Pop247NRT: Near real-time spatiotemporal population estimates for health, emergency response and national security

ESRC NEFD Reporting Event

London, 8 December 2017

Samantha Cockings

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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

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The problem … A typical small area population map

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© 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

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© David Martin

© Samantha Cockings© David Martin

© Samantha Cockings

Inadequate spatio-temporal resolution

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• Conventional population map interpreted over time

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Required spatio-temporal resolution

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• Integrated multi-source datasets interpreted over time

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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

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Persons

University of Southampton travel survey: respondents on campus, 15 minute intervals

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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

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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

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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

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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.

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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)

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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

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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

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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

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s.cockings@soton.ac.uk@SamCGeog @population247

Pop247NRT