Post on 19-Aug-2020
Pop247NRT: Near real-time spatiotemporal population estimates for health, emergency response and national security
ESRC NEFD Reporting Event
London, 8 December 2017
Samantha Cockings
2
Why do we need space-time specific population estimates?• For decision-making, planning and policy formulation in
many sectors– E.g. emergency planning/response, health, national security
• How many people in specific places and specific times e.g. hourly, daily, weekly, seasonal, one-off events/scenarios?
• Idea of ‘normal’ patterns and ‘current/recent’ situation
• Census data inadequate (even with recent improvements in workplace, daytime populations etc)– Static, residential-based, every 10 years
3
The problem … A typical small area population map
4
Ph
oto
s: D
av
id M
art
in
© Samantha Cockings
© Samantha Cockings
© Samantha Cockings
© Samantha Cockings
5Photos: Samantha Cockings, David Martin
© Samantha Cockings
© Samantha Cockings
© Copyright Elliott Simpson and licensed for reuse under this Creative Commons Licencehttp://www.geograph.org.uk/photo/919011
© Copyright Nigel Davies and licensed for reuse under this Creative Commons Licencehttp://www.geograph.org.uk/photo/21092
6
© David Martin
© Samantha Cockings© David Martin
© Samantha Cockings
Inadequate spatio-temporal resolution
7
• Conventional population map interpreted over time
Ho
me R
esid
ence
Offic
e W
ork
Outd
oors
Work
All E
mp
loym
ent
Oth
er W
ork
Ed
ucation b
y S
tage
All E
ducation
Oth
ers
Ro
ad
s
Tra
nsport
Hubs
0%
20%
40%
60%
80%
100%
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:0022:0000:00
Po
pu
lati
on
Dis
trib
uti
on
(%
)
Time(Hour)
Required spatio-temporal resolution
8
• Integrated multi-source datasets interpreted over time
00:00
02:00
04:0006:00
08:0010:00
12:0014:00
16:0018:00
20:0022:0000:00
Hom
e R
esid
ence
Offic
e W
ork
Outd
oors
Work
Reta
il W
ork
Oth
er
Work
School E
ducatio
n
Hig
her
Educatio
n
Oth
ers
Roads
Tra
nsport
Hubs
0%
20%
40%
60%
80%
100%
Po
pu
lati
on
Dis
trib
uti
on
(%
) .
Time
(Hour)
Previous Population247 research• ESRC Population24/7 project, 2009-2011
– Spatiotemporal population estimates – by time of day, week, term, season, etc.
– General approach/framework
– Software: SurfaceBuilder247 (.NET)
– Sample outputs
• Various related PhD / University of Southampton projects – population exposure to hazards e.g. flooding,
radiation
Martin, D., Cockings, S., & Leung, S. (2015). Developing a flexible framework for spatiotemporal population modeling. Annals of the Association of American Geographers, 105(4), 754-772
doi: 10.1080/00045608.2015.1022089
Locations (origins and destinations)
Time profiles Magnitudes
Background
11
0
500
1000
1500
2000
2500
00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Persons
University of Southampton travel survey: respondents on campus, 15 minute intervals
12
2011 term-time weekday 02:00
Contains National Statistics and Ordnance Survey data © Crown
copyright and database right 2015; Copyright © 2015, Re-used with the
permission of the Health and Social Care Information Centre. All rights
reserved. Contains public sector information licensed under the Open
Government Licence v3;
© OpenStreetMap contributors
13
2011 term-time weekday 14:00
Contains National Statistics and Ordnance Survey data © Crown
copyright and database right 2015; Copyright © 2015, Re-used with the
permission of the Health and Social Care Information Centre. All rights
reserved. Contains public sector information licensed under the Open
Government Licence v3;
© OpenStreetMap contributors
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
Total
population
+/-
external
visitors
Private dwellings
Non-
residential
Communal ests.
Transport
Employment
Education
Residential
Temp accomm.
Generalised local
Family/social
Retail
Leisure
Tourism
Healthcare
Rail
Metro/subway
Air
Water
Road
Locations Data Sources
- Census, Mid-Year Population Estimates (MYEs)
- Census, Mid-Year Population Estimates (MYEs)
- Census, Annual Business Inquiry, QLFS
- EduBase, DCSF school performance tables, HESA
- 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
-
New sources/signals/proxies for more dynamic/less routine activities … e.g.
