Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic meteorological station data
Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic meteorological station data
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通过耦合社交媒体数据和自动气象站数据，显式空间化热暴露风险和当地相关因素
DOI：
10.1016/j.envres.2020.109813
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发表时间：
2020
影响因子：
8.3
通讯作者：
Wenchuan Guan
中科院分区：
文献类型：
--
作者：
Zheng Cao;Zhifeng Wu;Shaoying Li;Guanhua Guo;Song;Song;Yujiao Deng;Wenjun Ma;Hui Sun;Wenchuan Guan
极端高温是与天气相关的公共卫生问题的一个主要原因，预计将会加剧并变得更加频繁。为了减轻不利影响，应开发低成本且有效的风险评估方法。因此，我们应用自动气象站数据和人口流动数据开发了高时空分辨率的温度风险评估方法。人口流动分析结果显示天河区的工住复合格局，空间集聚热点位于研究区的北部、西南部和东南部。考虑到人口流动模式，获得了分辨率为100 m的高温风险评估结果。使用2014年和2015年的总死亡病例来验证这一结果。验证显示，高温风险地区总死亡人数占天河区的36%以上。因此，本研究引入的方法能够反映与天气相关的风险。此外，高温风险评估结果显示，高温风险区大部分位于研究区西南部。确定了风险区域的两个高峰时间，即黎明前和晚上。与工作日相比，周末风险区域有所扩大。此外，我们使用地理加权回归模型来研究潜在的影响因素。个体因素对热暴露空间分布的贡献超过22.4%。餐饮服务、交通服务和生活服务高于其他服务，均值R2值分别为0.28、0.23和0.25。超过47.9%的热暴露空间分布归因于影响因素的联合作用，全局R2范围为0.23~0.34。我们的研究引入了一种特定空间的方法来定量评估高温风险。此外，还讨论了高温风险空间分布背后的机制。理论和管理意义可以帮助城市设计师和能源管理者制定有用的策略来减轻与天气相关的公共卫生风险。
Extremely high temperatures, a major cause for weather-related public health issues, are projected to intensify and become more frequent. To mitigate the adverse effects, a low-cost and effective risk assessment method should be developed. Therefore, we applied automatic meteorological station data and population mobility data to develop a high spatiotemporal resolution temperature risk assessment method. The population mobility analysis results showed the working/residential complex pattern in Tianhe District, with hotspots of spatial clustering located in the north, southwest, and southeast of the study area. Taking the population mobility patterns into consideration, high-temperature risk assessment results with a resolution of 100 m were obtained. The total mortality cases in 2014 and 2015 were used to validate this result. The validation showed that the total mortality in the high-temperature risk areas accounted for over 36% of that in Tianhe District. Thus, the method introduced in this study is capable of reflecting weather-related risk. Furthermore, the high-temperature risk assessment results showed that most of the risky areas were located in the southwest of the study area. Two peak times of the risk areas were determined, being before dawn and in the evening. Compared with the risk areas during weekdays, those at weekends expanded. In addition, we used the geographically weighted regression model to investigate the potential influencing factors. Individual factor contributed more than 22.4% to the spatial distribution of heat exposure. Catering services, transportation services, and living services were higher than others, with meanR2values of 0.28, 0.23, and 0.25, respectively. More than 47.9% of spatial distribution of heat exposure was attributed to joint function of influencing factors, with globalR2ranged from 0.23 to 0.34. Our research introduces a spatial-specific method to quantitatively assess high-temperature risk. Moreover, the mechanisms behind the spatial distribution of the high-temperature risk were discussed. The theoretical and management implications can help urban designers and energy governors to develop useful strategies to mitigate weather-related public health risks.