A Spatial Autocorrelation Analysis for Land Use Change in the Guangdong-Hong Kong-Macao Greater Bay Area

Xiao Tang (), Clyde Zhengdao Li (), Lin Jiang, Xulu Lai and Limei Zhang
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Xiao Tang: The University of Manchester
Clyde Zhengdao Li: Shenzhen University
Lin Jiang: Shenzhen University
Xulu Lai: Shenzhen University
Limei Zhang: Shenzhen University

A chapter in Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, 2022, pp 847-858 from Springer

Abstract: Abstract The rise of the megalopolis has become a crucial force in urbanization throughout the world. Its land-use change has become a central factor that will affect the benign development of the megalopolis. This paper conducts a spatial autocorrelation analysis to study the spatial dependence and heterogeneity of the land use/land cover change in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 1990 to 2018. It also proposes the land spatial development strategy combined with the results of empirical analysis. The findings include: 1) From 1990 to 2018, Moran's I values for agricultural land, forest land, and construction land were all range from 0.078 to 0.32. The spatial autocorrelation of forest land has been decreased since 1990, but the spatial autocorrelation of construction land and agricultural land showed a slight rebound trend in 2010, although it has been gradually decreasing since 1990. 2) Land use in the GBA region shows a High-Low agglomeration phenomenon, of which Shenzhen has been in a High-Low correlation from 1990 to 2010, while the High-Low correlation is no longer shown after 2010. 3) The areas presenting High-Low correlations between construction land and agricultural land or forest land are mainly located in regions with rapid economic development, such as Shenzhen. Low-Low areas are mainly concentrated in economically developed regions such as Macau and Hong Kong.

Keywords: Mega city; Guangdong-Hong Kong-Macao greater bay area; Land use; Spatial autocorrelation analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-981-19-5256-2_66

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