IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i24p9578-d465845.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/24/9578/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/24/9578/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. 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.
    4. 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.
    5. 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.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    2. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    3. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    4. Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2024. "Effects of Urban Land-Use Planning on Housing Prices in Chiang Mai, Thailand," Land, MDPI, vol. 13(8), pages 1-13, July.
    5. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    6. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    7. Li Gao & Mingjing Huang & Wuping Zhang & Lei Qiao & Guofang Wang & Xumeng Zhang, 2021. "Comparative Study on Spatial Digital Mapping Methods of Soil Nutrients Based on Different Geospatial Technologies," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    8. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    9. Moore, David & Webb, Amanda L., 2022. "Evaluating energy burden at the urban scale: A spatial regression approach in Cincinnati, Ohio," Energy Policy, Elsevier, vol. 160(C).
    10. Jack C. Yue & Ming-Huei Tu & Yin-Yee Leong, 2024. "A spatial analysis of the health and longevity of Taiwanese people," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 384-399, April.
    11. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    12. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    13. Yongxin Liu & Yiting Wang & Yiwen Lin & Xiaoqing Ma & Shifa Guo & Qianru Ouyang & Caige Sun, 2023. "Habitat Quality Assessment and Driving Factors Analysis of Guangdong Province, China," Sustainability, MDPI, vol. 15(15), pages 1-23, July.
    14. Tao Wang & Kai Zhang & Keliang Liu & Keke Ding & Wenwen Qin, 2023. "Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    15. Rémy Le Boennec & Julie Bulteau & Thierry Feuillet, 2022. "The role of commuter rail accessibility in the formation of residential land values: exploring spatial heterogeneity in peri-urban and remote areas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(1), pages 163-186, August.
    16. Lu, Haiyan & Zhao, Pengjun & Hu, Haoyu & Zeng, Liangen & Wu, Kai Sheng & Lv, Di, 2022. "Transport infrastructure and urban-rural income disparity: A municipal-level analysis in China," Journal of Transport Geography, Elsevier, vol. 99(C).
    17. Junfeng Wang & Shaoyao Zhang & Wei Deng & Qianli Zhou, 2024. "Metropolitan Expansion and Migrant Population: Correlation Patterns and Influencing Factors in Chengdu, China," Land, MDPI, vol. 13(1), pages 1-20, January.
    18. Wang, Jiaoe & Xiao, Fan & Dobruszkes, Frédéric & Wang, Wei, 2023. "Seasonality of flights in China: Spatial heterogeneity and its determinants," Journal of Air Transport Management, Elsevier, vol. 108(C).
    19. Xin Lao & Hengyu Gu, 2020. "Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1860-1876, December.
    20. Huxiao Zhu & Xiangjun Ou & Zhen Yang & Yiwen Yang & Hongxin Ren & Le Tang, 2022. "Spatiotemporal Dynamics and Driving Forces of Land Urbanization in the Yangtze River Delta Urban Agglomeration," Land, MDPI, vol. 11(8), pages 1-21, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9578-:d:465845. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.