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Spatio-temporal population modelling for enhanced assessment of urban exposure to flood risk

Spatio-temporal population modelling for enhanced assessment of urban exposure to flood risk
Spatio-temporal population modelling for enhanced assessment of urban exposure to flood risk
There is a growing need for high resolution spatio-temporal population estimates which allow accurate assessment of population exposure to natural hazards. Current approaches to population estimation are usually limited either by the use of arbitrary administrative boundaries or insufficient resolution in the temporal dimension. The innovative approach proposed here combines the use of a spatio-temporal gridded population model with flood inundation data to estimate time-specific variations in population exposed to natural hazards. The approach is exemplified through an application centred on Southampton (UK) using Environment Agency flood map inundation data. Results demonstrate that large fluctuations occur over time in the population distribution within flood risk zones. Variations in the spatio-temporal distribution of population subgroups are explored. Analysis using GIS indicates a diurnal shift in exposure between fluvial and tidal flooding, particularly attributable to the movement of the working age population. This illustrates the improvements achievable to flood risk management as well as potential application to other natural hazard scenarios both within the UK and globally.
spatio-temporal modelling, population surface modelling, natural hazards, vulnerability, urban exposure
1874-463X
145-163
Smith, Alan
63ec33c7-fa1d-41ae-a0e1-5a96b7140664
Martin, David J.
e5c52473-e9f0-4f09-b64c-fa32194b162f
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Smith, Alan
63ec33c7-fa1d-41ae-a0e1-5a96b7140664
Martin, David J.
e5c52473-e9f0-4f09-b64c-fa32194b162f
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800

Smith, Alan, Martin, David J. and Cockings, Samantha (2016) Spatio-temporal population modelling for enhanced assessment of urban exposure to flood risk. Applied Spatial Analysis and Policy, 9 (2), 145-163. (doi:10.1007/s12061-014-9110-6).

Record type: Article

Abstract

There is a growing need for high resolution spatio-temporal population estimates which allow accurate assessment of population exposure to natural hazards. Current approaches to population estimation are usually limited either by the use of arbitrary administrative boundaries or insufficient resolution in the temporal dimension. The innovative approach proposed here combines the use of a spatio-temporal gridded population model with flood inundation data to estimate time-specific variations in population exposed to natural hazards. The approach is exemplified through an application centred on Southampton (UK) using Environment Agency flood map inundation data. Results demonstrate that large fluctuations occur over time in the population distribution within flood risk zones. Variations in the spatio-temporal distribution of population subgroups are explored. Analysis using GIS indicates a diurnal shift in exposure between fluvial and tidal flooding, particularly attributable to the movement of the working age population. This illustrates the improvements achievable to flood risk management as well as potential application to other natural hazard scenarios both within the UK and globally.

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Accepted/In Press date: 4 July 2014
e-pub ahead of print date: 1 August 2014
Published date: June 2016
Keywords: spatio-temporal modelling, population surface modelling, natural hazards, vulnerability, urban exposure
Organisations: Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 367728
URI: http://eprints.soton.ac.uk/id/eprint/367728
ISSN: 1874-463X
PURE UUID: ec80dd92-4e24-4c96-82ab-491c54a028cb
ORCID for David J. Martin: ORCID iD orcid.org/0000-0003-0397-0769
ORCID for Samantha Cockings: ORCID iD orcid.org/0000-0003-3333-4376

Catalogue record

Date deposited: 06 Aug 2014 10:12
Last modified: 15 Mar 2024 03:10

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Contributors

Author: Alan Smith
Author: David J. Martin ORCID iD

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