Cockings, Samantha

Martin, David

Smith, Alan

English

Southampton (e-Prints Soton)

Geography and Environment  24/7 Population modelling for natural hazard assessment  Alan Smith University of Southampton, UK Royal Geographical Society Annual Conference 2013 Acknowledgments Developing the “Pop 24/7” methodologies: Professor David Martin Dr Samantha Cockings  http://www.southampton.ac.uk/geography/research/projects/space_time.page 2  3 Background • Better population estimations are required for hazard risk assessment • Censuses typically provide a decadal ‘night-time’ population estimation • This does not take into account the large fluxes of temporary populations during the day • Events of 2011/12 have focused global attention on natural hazards and their impacts 4 5 Southampton, UK Conventional density maps Southampton 6 Cruise and  freight terminals University Airport Sporting grounds City centre  developments Train station Retail centre Ferry 7  “Pop 24/7” overview • Spatio-temporal gridded population modelling – Variable kernel density estimation (KDE) – Utilises population centroids – Redistributes resident populations according to a temporal profile – Population subgroups • Removal of arbitrary administrative boundaries • Allows locations of zero population density (Eg. Water)   Analytical overview 8 Model database Spatial extentTarget time HH:MM DD/MM/YYYPopulation/SB247Hazard footprintValidation log Run logBase mapData validated?Origin centroidsDestination centroidsTemporal profilesBackground maskRun population modelNoYesPopulation surface Session parametersProbable extentPopulation exposureGIS analysisResults databaseVulnerability indicatorsSpace-time interpolation 9 Temporal Profile A retail example of a temporal profile derived from the Time Use Survey 2000 indicating potential shopper numbers for a given time. 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 00:00:00  02:30:00  05:00:00  07:30:00  10:00:00  12:30:00  15:00:00  17:30:00  20:00:00  22:30:00 Time Mon-Fri Saturday Sunday P(Respondents Shopping) 10 11  Results • Variable grid size, currently using 200 metre resolution • Visualization for public communication • Population weighted to background mask • Combination and analysis with hazard footprint data • Application to a UK flooding scenario, using the Environment Agency’s ‘Flood Map’.   12 13 14 Example weekday population 15 Example weekday population 16 Example weekday population 17 Example weekday population 18 Example weekday population 19 Example weekday population 20 Example weekday population 21 Example weekday population 22 23 24 25 26 Two flood risk case studies: Ulley • Destabilized 25/07/2007 • M1 J32-34 closed 40 hours • 1000 evacuated • High voltage pylons • High pressure gas main  27 St Austell 28 29  Next steps • Further analysis of data  • Continued development of datasets and temporal profiles • Demonstrate improved exposure estimations • Advances in natural hazard risk management • Many more potential applications…  Contact: Alan.Smith@soton.ac.uk  Geography and Environment University of Southampton Southampton, UK SO17 1BJ        @NatHazard  www.personal.soton.ac.uk/ads4g11    

24/7 population modelling for natural hazard assessment

Abstract

Similar works

Full text

Available Versions

Southampton (e-Prints Soton)