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- Publisher Website: 10.3390/ijerph121215046
- Scopus: eid_2-s2.0-84951060639
- PMID: 26694445
- WOS: WOS:000367539000088
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Article: A spatial framework to map heat health risks at multiple scales
Title | A spatial framework to map heat health risks at multiple scales |
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Authors | |
Keywords | Extremely hot weather event Heat risk Heat vulnerability Modifiable areal unit problem |
Issue Date | 2015 |
Citation | International Journal of Environmental Research and Public Health, 2015, v. 12, n. 12, p. 16110-16123 How to Cite? |
Abstract | © 2015 by the authors; licensee MDPI, Basel, Switzerland. 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. |
Persistent Identifier | http://hdl.handle.net/10722/265684 |
ISSN | 2019 Impact Factor: 2.849 2023 SCImago Journal Rankings: 0.808 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ho, Hung Chak | - |
dc.contributor.author | Knudby, Anders | - |
dc.contributor.author | Huang, Wei | - |
dc.date.accessioned | 2018-12-03T01:21:22Z | - |
dc.date.available | 2018-12-03T01:21:22Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | International Journal of Environmental Research and Public Health, 2015, v. 12, n. 12, p. 16110-16123 | - |
dc.identifier.issn | 1661-7827 | - |
dc.identifier.uri | http://hdl.handle.net/10722/265684 | - |
dc.description.abstract | © 2015 by the authors; licensee MDPI, Basel, Switzerland. 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. | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Environmental Research and Public Health | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Extremely hot weather event | - |
dc.subject | Heat risk | - |
dc.subject | Heat vulnerability | - |
dc.subject | Modifiable areal unit problem | - |
dc.title | A spatial framework to map heat health risks at multiple scales | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/ijerph121215046 | - |
dc.identifier.pmid | 26694445 | - |
dc.identifier.scopus | eid_2-s2.0-84951060639 | - |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 12 | - |
dc.identifier.spage | 16110 | - |
dc.identifier.epage | 16123 | - |
dc.identifier.eissn | 1660-4601 | - |
dc.identifier.isi | WOS:000367539000088 | - |
dc.identifier.issnl | 1660-4601 | - |