• Retail – supermarkets, retail centres, high streets
• Leisure – tourist sites, leisure activities, sports events
• Transport – transport hubs e.g. airports, train stations
• One-off/unusual events – festivals, Bank Holidays
Population247 Near Real Time (Pop247NRT) Project • ESRC NEFD Policy Demonstrator Project (Feb 17-May 18*)
– Enhance existing methods and tools for harvesting, processing, integrating and calibrating new, emerging and existing data sources
– Deliver two case studies with project partners to show how methods can be used to inform policy and practice
– Promote knowledge transfer between and beyond partners
• Partners– Public Health England (PHE)
– Defence, Science and Technology Laboratory (Dstl)
– Health and Safety Executive (HSE)
*3-month no-cost extension
Pop247NRT: project team
University of Southampton (UoS)
PHETom Charnock
DstlGlen Hart
HSEWill Holmes
GeographySamantha Cockings
David MartinElectronics and
Computer Science (ECS)Nick GibbinsGeoData
GIS Web
ARCHIVED LIVE DATA
Traffic England
SmartStreetSensor
LIVE FEEDS
Google Opening
Hours
Google Popular Times
Traffic England
New, emerging and existing forms of data
STATIC DATA
ONS SPD v2.0
OS PoI
GeolytixSuper
markets
ORR train station usage
EXISTINGDATASETS
DfT NTM
HESA/ EduBase
Census
HES
“Normal” distributions / Near real-time estimates
Case studies
Retail example 1: stand-alone large stores e.g. supermarkets
Locations + extents
MagnitudesTime profiles
Geolytix locations + size bands
Google Popular Times Geolytix size bandsIndicative footfall – by size band
© Copyright Alex McGregor and licensed for reuse under this Creative Commons Licence
Geolytix - supermarkets
Google Places – Popular Times
Retail example 2: area of mixed retail use e.g. high street, shopping centre
Locations + extents
MagnitudesTime profiles
SmartStreetSensor footfall profiles SmartStreetSensor footfall counts
Census – Classification of Workplace Zones (COWZ)OS Points of Interest – retail locations
© Samantha Cockings
SmartStreetSensor / POI locations
COWZ – Classification of Workplace Zones
Classification of Workplace ZonesRetail Supergroup
Group 1.5: Shop until you drop (782 Workplace Zones) Major retail centres of national and regional significance
Example(s): Meadowhall Shopping Centre, Sheffield; West Quay Shopping Centre, Southampton; Bluewater Shopping Centre, Greenhithe
Image: S9 1EP, WZ: E33009173
High female participation in the workforce, which is young and with above average levels of Black and Asian ethnicities. Very high representation of students and part-time working. Retail and wholesale exceeds all other activities. Travel to work distances are short and percentages travelling on foot or by bicycle are high. This group includes major national and regional retail centres, including large purpose-built out of town and in-town developments.
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
Axi
s Ti
tleTraditional high streets
First working day of normal week
Normal working Tuesday
Normal working Wednesday
Normal working Thursday
Last working day of normal week
Saturday
Sunday
First day of school holidays
Middle of week - school holidays
Last day of week - school holiday
Bank Holidays
December Saturday
Christmas period
Christmas Day/New Year’s Day
SmartStreetSensor data from Consumer Data Research Centre (CDRC)/Local Data Company (LDC)
-1
-0.5
0
0.5
1
1.5
2
Axi
s Ti
tle
Top jobs
First working day of normal week
Normal working Tuesday
Normal working Wednesday
Normal working Thursday
Last working day of normal week
Saturday
Sunday
First day of school holidays
Middle of week - school holidays
Last day of week - school holiday
Bank Holidays
December Saturday
Christmas period
Christmas Day/New Year’s Day
SmartStreetSensor data from Consumer Data Research Centre (CDRC)/Local Data Company (LDC)
Assigning profiles/magnitudes to grid cells
0
200
400
600
800
1000
1200
1400
1600
1800
CC
TV F
oo
tfal
l / S
SS v
alu
e
CCTV
Smart Street Sensor
Calibration: CCTV and SmartStreetSensor data
Leeds City Centre Footfall Data (c) Leeds City Council, 2017, https://datamillnorth.org/dataset/leeds-city-centre-footfall-data. This information is licensed under the terms of the Open Government Licence
SmartStreetSensor data from Consumer Data Research Centre (CDRC)/Local Data Company (LDC)
Calibration: Google Popular Times and SmartStreetSensor data
-1
-0.5
0
0.5
1
1.5
2
00
00
-01
00
01
00
-02
00
02
00
-03
00
03
00
-04
00
04
00
-05
00
05
00
-06
00
06
00
-07
00
07
00
-08
00
08
00
-09
00
09
00
-10
00
10
00
-11
00
11
00
-12
00
12
00
-13
00
13
00
-14
00
14
00
-15
00
15
00
-16
00
16
00
-17
00
17
00
-18
00
18
00
-19
00
19
00
-20
00
20
00
-21
00
21
00
-22
00
22
00
-23
00
0
10
20
30
40
50
60
70
Po
pu
lar
tme
s
Popular times: Tuesday
SSS: Tuesday
SmartStreetSensor data from Consumer Data Research Centre (CDRC)/Local Data Company (LDC)
Domains
OriginsUsual residentsSpecial populationsVisitors
DestinationsWorkplacesEducationHealthRetailLeisureTransport hubs
BackgroundDfT - 19 NTM periodsTraffic England – Day types
What constitutes a case study?
