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

Pengyu Liu, Chao Wu, Miaomiao Chen, Xinyue Ye, Yunfei Peng and Sheng Li
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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
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

Sustainability, 2020, vol. 12, issue 6, 1-15

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.

Keywords: landscape pattern; urbanization; scale; spatial heterogeneity; Shenzhen (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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