A Spatial Framework to Map Heat Health Risks at Multiple Scales

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming...

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Main Authors: Hung Chak Ho, Anders Knudby, Wei Huang
Format: Article
Language:English
Published: MDPI AG 2015-12-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:http://www.mdpi.com/1660-4601/12/12/15046
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spelling doaj-f60ea3562efc490bbbd3608760fa348b2020-11-24T23:05:45ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012015-12-011212161101612310.3390/ijerph121215046ijerph121215046A Spatial Framework to Map Heat Health Risks at Multiple ScalesHung Chak Ho0Anders Knudby1Wei Huang2Department of Geography, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaDepartment of Geography, University of Ottawa, ON, K1N 6N5, CanadaDepartment of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USAIn the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord Gi index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord Gi index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.http://www.mdpi.com/1660-4601/12/12/15046heat riskmodifiable areal unit problemheat vulnerabilityextremely hot weather event
collection DOAJ
language English
format Article
sources DOAJ
author Hung Chak Ho
Anders Knudby
Wei Huang
spellingShingle Hung Chak Ho
Anders Knudby
Wei Huang
A Spatial Framework to Map Heat Health Risks at Multiple Scales
International Journal of Environmental Research and Public Health
heat risk
modifiable areal unit problem
heat vulnerability
extremely hot weather event
author_facet Hung Chak Ho
Anders Knudby
Wei Huang
author_sort Hung Chak Ho
title A Spatial Framework to Map Heat Health Risks at Multiple Scales
title_short A Spatial Framework to Map Heat Health Risks at Multiple Scales
title_full A Spatial Framework to Map Heat Health Risks at Multiple Scales
title_fullStr A Spatial Framework to Map Heat Health Risks at Multiple Scales
title_full_unstemmed A Spatial Framework to Map Heat Health Risks at Multiple Scales
title_sort spatial framework to map heat health risks at multiple scales
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1660-4601
publishDate 2015-12-01
description In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord Gi index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord Gi index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.
topic heat risk
modifiable areal unit problem
heat vulnerability
extremely hot weather event
url http://www.mdpi.com/1660-4601/12/12/15046
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