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