Estimating fine-scale heat vulnerability in Beijing through two approaches: Spatial patterns, similarities, and divergence

Xuan Guo, Ganlin Huang, Peng Jia, Jianguo Wu

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number2358
JournalRemote Sensing
Volume11
Issue number20
DOIs
StatePublished - Oct 1 2019

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vulnerability
divergence
principal component analysis
megacity
census
land cover
urban area
air
index

Keywords

  • Beijing
  • Principal component analysis
  • Spatial pattern
  • Urban heat
  • Vulnerability

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

Estimating fine-scale heat vulnerability in Beijing through two approaches : Spatial patterns, similarities, and divergence. / Guo, Xuan; Huang, Ganlin; Jia, Peng; Wu, Jianguo.

In: Remote Sensing, Vol. 11, No. 20, 2358, 01.10.2019.

Research output: Contribution to journalArticle

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