• The use of Pop247NRT population estimates to inform the research, practice and/or policy of one or more partners
– Develop data libraries for study area
– Use SurfaceBuilder247 to generate population estimates for specific times/places
– Integrate and analyse population estimates in case studies with partners
Study area
Case study 1: PHE• Scenario modelling for nuclear power plant (NPP) licensing
and planning• Evaluation for use in PHE role in radiation emergency
response • PACE: PHE’s Probabilistic Accident Consequence Evaluation
software– Release parameters (type, amount, length of release etc.)– Meteorological factors (wind direction, speed etc.)– Population density (age, currently static night-time data)
• Seed PACE with Pop247NRT space-time specific population estimates– by age, on site/in-transit, visitor/resident, time of day, type of
day e.g. normal, term/not-term, school holidays, public holidays, unusual events etc.) for 1 year (nominally 2016)
• Evaluate potential health outcomes
Example of four distinct plumes generated under different meteorological conditions for a single accident sequence at a hypothetical NPP in Bristol study area
© Public Health England
Example output: probability of the mean number of fatalities in each grid square being greater than 0.1, with a static adult population assumption
© Public Health England
Case study 2: HSE
• Comparison of insights gained from using Pop247NRT and HSE’s National Population Database (NPD) models
• NPD– Developed and maintained by HSE
– GIS-based model of population
– Population estimates for objects (buildings, transport network), for themes (e.g. residential, workplace) and specific time periods (e.g. daytime term-time)
• Implement both models and compare outputs - either for PHE case study or for HSE-led case study (workplace-based incident)
Case study 3: Dstl
A feasibility assessment to indicate:
• Whether the Pop247NRT models might be used in other (data poor) countries?
• Which datasets are essential?
• Which methods can be readily translated and implemented in other data settings?
• What are the barriers to implementation?
Timeline
Feb-Dec 2017 Jan-Apr 2018 Apr 2018 Apr-May 2018
Data library preparation
Case studies Stakeholder workshop
Reporting/data depositing
Planned outputs• Methods/tools
• Data libraries
• Processed population estimates
• Data and findings from case studies
• Policy brief + podcast, incorporating findings from stakeholder workshop
• Project report(s)
• Contributions to conferences, seminars, other events
• Academic papers
Dissemination/communication activities
• Partner meetings (04/04/17 @Dstl; 08/11/17 @UoS + email)
• Contributions to conferences/seminars/events:– Dstl/NGA Workshop on Place Intelligence, London, Aug 2017
– SmartPop Seminar, Brussels, Oct 2017
– European Forum for Geography and Statistics Conference, Dublin, Nov 2017
– [GIS Research UK Conference, Leicester, April 2018]
• Stakeholder Workshop (April 2018)
• Website, Twitter feed
Stakeholder workshop
• 26 April 2018, PHE (Chilton, Oxfordshire)
• Aims
– Showcase data, methods, tools, case studies
– Identify opportunities and challenges in implementing these for decision-making and policy formulation
– How to scale-up, within and beyond partners’ sectors
• Participants
– c. 35 total: 15 project team + c.5 invitees per partner
• Outputs
– Policy brief + podcast
Projected policy/non-policy impacts
• Policy and practice– Raised awareness within/beyond partners– Collaborative development of methods/tools– New understanding of population distributions– Examples of use of outputs in policy/practice– Identification of implementation challenges– Longer-term: more efficient and effective decision-making/
planning/policy
• Academic– Enhanced methods/tools for analysing NEFD & existing datasets– Data libraries and processed layers (open where possible)– Insights into population distributions– Of interest to any researchers/practitioners generating or using
population estimates + ONS (Census Transformation Programme)
Acknowledgements
• Economic and Social Research Council Award ES/P010768/1
• Glen Hart, William Holmes, Tom Charnock, Nick Gibbins, David Martin, GeoData team
44