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Gisruk2010 1 b cockings, martin and leung (2010) population 247 building space-time specific population surface models


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  • 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. Presentation overview
    • Space-time population modelling: background concepts
    • Conceptual framework for our approach
    • Data sources
    • Empirical example
    • Next steps
    Project website ( new! ) at
  • 3. Space-time population modelling: background concepts
  • 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
  • 5. Currently mapped locations Photos: David Martin, Sam Cockings
  • 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
  • 7. “ Uncharted” locations 1 Photos: David Martin
  • 8. “ Uncharted” locations 2 Photos: David Martin
  • 9. Conceptual framework for our approach
  • 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’
  • 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
  • 12. Mapping population to activities/places… Total population +/- external visitors Private dwellings Non-residential Communal ests. Transport Education Employment Residential Temp accomm. Generalized local Family/social Retail Leisure Tourism Healthcare Rail Metro/subway Air Water Road Locations ... and further subdivisions
  • 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
  • 14. Extension to the spatio-temporal problem
    • Spatial centroid:
      • Population count
      • Spatial extent
    • Space-time centroid:
      • Population capacity
      • Spatial extent
      • Time profile
      • Area of influence
    • e.g. census output area
      • Census population
      • Modelled
    • e.g. primary school
      • Pupil numbers
      • Small (one cell)
      • Term dates, school day
      • Catchment area (modelled time/space)
  • 15. Time profile example – school 00 06 12 18 00 Time of day Population In transit Present
  • 16. Basic space-time interpolation algorithm
    • Specify study area a and time t
    • Identify background layer b (cells that can contain population) for time t
    • Adjust for external visitors in/out of a at time t
    • Examine each destination centroid i to identify populations p in local extent d and area of influence j at time t
    • Transfer required populations from origin to destination centroids within areas of influence j
    • Redistribute p across d for every centroid , constrained by b
    study area a time t background layer b - visitors out + visitors in area of influence j local extent d centroid i
  • 17. Data sources
  • 18. What do current data sources cover? Total population +/- external visitors Private dwellings Non-residential Communal ests. Transport Education Employment Residential Temp accomm. Generalized local Family/social Retail Leisure Tourism Healthcare Rail Metro/subway Air Water Road - Census, Mid-Year Population Estimates (MYEs) - Census, Mid-Year Population Estimates (MYEs) Locations Data Sources
  • 19. Data sources for all non-residential 24% 49% Total population +/- external visitors Private dwellings Non-residential Communal ests. Transport Education Employment Residential Temp accomm. Generalized local Family/social Retail Leisure Tourism Healthcare Rail Metro/subway Air Water Road - Census, Mid-Year Population Estimates (MYEs) - Census, Mid-Year Population Estimates (MYEs) - Neighbourhood Statistics, DCSF EduBase, HESA - Census, Annual Business Inquiry, QLFS - VisitBritain, Annual Business Inquiry - VisitBritain - Annual Business Inquiry, commercial sources - ALVA Visitor Statistics, DCMS - ALVA Visitor Statistics, DCMS - Hospital Episode Statistics - National Rail station usage data - DfT Light Rail Statistics, TfL Tube customer metrics - CAA UK Airport Statistics - DfT Sea Passenger Statistics , London River Services - DfT Road Statistics, Annual Average Daily Flow Locations Data Sources -
  • 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
  • 21. Timetable Examples
  • 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
  • 23. Empirical example
  • 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
  • 25. Centroid set
    • 1696 census OAs
    • 3329 workplaces
    • 211 schools and colleges
    • 2 universities
    • Hospitals, stations, airport, etc.
  • 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
  • 27.
    • 02:00 – residents + 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
    Southampton study area Southampton, 200m cells
  • 28. Next steps
  • 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”
  • 30. Pop247.NET program
  • 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
  • 32. Questions?
  • 33. Explanatory notes
  • 34. Centroids, boundaries and grids Centroid locations and boundaries Centroid populations redistributed onto grid
  • 35. Distance decay function Always try to provide a caption next to your picture in this style
  • 36. Southampton - Data Sources
  • 37. Data sources 1 - Southampton
    • Residential population: 2006 small area mid-year estimates (LSOA) allocated to census OAs and unit postcodes, pro rata using 2001 Census and 2006 NSPD weights, subdivided by 5 age bands
    • Workplace population: 2006 Annual Business Inquiry dataset (LSOA) allocated to NSPD unit postcodes, by large/small and 17 SIC categories
    • Education population: NeSS 2005 schools; EduBase private schools; DCSF colleges/FE; HESA univs; student numbers by NSPD locations and age groups
  • 38. Data sources 2 - Southampton
    • Hospital population: HES inpatient, outpatient, A&E numbers and hospital providers datasets 06-07, NSPD locations, NHS etc. sources
    • Visitor attractions: Visit Britain; Association of Large Visitor Attractions; English Heritage, National Trust annual reports etc. 2006 visitor counts, season/open dates etc.
    • Train stations: ORR passenger numbers in/out, grid-referenced
    • Airport passengers: CAA airport terminal and passenger numbers 2006, by month, operator timetables
  • 39. Southampton study area in 6 time slices
  • 40. 02:00 Residential “night-time” model; considerable goods vehicle traffic on motorway & trunk roads Southampton, 200m cells
  • 41. 08:00 Early workplaces, docks, industrial estates; rest as residential; near-peak traffic Southampton, 200m cells
  • 42. 09:00 Workplaces, educational institutions, “daytime” model; low residential; very high central densities; peak traffic volume Southampton, 200m cells
  • 43. 16:00 Workplaces, FE & HE institutions still open, schools closed; low residential; very high central densities Southampton, 200m cells
  • 44. 18:00 Late workplaces remain, education closed; return to residential; high central densities Southampton, 200m cells
  • 45. 21:00 Residential “night-time” model; late night light traffic flow on all roads Southampton, 200m cells