1B_2_population 247 building space-time specific population surface models

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Session 1B, Presentation 2

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1B_2_population 247 building space-time specific population surface models

  1. 1. Population 24/7: building space-time specific population surface models Samantha Cockings, David Martin and Samuel Leung, University of Southampton, UK GISRUK Conference, UCL London, 14 April 2010
  2. 2. Presentation overview • Space-time population modelling: background concepts • Conceptual framework for our approach • Data sources • Empirical example • Next steps Project website (new!) at http://www.southampton.ac.uk/geography/research/phew/pop247/index.html 2
  3. 3. Space-time population modelling: background concepts
  4. 4. Small area population mapping • Resource allocation: large areas > small areas • Targeting services/marketing • Site location decisions/transportation demand • Used as denominator populations • BUT limitations from current focus on residential base hence static, “night-time” populations – Doesn‟t meet needs of emergency planning, many other time-specific applications 4
  5. 5. Currently mapped locations Photos: David Martin, Sam Cockings 5
  6. 6. Observations… • There have been enormous advances in geo-visualization techniques, computing power and dynamic modelling sophistication • We have not yet seriously tackled the entire area of space- time-specific population modelling • “One of the most important and difficult problems now facing city planners is the development of accurate, usable techniques for estimating the current daytime population of census tracts in urban areas” Schmitt, R. C. (1956) Journal of the American Planning Association 22 (2), 83-85 6
  7. 7. “Uncharted” locations 1 Photos: David Martin 7
  8. 8. Photos: David Martin “Uncharted” locations 2 8
  9. 9. Conceptual framework for our approach
  10. 10. Space-time population modelling • Where tried, the general approach is to start with night- time population model/map and transfer population subgroups to specific daytime locations, e.g. schools, workplaces • Recent emergency planning examples e.g. Smith and Fairburn (2008); Sleeter and Wood (2006); McPherson et al. (2006); Bhadhuri et al. (2007); Ahola et al. (2007) • Longstanding difficulty of obtaining data with sufficient space/time resolution for the non-residential addresses • In reality, many different timescales to be modelled, not just simple „daytime‟ and „night-time‟ 10
  11. 11. Objective • We want a count of all the people in our study area at a given time – Which means having an estimate relevant to that time • And we need to allocate them to a spatially detailed reference location – Specific to the chosen time • And we need to take account of population in transit between reference locations at the time in question 11
  12. 12. Mapping population to activities/places… Locations Residential Private dwellings Communal ests. Education Employment Total Temp accomm. population Healthcare Non- Family/social +/- external residential visitors Retail Leisure ... and further Tourism subdivisions Generalized local Road Rail Transport Metro/subway Air Water 12
  13. 13. Spatial modelling framework • Builds on Martin (1989, 2006), Martin et al. (2000) currently implemented in SurfaceBuilder program • One of a variety of methods for reallocation of population counts onto a series of geographical features • Akin to dasymetric models where known population counts are allocated to most likely set of spatial locations • Adaptive kernel estimation, treating each centroid as a high information point. • Weight each cell to receive population reallocated from local centroids, hence volume preserving 13
  14. 14. Extension to the spatio-temporal problem Spatial centroid: e.g. census output area Population count Census population Spatial extent Modelled Space-time centroid: e.g. primary school Population capacity Pupil numbers Spatial extent Small (one cell) Time profile Term dates, school day Area of influence Catchment area (modelled time/space) 14
  15. 15. Time profile example – school Population In transit Present 00 06 12 18 00 Time of day 15
  16. 16. Basic space-time interpolation algorithm • Specify study area a and time t • Identify background layer b (cells that can contain time + visitors in - visitors out population) for time t centroid i • Adjust for external visitors in/out of a at time t • Examine each destination local extent d centroid i to identify populations p in local extent d and area of area of influence j influence j at time t background layer b • Transfer required populations from origin to destination study area a centroids within areas of t influence j • Redistribute p across d for every centroid, constrained by b 16
  17. 17. Data sources
