How does population exposure to flood risk vary by time of day?

Dr Alan SmithGeography and Environment, University of SouthamptonCoastal Seminar Series, 9 December 2015

A ‘transient’ population at London Waterloo: Geograph/David Martin

Population estimates are often static and uniformly spread across

arbitrarily areal units

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Background• Better population estimates are required for hazard risk

assessment

• Censuses typically provide a decadal ‘night-time’ population estimation

• This does not take into account the large seasonal fluxes of temporary populations during the day

• Application of the Population 24/7 tool to produce estimates of population distribution

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Population 24/7• Redistributes population from ‘origin’ to ‘destination’

locations

• Population subgroups can be considered (e.g. age)

• Movements governed by temporal profiles

• ‘In travel’ population weighted to a background mask

• Spatiotemporal gridded population output

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Population subgroups• Seven age subgroups for this study:

– 0-3, 4-10, 11-15, 16-64 (FE/HE), 16-64 and >65

• Discernible temporal characteristics by age

• Other characteristics? E.g. ethnicity?

– Jane Fielding (2007)

– Temporal behaviour more problematic?

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2001 Census (Southampton UA) Uniform population density in

contiguous zones

Gridded (100 m) ‘weekday’ 12:00 example

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100 m gridded density

UniversityCity centre

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St Austell, Cornwall case study• Popular tourist destination and largest town in Cornwall

• 15 x 20 km study area

• Hourly population estimates at 100 m resolution

• Large seasonal population fluctuations

• Identified a +10,000 peak in summer population

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Pop24/7 origin centroids• Usually resident term time population (2010 MYE)

• Usually resident non-term time population (2010 MYE)

• Seasonal visitor population

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Pop24/7 destination centroids• Workplaces (Annual Business Inquiry 2010)

• Education: schools, FE, HE (students)

• Healthcare: in/out/A&E patients

• Retail centres

• Leisure/attractions

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Seasonal visitor population• Model datasets based on the work undertaken by Newing et

al at Leeds

• Visitor survey and occupancy data used to construct population estimates at the unit postcode level

Month 2010 (season) ‘Resident’ visitor est.

January (low) 1,000

May (fringe) 6,000

August (peak) 12,400

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Leisure destinations• Location: georeferenced unit postcode

• Population capacity: VisitEngland, English Heritage (EH), National Trust

• Temporal profile

– Daily: Time Use Survey

– Seasonal: EH data for representative attraction

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Constraints on population redistribution

Usual residents

‘Resident’ visitors

Workplaces Education Healthcare Leisure Retail

Origin Destination

Model dataset Key:

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Flood exposure calculations• Population and flood models: loosely coupled approach

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Flood risk and LISFLOOD-FP• Environment Agency flood map (1 in 100 year river, 200

year coastal)

• LISFLOOD 1 in 100, 250 and 500 year event inundation extents

– Explicitly accounts for defences and buildings

– CEH return periods for a given rainfall event

– Depth and velocity estimates at 5 m resolution

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LISFLOOD-FP inundation extents

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Results

Detailed observation of local scale population variation

E.g. campsites, retail centres, attractions

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Seasonal population flood risk exposure

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Summary• More realistic, seasonally varying, spatiotemporal

population estimates

• Ability to make fatality predictions based on improved knowledge of population exposure, flood depth and velocity

• Advances in natural hazard risk management

• Compatibility with environmental applications (?)

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The future?• Coastal Communities (2011 Census)

• Widening seasonal population estimates

• Google’s ‘popular times’ feature

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Questions?Alan.Smith@soton.ac.uk

Link to slides:

Papers and further information:

Smith, A.D., Newing A., Quinn, N., Martin D., Cockings, S. and Neal, J. (2015) Assessing the impact of seasonal population fluctuation on regional flood risk management. International Journal of Geo-Information 4(3):1118-1141

Smith, A.D., Martin, D. and Cockings, S. (2014 online first) Spatio-temporal population modelling for enhanced assessment of urban exposure to flood risk. Applied Spatial Analysis and Policy

http://www.southampton.ac.uk/geography/research/projects/space_time.page

@DrAlanSmith_

Research supported by the Economic and Social Research Council

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