Literature DB >> 31654141

Spatial characteristics and risk factor identification for land use spatial conflicts in a rapid urbanization region in China.

Zhulu Lin1, Siew Hoon Lim2.   

Abstract

Land use conflict is a complex problem driven by a myriad of risk factors as a result of rapid socioeconomic development and urbanization. Analyzing the spatial characteristics of land use conflict and identifying its risk factors using statistical models will help us to better understand the causes and effects of the land use conflicts for sustainable management of the limited land resources under the pressure of rapid urbanization. In this study, regression models including multiple linear regression (MLR), spatial autoregressive (SAR), and geographically weighted regression (GWR) models were employed to identify risk factors for the land use spatial conflicts in the Urban Agglomeration around Hangzhou Bay (UAHB) of China in the past 25 years. Our results showed that the overall extent and the higher-level land use spatial conflicts were actually on the decline, and their spatial autocorrelation has been weakening in the UAHB. The key risk factors that mainly caused the land use spatial conflicts in the UHAB appeared to be different at the global and local scales. This knowledge should help urban managers and policymakers to be better informed when developing pertinent land use policies at the regional and local levels. This study also underlined the importance of considering spatial autocorrelation and scale effects when identifying the risk factors for land use spatial conflicts. The lessons learned from this particular context can be extended to other areas under rapid urbanization to assess and better manage their land resources for sustainable use. Graphical abstract.

Keywords:  Geographically weighted regression; Land use conflicts; Risk factor identification; Scale effect; Spatial autoregressive model; Urbanization

Year:  2019        PMID: 31654141     DOI: 10.1007/s10661-019-7809-1

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


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