Session 1B, Presentation 2

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
http://www.southampton.ac.uk/geography/research/phew/pop247/index.html
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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
4

5. Currently mapped locations
Photos: David Martin, Sam Cockings 5

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
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7. “Uncharted” locations 1
Photos: David Martin 7

8. Photos:
David Martin
“Uncharted” locations 2
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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‟ 10

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
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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. 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
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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)
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15. Time profile example – school
Population
In transit
Present
00 06 12 18 00 Time of day
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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
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17. Data sources

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
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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
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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
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21. Timetable Examples
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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
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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
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25. Centroid set
• 1696 census
OAs
• 3329
workplaces
• 211 schools
and colleges
• 2
universities
• Hospitals,
stations,
airport, etc.
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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
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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
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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”
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30. Pop247.NET
program
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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
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32. Questions?