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How Urban Street Spatial Composition Affects Land Surface Temperature in Areas with Different Population Densities: A Case Study of Zhengzhou, China

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  • Mengze Fu

    (School of Architecture, Zhengzhou University, Zhengzhou 450001, China
    Henan International Joint Laboratory of Eco-Community & Innovative Technology, Zhengzhou 450001, China)

  • Kangjia Ban

    (School of Architecture, Zhengzhou University, Zhengzhou 450001, China)

  • Li Jin

    (School of Architecture, Zhengzhou University, Zhengzhou 450001, China)

  • Di Wu

    (School of Architecture, Zhengzhou University, Zhengzhou 450001, China
    Henan International Joint Laboratory of Eco-Community & Innovative Technology, Zhengzhou 450001, China)

Abstract

The arrangement and design of urban streets have a profound impact on the thermal conditions within cities, including the mitigation of excessive street land surface temperatures (LSTs). However, previous research has mainly addressed the linear relationships between the physical spatial elements of streets and LST. There has been limited exploration of potential nonlinear relationships and the influence of population density variations. This study explores multi-dimensional street composition indicators obtained from street-view imagery and applies generalized additive models (GAMs) and geographically weighted regression (GWR) to evaluate the indicators’ impact on LST in areas with various population densities. The results indicate the following: (1) The six indicators—green space index (GSI), tree canopy index (TCI), sky open index (SOI), spatial enclosure index (SEI), road width index (RWI), and street walking index (SWI)—all have significant nonlinear effects on summer daytime LST. (2) Among all categories, the GSI negatively affects LST. Moreover, the TCI’s impact on LST shifts from negative to positive as its value increases. The SOI and SWI positively affect LST in all categories. The SEI’s effect on LST changes from negative to positive in the total and high-population (HP) categories, and it remains negative in the low-population (LP) category. The RWI positively affects LST in the total category, shifts from negative to positive in the LP category, and remains negative in the HP category. (3) The influence ranking is GSI > SEI > SWI > SOI > TCI > RWI, with GSI being the most significant factor. These findings provide key insights for mitigating street LSTs through design interventions, contributing to sustainable urban development.

Suggested Citation

  • Mengze Fu & Kangjia Ban & Li Jin & Di Wu, 2024. "How Urban Street Spatial Composition Affects Land Surface Temperature in Areas with Different Population Densities: A Case Study of Zhengzhou, China," Sustainability, MDPI, vol. 16(22), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9938-:d:1520990
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    References listed on IDEAS

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    1. Ruirui Dong & Michael Wurm & Hannes Taubenböck, 2022. "Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns," IJERPH, MDPI, vol. 19(19), pages 1-20, October.
    2. Yunfang Jiang & Xuemei Han & Tiemao Shi & Danran Song, 2019. "Microclimatic Impact Analysis of Multi-Dimensional Indicators of Streetscape Fabric in the Medium Spatial Zone," IJERPH, MDPI, vol. 16(6), pages 1-31, March.
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