Visualising Geodemographics and the Built Environment


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Presentation for a workshop on the Output Area Classification at the Royal Statistical Society 15/09/09.

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Visualising Geodemographics and the Built Environment

  1. 1. Visualising Geodemographics and the Built Environment<br />Duncan Smith, UCL<br />Royal Statistical Society, 15th Sept 2009.<br />
  2. 2. Overview<br />Built Environment and Geodemographics<br />Adding 3D Buildings to Geodemographic Visualisation<br />A New Address Geography? Residential Classification Example<br />Note: All the building outline data used in this presentation is ©Crown Copyright Ordnance Survey, and the building heights data is ©Infoterra.<br />
  3. 3. Built Environment and Geodemographics<br />Relationships Between Urban Form and Demographics<br />Through various processes- geography of property market, income segregation, household preferences, council housing- strong relationships between housing types, urban form and demographic characteristics.<br />Interesting to explore and map how demographic characteristics relate to urban texture and built environment.<br />Gentrification and Social Change<br />Residential location and urban development dynamic. Processes should be identifiable using this approach.<br />
  4. 4. Visualising the Built Environment<br />Can add a sense of place to visualisations, may be useful for public engagement. Also various applications for environmental modelling (energy efficiency, flooding etc).<br />Data Sources<br />Created by combining building outline data (OS Mastermap, Cities Revealed) with LIDAR building heights data (Infoterra, Landmap).<br />Virtual London<br />CASA project creating a 3D building model of Greater London. Based on Mastermap and Infoterra data. Used in various visualisation and modelling projects.<br />
  5. 5. London OAC<br />London Demographically Distinct<br />Greater ethnic diversity, income extremes, younger. UK wide OAC classification tends to group all of central London into a small number of super groups.<br />London OAC designed to address this as only applied to Greater London. Developed by Jacob Petersen.<br />1 Suburban<br />2 Council Flats<br />3 Asian Quarters<br />4 Central District<br />5 Blue Collar<br />6 City Commuter<br />7 London Terraces<br />
  6. 6. Case Study: Inner East London<br />Area of contrast and change<br />Historically industrial and working class, ethnically diverse, high immigration and deprivation.<br />Regeneration<br />Massive redevelopment and gentrification over last twenty years, spreading east. Processes continuing.<br />Interesting case study for built environment change and geodemographics.<br />
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  12. 12. Building Level Visualisation Pros and Cons<br />Strong Sense of Geography<br />Get a sense of urban texture, river and landmarks. Aids understanding of development and gentrification processes.<br />Visually Engaging<br />May be useful for promotion, public participation.<br />Appropriate Scale<br />Works best at fairly local scales. 2D more appropriate for city wide studies.<br />Spatial Statistical Errors<br />Geodemographic data at Output Area level, while built environment data at building level.<br />Finer scale demographics possible? Useful?<br />
  13. 13. New Fine Scale Geography- Address Level<br />Data Innovations<br />Recent improvements to spatial referencing of addresses. From PAF, and related products NLPG and OS AL2.<br />Can be combined with socio-economic data e.g. house price from land registry, valuation office data etc.<br />Advantages<br />Minimise Modifiable Areal Unit Problem with highly disaggregate data.<br />Relate data to real estate properties (e.g. house size), and fine scale locational properties<br />Privacy Concerns<br />Sensitive data often not available, and should be aggregated for publishing. For most applications fine scale not needed, but useful for some, particularly relating to real estate.<br />Technical Challenges<br />Errors in address matching, Computational intensity.<br />
  14. 14. Residential Classification<br />Example of address level approach- dwelling type classification.<br />Applications<br />Urban planning, house price modelling, studies of urban character and growth (as well as geodemographics).<br />Advantages Over Census<br />Why another dataset? Finer scale useful. Also improved data currency with census now 8 years old.<br />Methodology<br /><ul><li> Create building and address data model
  15. 15. Spatial analysis to assess dwelling type
  16. 16. Aggregate/analyse for particular application.</li></li></ul><li>Building and Address Data Model<br />Blocks<br />Buildings<br />Dwellings<br />Address<br />Address<br />Address<br />Address<br />Address<br />Address<br />Basic categories of residential type can be identified using address and building adjacency relationships.<br />Create basic objects<br />Block- group of one or more adjacent Buildings.<br />Building- one or more adjacent building polygons with one Building Address.<br />Dwelling-Sub-building address linked to a Building. <br />Relationship between objects define housing types e.g.<br />Detached- 1 Building with 1 Dwelling per Block.<br /> Semi-detached- 2 adjacent Buildings each with 1 Dwelling per Block.<br /> Terraced- more than 2 adjacent Building each with 1 Dwelling per Block.<br /> Flats- 1 or more Buildings per block with more than 2 Dwellings.<br />
  17. 17. Housing Classification Patterns<br />Local Scale Patterns<br />Highly diverse housing types. Likely due to complex history of growth, infill, conversions, local town centres. Implies local demographic diversity also?<br />City Wide Trends<br />Need to aggregate the data. Example uses a 100m grid showing the most frequently occurring dwelling type in each cell.<br />Clear density gradient from the city centre to outskirts. Interplay play of local and city wide influences.<br />
  18. 18. City Wide Housing Patterns<br />Can graph the housing types against distance from the city centre. Fits closely with Alonso’s model-<br /><ul><li>In Central Business District commercial functions outbid residential.
  19. 19. Density gradient from centre, greatest demand where access to jobs highest.</li></ul>Terraced housing the most common dwelling type in Greater London.<br />
  20. 20. Conclusions<br />Many relationships between demographics and the built environment that can be explored using this approach.<br />Addition of buildings and building heights can add a sense of place, consider urban texture in demographics. Was useful in mapping gentrification processes in Inner East London.<br />Most useful and practical at local scales.<br />Need to be aware of statistical errors relating to the MAUP.<br />Trends towards an address based geography to minimise MAUP errors. This is most relevant for real estate and housing type applications.<br />
  21. 21. Thank you for listening! Welcome comments and questions.<br />Contact Email:<br />More about research: ,<br />More about urban visualisation:<br />Data providers for this research:<br />Ordnance Survey<br />Infoterra<br />Greater London Authority<br />