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The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones

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  • Sihang Gao

    (School of Urban Design, Wuhan University, Wuhan 430072, China
    Collaborative Innovation Centre of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China)

  • Qingming Zhan

    (School of Urban Design, Wuhan University, Wuhan 430072, China
    Collaborative Innovation Centre of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China)

  • Chen Yang

    (College of Urban and Environmental Sciences, Peking University, Beijing 100871, China)

  • Huimin Liu

    (Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China)

Abstract

Local warming induced by rapid urbanization has been threatening residents’ health, raising significant concerns among urban planners. Local climate zone (LCZ), a widely accepted approach to reclassify the urban area, which is helpful to propose planning strategies for mitigating local warming, has been well documented in recent years. Based on the LCZ framework, many scholars have carried out diversified extensions in urban zoning research in recent years, in which urban functional zone (UFZ) is a typical perspective because it directly takes into account the impacts of human activities. UFZs, widely used in urban planning and management, were chosen as the basic unit of this study to explore the spatial heterogeneity in the relationship between landscape composition, urban morphology, urban functions, and land surface temperature (LST). Global regression including ordinary least square regression (OLS) and random forest regression (RF) were used to model the landscape-LST correlations to screen indicators to participate in following spatial regression. The spatial regression including semi-parametric geographically weighted regression (SGWR) and multiscale geographically weighted regression (MGWR) were applied to investigate the spatial heterogeneity in landscape-LST among different types of UFZ and within each UFZ. Urban two-dimensional (2D) morphology indicators including building density (BD); three-dimensional (3D) morphology indicators including building height (BH), building volume density (BVD), and sky view factor (SVF); and other indicators including albedo and normalized difference vegetation index (NDVI) and impervious surface fraction (ISF) were used as potential landscape drivers for LST. The results show significant spatial heterogeneity in the Landscape-LST relationship across UFZs, but the spatial heterogeneity is not obvious within specific UFZs. The significant impact of urban morphology on LST was observed in six types of UFZs representing urban built up areas including Residential (R), Urban village (UV), Administration and Public Services (APS), Commercial and Business Facilities (CBF), Industrial and Manufacturing (IM), and Logistics and Warehouse (LW). Specifically, a significant correlation between urban 3D morphology indicators and LST in CBF was discovered. Based on the results, we propose different planning strategies to settle the local warming problems for each UFZ. In general, this research reveals UFZs to be an appropriate operational scale for analyzing LST on an urban scale.

Suggested Citation

  • Sihang Gao & Qingming Zhan & Chen Yang & Huimin Liu, 2020. "The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones," IJERPH, MDPI, vol. 17(24), pages 1-20, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9578-:d:465845
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    References listed on IDEAS

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    1. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
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    1. Lina Tang & Alimujiang Kasimu & Haitao Ma & Mamattursun Eziz, 2023. "Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains’ Northern Slopes, Xinjiang, China," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    2. Jie Yin & Qingming Zhan & Muhammad Tayyab & Aqeela Zahra, 2021. "The Ventilation Efficiency of Urban Built Intensity and Ventilation Path Identification: A Case Study of Wuhan," IJERPH, MDPI, vol. 18(21), pages 1-16, November.
    3. Suiping Zeng & Jiahao Zhang & Jian Tian, 2023. "Analysis and Optimization of Thermal Environment in Old Urban Areas from the Perspective of “Function–Form” Differentiation," Sustainability, MDPI, vol. 15(7), pages 1-23, April.
    4. Jinlong Yan & Chaohui Yin & Zihao An & Bo Mu & Qian Wen & Yingchao Li & Yali Zhang & Weiqiang Chen & Ling Wang & Yang Song, 2023. "The Influence of Urban Form on Land Surface Temperature: A Comprehensive Investigation from 2D Urban Land Use and 3D Buildings," Land, MDPI, vol. 12(9), pages 1-18, September.
    5. Ömer Ünsal & Aynaz Lotfata & Sedat Avcı, 2023. "Exploring the Relationships between Land Surface Temperature and Its Influencing Determinants Using Local Spatial Modeling," Sustainability, MDPI, vol. 15(15), pages 1-26, July.
    6. Zherong Wu & Xinyang Zhang & Peifeng Ma & Mei-Po Kwan & Yang Liu, 2023. "How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data," Sustainability, MDPI, vol. 15(21), pages 1-26, November.

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