Population 24/7. David Martin, University of Southampton

Transcription

1 Population 24/7 David Martin, University of Southampton Demographics User Group 18 March 2011

2 Presentation overview Acknowledgement: Samantha Cockings and Samuel Leung; ESRC award RES Small area spatial population distributions The time dimension Data considerations Modelling and visualizing population 24/7 2

3 Small area spatial population distributions

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5 Small area spatial population distributions Resource allocation: large areas > small areas Targeting services/marketing Site location decisions/transportation demand Denominator populations BUT focus on residential population base census, surveys, administrative data; also tendency to use irregular zones based on residential geography 5

6 Photos: David Martin, Sam Cockings 6

7 The time dimension

8 Need for better time-space distributions Conventional population mapping whether area-based or gridded focused on residential night-time populations Widespread demand for population maps which are more temporally appropriate, in two ways: Up to date (chronological time) Relating to a relevant time period (cyclical time) Especially where population exposure is concerned: emergency planning, exposure to risk, services to dynamic populations, etc. 8

9 Photos: David Martin 9

10 Photos: David Martin 10

11 Space-time population modelling Where tried, the general approach is to start with nighttime population model/map and transfer population subgroups to specific daytime locations, e.g. schools, workplaces Various recent application examples, particularly driven by emergency planning and modelling of population exposure to hazards In reality, many different timescales to be modelled, not just simple daytime and night-time Longstanding difficulty of obtaining data with sufficient space/time resolution for the non-residential addresses

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13 Next steps Already have a method for building gridded population models (more later) We want to be able to build time-specific gridded population models This requires constructing conceptual and practical models for time-specific population activities These need to be operationalised using existing, relevant data 13

14 Data considerations

15 100% Population Distribution (%) 80% 60% 40% 20% 0% Home Residence Office Work Outdoors Work All Employment Other Work Education by Stage Conventional population map interpreted over time All Education Others Roads Transport Hubs 00:00 22:00 20:00 18:00 16:00 14:00 12:00 10:00 08:00 06:00 04:00 02:00 00:00 Time (Hour) 15

16 Residential Total population +/- external visitors Nonresidential Transport

17 Residential Locations Private dwellings Communal ests. Total population +/- external visitors Nonresidential Employment Education Temp accomm. Healthcare Family/social Retail Leisure Tourism Generalised local Transport Road Rail Metro/subway Air Water

18 Locations Data Sources Residential Private dwellings Communal ests. - Census, Mid-Year Population Estimates (MYEs) - Census, Mid-Year Population Estimates (MYEs) Total population +/- external visitors Nonresidential Employment Education Temp accomm. Healthcare Family/social Retail Leisure Tourism Generalised local - Census, Annual Business Inquiry, QLFS - EduBase, DCSF school performance tables, HESA - VisitBritain, Annual Business Inquiry - Hospital Episode Statistics - VisitBritain - Annual Business Inquiry, commercial sources - ALVA Visitor Statistics, DCMS - ALVA Visitor Statistics, DCMS - Transport Road Rail Metro/subway Air Water - DfT Road Statistics, Annual Average Daily Flow - National Rail station usage data - DfT Light Rail Statistics, TfL Tube customer metrics - CAA UK Airport Statistics - DfT Sea Passenger Statistics, London River Services Acronyms: QLFS Quarterly Labour Force; DCSF Department for Children, Schools and Families; HESA Higher Education Statistics Agency; 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

19 Locations Data Sources Residential Private dwellings Communal ests. - Census, Mid-Year Population Estimates (MYEs) - Census, Mid-Year Population Estimates (MYEs) Total population +/- external visitors Nonresidential Employment Education Temp accomm. Healthcare Family/social Retail Leisure Tourism Generalised local - Census, Annual Business Inquiry, QLFS - EduBase, DCSF school performance tables, HESA - VisitBritain, Annual Business Inquiry - Hospital Episode Statistics - VisitBritain - Annual Business Inquiry, commercial sources - ALVA Visitor Statistics, DCMS - ALVA Visitor Statistics, DCMS - Transport Road Rail Metro/subway Air Water - DfT Road Statistics, Annual Average Daily Flow - National Rail station usage data - DfT Light Rail Statistics, TfL Tube customer metrics - CAA UK Airport Statistics - DfT Sea Passenger Statistics, London River Services Acronyms: QLFS Quarterly Labour Force; DCSF Department for Children, Schools and Families; HESA Higher Education Statistics Agency; 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

