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A Spatiotemporal Analysis of the Effects of Urbanization’s Socio-Economic Factors on Landscape Patterns Considering Operational Scales

Author

Listed:
  • Pengyu Liu

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Chao Wu

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Miaomiao Chen

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Xinyue Ye

    (Urban Informatics & Spatial Computing Lab, Department of Informatics, New Jersey Institute of Technology, Newark, NJ 07102, USA)

  • Yunfei Peng

    (Shenzhen Urban Planning & Land Resource Research Center, Shenzhen 518040, China)

  • Sheng Li

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518034, China
    Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China)

Abstract

Landscape patterns are significantly affected during the urbanization process. Identifying the spatiotemporal impacts of urbanization’s socio-economic factors on landscape patterns is very important and can provide scientific evidence to support urban ecological management and guide managers to establish appropriate sustainability policies. This article applies multiscale geographically weighted regression (MGWR) to reveal the relationships between landscape patterns and the socio-economic factors of urbanization in Shenzhen, China, from 2000 to 2015, in five-year intervals. MGWR is a powerful extension of geographically weighted regression (GWR) that can not only reveal spatial heterogeneity patterns but also measure the operational scale of covariates. The empirical results indicate that MGWR is superior to GWR. Furthermore, the changes in operational scale represented by the spatial bandwidth of MGWR in different years reflect temporal changes in the spatial relationships of given factors, which is significant information for urban studies. These multiscale relationships between landscape patterns and the socio-economic factors of urbanization, revealed via MGWR, are useful for strategic planning around urban dynamic development and land resource and ecological landscape management. The results can provide additional insight into landscape and urbanization studies from a multiscale perspective, which is important for local, regional, and global urban planning.

Suggested Citation

  • Pengyu Liu & Chao Wu & Miaomiao Chen & Xinyue Ye & Yunfei Peng & Sheng Li, 2020. "A Spatiotemporal Analysis of the Effects of Urbanization’s Socio-Economic Factors on Landscape Patterns Considering Operational Scales," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2543-:d:336298
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    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.
    2. Daisuke Murakami & Binbin Lu & Paul Harris & Chris Brunsdon & Martin Charlton & Tomoki Nakaya & Daniel A. Griffith, 2019. "The Importance of Scale in Spatially Varying Coefficient Modeling," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(1), pages 50-70, January.
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    2. Rares Halbac-Cotoara-Zamfir & Gianluca Egidi & Enrico Maria Mosconi & Stefano Poponi & Ahmed Alhuseen & Luca Salvati, 2020. "Uncovering Demographic Trends and Recent Urban Expansion in Metropolitan Regions: A Paradigmatic Case Study," Sustainability, MDPI, vol. 12(9), pages 1-15, May.
    3. Yu Li & Haipeng Ye & Xu Sun & Ji Zheng & Dan Meng, 2021. "Coupling Analysis of the Thermal Landscape and Environmental Carrying Capacity of Urban Expansion in Beijing (China) over the Past 35 Years," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    4. Xueling Zhang & Alimujiang Kasimu & Hongwu Liang & Bohao Wei & Yimuranzi Aizizi, 2022. "Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China," IJERPH, MDPI, vol. 19(20), pages 1-21, October.

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