Visualising Geodemographics and the Built EnvironmentDuncan Smith, UCLRoyal Statistical Society, 15th Sept 2009.

OverviewBuilt Environment and GeodemographicsAdding 3D Buildings to Geodemographic VisualisationA New Address Geography? Residential Classification ExampleNote: All the building outline data used in this presentation is ©Crown Copyright Ordnance Survey, and the building heights data is ©Infoterra.

Built Environment and GeodemographicsRelationships Between Urban Form and DemographicsThrough various processes- geography of property market, income segregation, household preferences, council housing- strong relationships between housing types, urban form and demographic characteristics.Interesting to explore and map how demographic characteristics relate to urban texture and built environment.Gentrification and Social ChangeResidential location and urban development dynamic. Processes should be identifiable using this approach.

Visualising the Built EnvironmentCan add a sense of place to visualisations, may be useful for public engagement. Also various applications for environmental modelling (energy efficiency, flooding etc).Data SourcesCreated by combining building outline data (OS Mastermap, Cities Revealed) with LIDAR building heights data (Infoterra, Landmap).Virtual LondonCASA project creating a 3D building model of Greater London. Based on Mastermap and Infoterra data. Used in various visualisation and modelling projects.

London OACLondon Demographically DistinctGreater ethnic diversity, income extremes, younger. UK wide OAC classification tends to group all of central London into a small number of super groups.London OAC designed to address this as only applied to Greater London. Developed by Jacob Petersen.1 Suburban2 Council Flats3 Asian Quarters4 Central District5 Blue Collar6 City Commuter7 London Terraces

Case Study: Inner East LondonArea of contrast and changeHistorically industrial and working class, ethnically diverse, high immigration and deprivation.RegenerationMassive redevelopment and gentrification over last twenty years, spreading east. Processes continuing.Interesting case study for built environment change and geodemographics.

Building Level Visualisation Pros and ConsStrong Sense of GeographyGet a sense of urban texture, river and landmarks. Aids understanding of development and gentrification processes.Visually EngagingMay be useful for promotion, public participation.Appropriate ScaleWorks best at fairly local scales. 2D more appropriate for city wide studies.Spatial Statistical ErrorsGeodemographic data at Output Area level, while built environment data at building level.Finer scale demographics possible? Useful?

New Fine Scale Geography- Address LevelData InnovationsRecent improvements to spatial referencing of addresses. From PAF, and related products NLPG and OS AL2.Can be combined with socio-economic data e.g. house price from land registry, valuation office data etc.AdvantagesMinimise Modifiable Areal Unit Problem with highly disaggregate data.Relate data to real estate properties (e.g. house size), and fine scale locational propertiesPrivacy ConcernsSensitive 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.Technical ChallengesErrors in address matching, Computational intensity.

Residential ClassificationExample of address level approach- dwelling type classification.ApplicationsUrban planning, house price modelling, studies of urban character and growth (as well as geodemographics).Advantages Over CensusWhy another dataset? Finer scale useful. Also improved data currency with census now 8 years old.Methodology Create building and address data model Spatial analysis to assess dwelling type Aggregate/analyse for particular application.

Create building and address data model

Spatial analysis to assess dwelling type

Aggregate/analyse for particular application.

Building and Address Data ModelBlocksBuildingsDwellingsAddressAddressAddressAddressAddressAddressBasic categories of residential type can be identified using address and building adjacency relationships.Create basic objectsBlock- group of one or more adjacent Buildings.Building- one or more adjacent building polygons with one Building Address.Dwelling-Sub-building address linked to a Building. Relationship between objects define housing types e.g.Detached- 1 Building with 1 Dwelling per Block. Semi-detached- 2 adjacent Buildings each with 1 Dwelling per Block. Terraced- more than 2 adjacent Building each with 1 Dwelling per Block. Flats- 1 or more Buildings per block with more than 2 Dwellings.

Housing Classification PatternsLocal Scale PatternsHighly diverse housing types. Likely due to complex history of growth, infill, conversions, local town centres. Implies local demographic diversity also?City Wide TrendsNeed to aggregate the data. Example uses a 100m grid showing the most frequently occurring dwelling type in each cell.Clear density gradient from the city centre to outskirts. Interplay play of local and city wide influences.

City Wide Housing PatternsCan graph the housing types against distance from the city centre. Fits closely with Alonso’s model-In Central Business District commercial functions outbid residential.Density gradient from centre, greatest demand where access to jobs highest.Terraced housing the most common dwelling type in Greater London.

In Central Business District commercial functions outbid residential.

Density gradient from centre, greatest demand where access to jobs highest.

ConclusionsMany relationships between demographics and the built environment that can be explored using this approach.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.Most useful and practical at local scales.Need to be aware of statistical errors relating to the MAUP.Trends towards an address based geography to minimise MAUP errors. This is most relevant for real estate and housing type applications.

Thank you for listening! Welcome comments and questions.Contact Email: duncan.a.smith@ucl.ac.ukMore about research: www.casa.ucl.ac.uk , blog.casa.ucl.ac.ukMore about urban visualisation: digitalurban.blogspot.comData providers for this research:Ordnance SurveyInfoterraGreater London Authority