Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence
High temperatures in urban areas cause a significant negative impact on the residents’ health. In a megacity such as Beijing, where both the land cover and social composition of residents are highly spatially heterogeneous, understanding heat vulnerability at a relatively fine scale is a p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/20/2358 |
| _version_ | 1798027080120664064 |
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| author | Xuan Guo Ganlin Huang Peng Jia Jianguo Wu |
| author_facet | Xuan Guo Ganlin Huang Peng Jia Jianguo Wu |
| author_sort | Xuan Guo |
| collection | DOAJ |
| description | High temperatures in urban areas cause a significant negative impact on the residents’ health. In a megacity such as Beijing, where both the land cover and social composition of residents are highly spatially heterogeneous, understanding heat vulnerability at a relatively fine scale is a prerequisite for place-based heat intervention actions. Both principal component analysis (PCA) and equal-weighted index (EWI) are commonly used in heat vulnerability studies. However, the extent to which the choice of these approaches may impact the results remains unclear. Our study aimed to fill this gap by estimating heat vulnerability at the jiedao scale (the smallest census unit) in Beijing based on socioeconomic characteristics, heat exposure, and the use of air conditioners. Our results show that the choice of methods had a considerable impact on the spatial patterns of estimated heat vulnerability. PCA resulted in a ring-like pattern (high in the central and low in the suburb), whereas EWI revealed a north−south discrepancy (low in the north and high in the south). Such a difference is caused by the weighting scheme used in the PCA. Our findings indicate that heat vulnerability pattern revealed by a single measure needs to be interpreted with caution because different measures may produce disparate results. |
| first_indexed | 2024-04-11T18:45:37Z |
| format | Article |
| id | doaj.art-c6fa06f423954b0c8ff1733254e363a8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:45:37Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c6fa06f423954b0c8ff1733254e363a82022-12-22T04:08:50ZengMDPI AGRemote Sensing2072-42922019-10-011120235810.3390/rs11202358rs11202358Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and DivergenceXuan Guo0Ganlin Huang1Peng Jia2Jianguo Wu3Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, No 19 Xinjiekouwai Road, Beijing 100875, ChinaCenter for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, No 19 Xinjiekouwai Road, Beijing 100875, ChinaGeoHealth Initiative, Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 Enschede, The NetherlandsCenter for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, No 19 Xinjiekouwai Road, Beijing 100875, ChinaHigh temperatures in urban areas cause a significant negative impact on the residents’ health. In a megacity such as Beijing, where both the land cover and social composition of residents are highly spatially heterogeneous, understanding heat vulnerability at a relatively fine scale is a prerequisite for place-based heat intervention actions. Both principal component analysis (PCA) and equal-weighted index (EWI) are commonly used in heat vulnerability studies. However, the extent to which the choice of these approaches may impact the results remains unclear. Our study aimed to fill this gap by estimating heat vulnerability at the jiedao scale (the smallest census unit) in Beijing based on socioeconomic characteristics, heat exposure, and the use of air conditioners. Our results show that the choice of methods had a considerable impact on the spatial patterns of estimated heat vulnerability. PCA resulted in a ring-like pattern (high in the central and low in the suburb), whereas EWI revealed a north−south discrepancy (low in the north and high in the south). Such a difference is caused by the weighting scheme used in the PCA. Our findings indicate that heat vulnerability pattern revealed by a single measure needs to be interpreted with caution because different measures may produce disparate results.https://www.mdpi.com/2072-4292/11/20/2358urban heatvulnerabilityspatial patternbeijingprincipal component analysis |
| spellingShingle | Xuan Guo Ganlin Huang Peng Jia Jianguo Wu Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence Remote Sensing urban heat vulnerability spatial pattern beijing principal component analysis |
| title | Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence |
| title_full | Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence |
| title_fullStr | Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence |
| title_full_unstemmed | Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence |
| title_short | Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence |
| title_sort | estimating fine scale heat vulnerability in beijing through two approaches spatial patterns similarities and divergence |
| topic | urban heat vulnerability spatial pattern beijing principal component analysis |
| url | https://www.mdpi.com/2072-4292/11/20/2358 |
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