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首页> 外文期刊>International Journal of Environmental Research and Public Health >A Spatial Framework to Map Heat Health Risks at Multiple Scales
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A Spatial Framework to Map Heat Health Risks at Multiple Scales

机译：在多个尺度上绘制热健康风险的空间框架

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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.
机译：在过去的几十年中，极端高温事件已导致大量的过高死亡率，最引人注目的是2003年在中欧，2010年在俄罗斯，甚至在2009年在加拿大温哥华等地势较凉的地区。与热有关的发病率和死亡率是由于气候驱动的全球极端热事件的严重性和频率增加，预计在未来的几个世纪中会增加。可将有关热暴露和人口脆弱性的空间信息结合起来，以绘制出最高风险的区域并集中精力进行缓解工作。但是，热暴露和脆弱性数据之间的空间分辨率不匹配会导致空间尺度问题，例如可修改的地域单位问题（MAUP）。我们使用基于栅格的模型将热暴露和脆弱性数据集成到多标准决策分析中，并将其与传统的基于矢量的模型进行比较。然后，我们使用Getis-Ord Gi索引来生成从精细到粗略的空间尺度的空间平滑的热风险热点图。基于栅格的模型允许以空间分辨率生成地图，更多地描述局部尺度的热风险可变性，以及识别未使用基于矢量的方法识别的热风险区域。用Getis-Ord Gi指数进行空间平滑会产生从局部到区域空间尺度的热风险热点。该方法是一个框架，用于减少将来的热风险图谱中的空间尺度问题，并用于在从区块级别到市政级别的空间尺度上标识热风险热点。

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