  18. 18. What do current data sources cover? Locations Data Sources Residential Private dwellings - Census, Mid-Year Population Estimates (MYEs) Communal ests. - Census, Mid-Year Population Estimates (MYEs) Education Employment Total Temp accomm. population Healthcare Non- Family/social +/- external residential Retail visitors Leisure Tourism Generalized local Road Rail Transport Metro/subway Air Water 18
  19. 19. Data sources for all non-residential Locations Data Sources Residential Private dwellings - Census, Mid-Year Population Estimates (MYEs) Communal ests. - Census, Mid-Year Population Estimates (MYEs) Education 24% Neighbourhood Statistics, DCSF EduBase, HESA - Employment 49% Census, Annual Business Inquiry, QLFS - Total Temp accomm. - VisitBritain, Annual Business Inquiry population Healthcare - Hospital Episode Statistics Non- Family/social - VisitBritain +/- external residential Retail - Annual Business Inquiry, commercial sources visitors Leisure - ALVA Visitor Statistics, DCMS Tourism - ALVA Visitor Statistics, DCMS Generalized local - Road - DfT Road Statistics, Annual Average Daily Flow Rail - National Rail station usage data Transport Metro/subway - DfT Light Rail Statistics, TfL Tube customer metrics Air - CAA UK Airport Statistics Water - DfT Sea Passenger Statistics , London River Services 19
  20. 20. Time profiles • Variety of sources, but only need reasonable reference time profiles for each type of activity – more detail can be added for specific sites or further subdivision of activity later • Quarterly Labour Force Survey for workforce time profiles (daytime, evening, night working, hours worked, days worked by SIC categories) • Opening hours by various services readily obtainable (schools, etc.) • Timetable data for major transportation nodes provides daily profile of associated activities 20
  21. 21. Timetable Examples 21
  22. 22. Background layer: land use/transportation • Need some kind of base layer which determines where people can be if they are not at a recognized reference location • This mostly refers to the transportation network, which contains variable and sometimes very high populations, mostly time-dependent • Should also identify very low-density areas (open water, mountains, cornfields) whose population is effectively zero at all times • We use ITN and DfT AADF x road, vehicle, area types 22
  23. 23. Empirical example
  24. 24. Study area and early results • Using existing model with pre-prepared data extracts for specific time slices (SurfaceBuilder program) • Demonstration data are not fully calibrated: time-space interpolation program currently being written in .Net using existing and new code components • 25 x 25 km, 200m grid cells, 2006 mid-year reference date • Contains City of Southampton, open water, New Forest, separate smaller towns (Romsey, Eastleigh), motorway, railway, airport, in-town and out-of-town business districts, residential development 24
  25. 25. Centroid set • 1696 census OAs • 3329 workplaces • 211 schools and colleges • 2 universities • Hospitals, stations, airport, etc. 25
  26. 26. Transport • Rasterised MasterMap ITN layer – Motorway (blue) – Trunk A- Road (green) – Principal A- Road (grey) • NTM Area Type in the study area: – Rural (green) – Urban (peach) • AADF Count Points 26
  27. 27. Southampton, 200m cells • 02:00 – residents + Southampton study area overnight traffic • 08:00 – early workers + morning traffic • 09:00 – children in education + workers + rush hour traffic • 16:00 workers + afternoon traffic • 18:00 late workers + evening traffic • 21:00 residents + late night traffic 27
  28. 28. Next steps
  29. 29. Initial visualization of output • Output to kml • Multiple layers overlaid in Google Earth • 3D navigation and exploration • Time slider allows time sequence to be “played” 29
  30. 30. Pop247.NET program 30
  31. 31. Acknowledgements Economic and Social Research Council award number RES- 062-23-1811 Datasets: Employee data from the Annual Business Inquiry Service, National Online Manpower Information Service, licence NTC/ABI07-P3020. Office for National Statistics 2001 Census: Standard Area Statistics (England and Wales): ESRC Census Programme, Census Dissemination Unit, Mimas (University of Manchester). National Statistics Postcode Directory Data: Office for National Statistics, Postcode Directories: ESRC Census Programme, Census Geography Data Unit (UKBORDERS), EDINA (University of Edinburgh). Quarterly Labour Force Survey, Economic and Social Data Service, usage number 40023. Mastermap ITN layer: © Crown Copyright/database right 2009, an Ordnance Survey/EDINA supplied service. Acronyms: DCSF Department for Children, Schools and Families; HESA Higher Education Statistics Agency; QLFS Quarterly Labour Force Survey; DCMS Department for Culture, Media and Sport; ALVA Association for Leading Visitor Attractions; DfT Department for Transport; TfL Transport for London; CAA Civil Aviation Authority 31
  32. 32. Questions?

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