Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

Lu Niu,Yazhen Jiang,Yingzi Liang,Jing Wei,Meng Liu,Zhong Peng,Ronglin Tang,Zhengfeng Zhang

doi:10.3390/rs13214428

Abstract

The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.

Highlights

Urbanization is a significant phenomenon of human activity that alters land use and cover [1,2,3]
Results indicate that the GWR bandwidth can be considered an intermediate value of the multiscale geographically weighted regression (MGWR) bandwidth, i.e., it ignores the global robustness of some variables and fails to capture the spatial heterogeneity of some variables
It is worth noting that previous studies usually mention that MGWR models can better handle the problem of multicollinearity

Summary

Introduction

Urbanization is a significant phenomenon of human activity that alters land use and cover [1,2,3]. One of the most widespread human-induced environmental influences of urbanization is the emergence of urban heat islands, described as higher temperature urban areas compared to rural references [4,5]. Urban heat islands (UHIs) have been widely observed worldwide in recent decades. The impacts of urban heat islands are a significant concern in urban environmental research. Urban heat islands can be classified as canopy (CUHI) and surface (SUHI) UHIs [16,17,18]

Methods

Results

Discussion

Conclusion