Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China
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 so...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/20/4086 |
| _version_ | 1797513189670256640 |
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| author | Guoqing Zhi Bin Meng Juan Wang Siyu Chen Bin Tian Huimin Ji Tong Yang Bingqing Wang Jian Liu |
| author_facet | Guoqing Zhi Bin Meng Juan Wang Siyu Chen Bin Tian Huimin Ji Tong Yang Bingqing Wang Jian Liu |
| author_sort | Guoqing Zhi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:14:10Z |
| format | Article |
| id | doaj.art-4a1e49fecd4b4d1680e6fab3e0cde10d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:14:10Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4a1e49fecd4b4d1680e6fab3e0cde10d2023-11-22T19:54:02ZengMDPI AGRemote Sensing2072-42922021-10-011320408610.3390/rs13204086Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in ChinaGuoqing Zhi0Bin Meng1Juan Wang2Siyu Chen3Bin Tian4Huimin Ji5Tong Yang6Bingqing Wang7Jian Liu8College of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Applied Arts and Sciences, Beijing Union University, No.197 Beitucheng West Road, Beijing 100191, ChinaCollege of Resource Environment and Tourism, Capital Normal University, No.105 West 3rd Ring Road North, Beijing 100048, ChinaUrban 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.https://www.mdpi.com/2072-4292/13/20/4086heatwave eventsresidential sensitivity to HWEssocial media Big Dataspatial match of sensitivity and HWEsChina |
| spellingShingle | Guoqing Zhi Bin Meng Juan Wang Siyu Chen Bin Tian Huimin Ji Tong Yang Bingqing Wang Jian Liu Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China Remote Sensing heatwave events residential sensitivity to HWEs social media Big Data spatial match of sensitivity and HWEs China |
| title | Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China |
| title_full | Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China |
| title_fullStr | Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China |
| title_full_unstemmed | Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China |
| title_short | Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China |
| title_sort | spatial analysis of urban residential sensitivity to heatwave events case studies in five megacities in china |
| topic | heatwave events residential sensitivity to HWEs social media Big Data spatial match of sensitivity and HWEs China |
| url | https://www.mdpi.com/2072-4292/13/20/4086 |
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