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Including the urban heat island in spatial heat health risk assessment strategies: a case study for Birmingham, UK

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Author(s): Tomlinson Charlie | Chapman Lee | Thornes John | Baker Christopher

Journal: International Journal of Health Geographics
ISSN 1476-072X

Volume: 10;
Issue: 1;
Start page: 42;
Date: 2011;
Original page

Keywords: Urban Heat Island | UHI | Birmingham | Experian | Heat Risk | Spatial Risk Assessment | GIS | Remote Sensing | MODIS

ABSTRACT
Abstract Background Heatwaves present a significant health risk and the hazard is likely to escalate with the increased future temperatures presently predicted by climate change models. The impact of heatwaves is often felt strongest in towns and cities where populations are concentrated and where the climate is often unintentionally modified to produce an urban heat island effect; where urban areas can be significantly warmer than surrounding rural areas. The purpose of this interdisciplinary study is to integrate remotely sensed urban heat island data alongside commercial social segmentation data via a spatial risk assessment methodology in order to highlight potential heat health risk areas and build the foundations for a climate change risk assessment. This paper uses the city of Birmingham, UK as a case study area. Results When looking at vulnerable sections of the population, the analysis identifies a concentration of "very high" risk areas within the city centre, and a number of pockets of "high risk" areas scattered throughout the conurbation. Further analysis looks at household level data which yields a complicated picture with a considerable range of vulnerabilities at a neighbourhood scale. Conclusions The results illustrate that a concentration of "very high" risk people live within the urban heat island, and this should be taken into account by urban planners and city centre environmental managers when considering climate change adaptation strategies or heatwave alert schemes. The methodology has been designed to be transparent and to make use of powerful and readily available datasets so that it can be easily replicated in other urban areas.

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