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Spatially varying relationships between surface urban heat islands and driving factors across cities in China

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  • Yaping Huang
  • Man Yuan
  • Youpeng Lu

Abstract

Global regression models, such as Ordinary Least Squares, are generally used to explore driving factors of surface urban heat island (SUHI) effects across large cities on a national level, but issues of spatial non-stationarity or local variations have rarely been taken into account. Our study quantifies SUHI effects for 274 cities in China with MODIS LST products and explores spatially varying relationships between SUHI intensity (SUHII) and their driving factors using geographically weighted regression (GWR). The results show that GWR models have stronger explanatory power and lower spatial autocorrelations of residuals compared with ordinary least square models; the application of GWR models finds that the relationships between SUHII and the driving factors vary across China. Spatially varying coefficients from GWR models could contribute to the development of local-specific urban planning or policies in different regions. The findings from our investigation suggest that GWR has the potential to serve as a useful tool for environmental investigations on a national scale.

Suggested Citation

  • Yaping Huang & Man Yuan & Youpeng Lu, 2019. "Spatially varying relationships between surface urban heat islands and driving factors across cities in China," Environment and Planning B, , vol. 46(2), pages 377-394, February.
    • Handle: RePEc:sae:envirb:v:46:y:2019:i:2:p:377-394
    DOI: 10.1177/2399808317716935
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    References listed on IDEAS

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    1. Yaoping Cui & Xinliang Xu & Jinwei Dong & Yaochen Qin, 2016. "Influence of Urbanization Factors on Surface Urban Heat Island Intensity: A Comparison of Countries at Different Developmental Phases," Sustainability, MDPI, vol. 8(8), pages 1-14, July.
    2. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
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