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Jones Et Al Gisruk 2008

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Presentation given to GISRUK 2008: Visualising London's Suburbs.

Presentation given to GISRUK 2008: Visualising London's Suburbs.

Published in: Business, Technology

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    • 1. Towards Successful Suburban Town Centres Visualising London’s Suburbs Presentation to: GISRUK Thursday 2 nd April 2008 Catherine Jones, Muki Haklay, Sam Griffiths, Laura Vaughan Successful Suburban Town Centres Project EPSRC reference: EP/D06595X/1 http://www.sstc.ucl.ac.uk/profiler
    • 2. Outline
      • Project Aims
      • Selecting the case studies
      • Profiler Context
      • Datasets and processing
      • Developing the profiler
      • Demonstration of the profiler
      • Advantages of the profiler
      • Hypotheses developed using the profiler
    • 3. Overall Project Aims
      • Investigate the relationship between suburban morphology and the economic success of suburban town centres over time
      • Further understanding of how the built form of suburban town centres contributes to the economic and social sustainability of their surroundings
      • Develop a toolkit for the assessment of suburban town centre design proposals
      • Develop policy recommendations for the design and management of suburban town centres
    • 4. Selecting the case studies
    • 5. Case study selection Derived from Address Layer 2 Count of general commercial address locations in the 250m buffer Derived from Address Layer 2 Count of address locations class of offices in the 250m buffer Derived from Address Layer 2 Count of manufacturing address locations in the 250m buffer Town Centre Statistics 2002 Floor space per square hectare Town Centre Statistics 2002 Total number of Employees working town centre Derived from Address Layer 2 Count of general commercial address locations in the town centre Derived from Address Layer 2 Count of address locations class of offices in the town centre Derived from Address Layer 2 Count of manufacturing address locations in the town centre Variable Source Variable
    • 6.  
    • 7. Profiler Context
      • What: There is a need for the provision of more accessible information on Greater London’s suburban town centres
      • Why: To improve understanding of their distinctive characteristics as places to live and work within an extended metropolitan region
      • How: The EPSRC Towards Successful Suburban Town Centres (SSTC) project seeks to meet need by profiling suburban town centres according to:
        • Socio-economic activity
        • Morphological characteristics
        • Commuting patterns
        • Typical modes of transport use
    • 8. What is the profiler?
      • Develop project hypotheses for further detailed exploration
      • Visualise the suburban form using spaces syntax methodology combined with socio-economic data
      • Enable configuration of streets and layout of buildings to be compared with information about the people who live and work in suburban centres
    • 9. Datasets and data processing
    • 10. Datasets – socio-economic Table UV31 (Resident population) Table UV76 (Workplace population) Census output area Data for population aged 16 to 74 by their socio-economic classification Classification of occupation Ordnance Survey Address Layer 2 Points Address Layer 2 is derived from the Royal Mail postal address file (PAF), whereby each address is allocated a unique reference and national grid reference. Functional activity land uses Table UV39 (Resident population) Table UV37 (Workplace population) Census output area Usual population aged 16 to 74 and their method of travel to work. The method of travel to work is for the longest part, by distance, of the usual journey to work. Method of travel to work Ordnance Survey Meridian & Address Layers 2 Polylines and points Railways, Stations (Tube and Rail), A, B and minor roads, motorways and car parks Infrastructure 2001 Census: Special Workplace Statistics Census output area Contains journey-to-work flows within and between output areas in England Commuting patterns Table KS17 Census output area Car or van ownership per household Car Ownership Source Areal Unit Description Data
    • 11. Datasets – space syntax Segments angular choice is : Measures the relative straightness of each segment from all other segments in the system. It calculates the number of times each segment falls on the shortest path between all pairs of segments where shortest path is that of least angular deviation - Red lines (high values) indicate greatest potential through movement. - Blue lines (low values) indicate lowest potential through movement.
    • 12. Total number of map themes
      • Number of socio-economic map themes = 936
      • Number of Syntax map = 2080
      • Total number of maps = 3016
    • 13. Developing the profiler
    • 14. Transforming data into information Syntax graphs Database Data Inputs Data Processes Data Outputs Spatial Functional activity Social characteristics Movement Commuting Travel Method Retail Industry Community services Offices Car Ownership Demographic GIS Processing code Map Images Map images php script Web Server Interface User java script 3016
    • 15. Profiler Interface
    • 16. Profiling the case study centres
    • 17. Demonstration of the profiler
      • Link to video / website
      • http://www-research.ge.ucl.ac.uk/sstc/profiler/profiler.php
    • 18. Advantages of using profiler – part I
      • Facilitates building of knowledge relating to the selected town centres
      • Compiling data sources into coherent and structured interface
      • Interface acts as an interactive atlas of maps, where the menu options provide a multidimensional index
      • The visualiser controls the extent of the map, making it easier for the user to move around the different centres
      • Consistent scales across the centres allows for systematic comparison
    • 19. Advantages of using the profiler – part II
      • The different types of interaction presents advantages over recently emerging interactive web mapping interfaces:
        • Creation of an interface to fit with a specific user-generated requirement
        • Maps as images enable GIS expert to maintain control of content, design and scale
        • Quick transition between town centres avoids short term memory problems in ‘zoom out-zoom in’ activities
        • Slideshow functionality with image transitions enables horizontal comparison across town centres, meaning similarities and differences can be visually examined
    • 20. How was the profiler used?
      • To provoke thought and facilitate ideas to be investigated in detail
      • To reinforce an initial hypotheses that suburban centres are not merely sleepy residential commuter towns reliant on the urban core
      • To show a wider range of activity generating land uses prevalent in the centres and their local hinterland
      • To explore the relationship between local patterns of movement, street accessibility and socio-economic activity
      • To approach the suburbs as multi-dimensional places characterised in part by the complexity of their relations with other places