20 Locations Data Sources Residential Private dwellings Communal ests. - Census, Mid-Year Population Estimates (MYEs) - Census, Mid-Year Population Estimates (MYEs) Total population +/- external visitors Nonresidential Employment Education Temp accomm. Healthcare Family/social Retail Leisure Tourism Generalised local - Census, Annual Business Inquiry, QLFS - EduBase, DCSF school performance tables, HESA - VisitBritain, Annual Business Inquiry - Hospital Episode Statistics - VisitBritain - Annual Business Inquiry, commercial sources - ALVA Visitor Statistics, DCMS - ALVA Visitor Statistics, DCMS - Transport Road Rail Metro/subway Air Water - DfT Road Statistics, Annual Average Daily Flow - National Rail station usage data - DfT Light Rail Statistics, TfL Tube customer metrics - CAA UK Airport Statistics - DfT Sea Passenger Statistics, London River Services Acronyms: QLFS Quarterly Labour Force; DCSF Department for Children, Schools and Families; HESA Higher Education Statistics Agency; 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

21 100% Population Distribution (% ). 80% 60% 40% 20% 0% Home Residence Office Work Outdoors Work Retail Work Other Work School Education Integrated multi-source datasets interpreted over time Higher Education Others Roads Transport Hubs 00:00 22:00 20:00 18 : : : : :0 0 08:00 06:00 04:00 02:00 00:00 Time (Hour) 21

22 Modelling and visualizing population 24/7

23 Spatial modelling framework Builds on existing grid modelling methodology developed for use with conventional census data One of a variety of approaches to reallocation of population counts from one set of geographical features to another Uses adaptive kernel estimation to generate gridded population estimates from input points ( centroids ) A key advantage of gridded models is continuity of spatial units through time (i.e. no boundary changes) 23

24 Centroids, boundaries and grids Centroid locations and boundaries Centroid populations redistributed onto grid 24

25 Centroid set Gridded surface (from postcodes)

26 time Study area a at time t t study area a

27 time Background layer for time t background layer b t study area a

28 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 (2006)

29 time Adjust for visitors + visitors in - visitors out background layer b t study area a

30 time Destination centroid i at time t centroid i background layer b local extent d area of influence j t study area a

31 time Origin centroid within area of influence j centroid i background layer b local extent d area of influence j t study area a

32 Time-space data handling Requires extensive library of centroid locations Scope of input centroids defines the scope of output model Conventional residential locations with population totals (e.g. postcodes, census output areas) All other locations, with population capacities, time profiles and areas of influence (e.g. schools, hospitals, workplaces) Population further subdivided into sub-groups e.g. by age 32

33 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 Opening hours by various services readily obtainable (schools, etc.) Quarterly Labour Force Survey for workforce time profiles (daytime, evening, night working, hours worked, days worked by SIC categories) 33

34 Time profile example school Population In transit Present Time of day 34

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36 Southampton, 200m cells 16:00 Workplaces, FE & HE institutions still open, schools closed; low residential; very high central densities 36

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38 Next steps Testing software and building demonstrator data Web science applications - could be using linked data Business activity data esp. customers/visitors Need for validation data: mobile telecomms? Talking to potential users Application projects PhD studentships?

39 Acknowledgements Economic and Social Research Council award number RES 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 Mastermap ITN layer: Crown Copyright/database right 2009, an Ordnance Survey/EDINA supplied service. 39

40 Questions, discussion.