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Open AccessArticle

Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence

by Xuan Guo 1,2, Ganlin Huang 1,2,*, Peng Jia 3,4 and Jianguo Wu 1,5
1
Center 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, China
2
School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3
GeoHealth Initiative, Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 Enschede, The Netherlands
4
International Initiative on Spatial Lifecourse Epidemiology (ISLE), 7500 Enschede, The Netherlands
5
School of Life Sciences and School of Sustainability, Arizona State University, Tempe, AZ 85287, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2358; https://doi.org/10.3390/rs11202358
Received: 30 August 2019 / Revised: 17 September 2019 / Accepted: 5 October 2019 / Published: 11 October 2019
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. View Full-Text
Keywords: urban heat; vulnerability; spatial pattern; Beijing; principal component analysis urban heat; vulnerability; spatial pattern; Beijing; principal component analysis
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Guo, X.; Huang, G.; Jia, P.; Wu, J. Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence. Remote Sens. 2019, 11, 2358.

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