文献类型：期刊文献

英文题名：Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China

作者：Zhi, Guoqing[1,2];Meng, Bin[1,2];Wang, Juan[1,2];Chen, Siyu[1,2];Tian, Bin[1,2];Ji, Huimin[1,2];Yang, Tong[1,2];Wang, Bingqing[1,2];Liu, Jian[3]

第一作者：Zhi, Guoqing

通讯作者：Meng, B[1];Meng, B[2]

机构：[1]Beijing Union Univ, Coll Appl Arts & Sci, 197 Beitucheng West Rd, Beijing 100191, Peoples R China;[2]Beijing Union Univ, Lab Urban Cultural Sensing & Comp, 197 Beitucheng West Rd, Beijing 100191, Peoples R China;[3]Capital Normal Univ, Coll Resource Environm & Tourism, 105 West 3rd Ring Rd North, Beijing 100048, Peoples R China

第一机构：北京联合大学应用文理学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Arts & Sci, 197 Beitucheng West Rd, Beijing 100191, Peoples R China;[2]corresponding author), Beijing Union Univ, Lab Urban Cultural Sensing & Comp, 197 Beitucheng West Rd, Beijing 100191, Peoples R China.|[11417]北京联合大学;[114172]北京联合大学应用文理学院;

年份：2021

卷号：13

期号：20

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20214311054943);Scopus(收录号：2-s2.0-85117361619);WOS:【SCI-EXPANDED(收录号:WOS:000719774900001)】；

基金：This research was funded by National Key Research and Development Program of China, grant number 2017YFB0503605; National Natural Science Foundation of China, grant number 41671165; Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality, grant number CIT & TCD201904070 and this research was funded by Beijing Union University, grant number ZK40202001, RB202101 and YZ2020K001.

语种：英文

外文关键词：heatwave events; residential sensitivity to HWEs; social media Big Data; spatial match of sensitivity and HWEs; China

摘要：Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi'an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.