevky and Bell (1955)
  • Liverpool Area Study (1971)
    Richard Webber et al
    CACI (Acorn)
    Experian (Mosaic)
  • Inputs
  • Variable 2
    Cluster 1
    Cluster 2
    Variable 1
    Cluster 3
    Cluster Analysis
  • 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)
  • One size fits all?
    Refined version of OAC for HE
  • Open? - Methods
    ONS
    2001 Census
    Vickers and Rees (2007)
  • Open? – Public Consultation
    79,051 hits over the 13 day period
    3,952 feedback responses
  • Frequency of feedback by origin e-Society Type
    The percentages of unit postcodes within each CAS Ward that were searched during the study period
    Frequency of destination e-Society Type
  • K-means optimisation
  • K-means(100 runs of k-means on OAC data set for k=4)
  • 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
  • Neogeography and Digital Exclusion
    Theme 2
  • 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
  • This is great... BUT!
  • Winners and the Losers
  • Widening Access to Higher Education
    Theme 3
  • 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
  • Occupational Group: 1968-1978
  • Socio-Economic Group: 2002 - 2007
  • Urban Intelligence
    - Higher Age Profile
    Who goes to university?
    Key Widening Participation Groups
    Symbols of Success
    Metro Multiculture
    Blue Collar Enterprise
    Welfare Borderline
    Twilight Subsistence
    Municipal Dependency
  • Who goes to university?
    Prospering Suburbs
    Countryside
    Aspiring Households 1 & 2
    Blue Collar Communities
    Key WP Groups
    Constrained by Circumstances
    Asian Communities 3
  • Can I recruit from anywhere?
    Average Distance from applicant home to accepting institution
  • Different courses attract different people
    Chemistry
    Base - UK
  • Different courses attract different people
    Music
    Base - UK
  • Different courses attract different people
    Physical Geography
    Base - UK
  • Different courses attract different people
    Human Geography
    Base - UK
  • School Catchment Areas
  • School Catchment Areas
    A low performing school in Cheltenham
    A high performing school in Cheltenham
  • Data Integration
  • Data Integration
    HESA (0)
    Direct Entry
    ~50%
    2004
    ~20%
    DCSF
    Key Stage 5
    HESA (+1)
    Gap Year
    ~5%
    HESA (+2)
    Gap Years
    National Targets = 18-30 Age Range
  • UCAS Subject Choice Associations
  • 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
  • M1 - Law by Area
    Within Line = 67.6%
  • L7 - Human and Social Geography
    Within Line = 50%
  • Standardised index scores for course choice behaviour by ethnic groups
  • Standardised Index Scores for course choice behaviour by NS-SEC
  • 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