Exploring the Driving Factors of Land Use Change and Spatial Distribution in Coastal Cities: A Case Study of Xiamen City

Tianhai Zhang (), Greg Foliente, Jiangtao Xiao and Lina Tang ()
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Tianhai Zhang: Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Sichuan Normal University, Ministry of Education, Chengdu 610066, China
Greg Foliente: Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia
Jiangtao Xiao: Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Sichuan Normal University, Ministry of Education, Chengdu 610066, China
Lina Tang: Key Lab for Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

Sustainability, 2025, vol. 17, issue 3, 1-19

Abstract: This study focuses on the coastal city of Xiamen, examining the factors and driving mechanisms influencing land use changes and spatial patterns. Spatial logistic regression and Statistical Package for the Social Sciences (SPSS) software were employed using grid data with a resolution of 100m to analyze the spatial relationships between six driving factors (such as elevation and slope) and five land use types within the study area. Regression models were established for each factor, and all Relative Operating Characteristic (ROC) tests were passed. Based on the results of the logistic regression analysis, land use changes and spatial distribution were simulated using the updated Conversion of Land Use and its Effects (CLUE) model so as to validate the driving mechanisms. The findings indicate that the six driving factors effectively explain the spatial patterns of land use in the study area. The distance to the coastline is the primary influencing factor in the evolution of spatial patterns, particularly impacting built-up land and farmland, while for forest land, slope is the main factor affecting the spatial distribution. The simulation and accuracy analysis revealed an overall simulation accuracy ranging from 73% to 90.1%, demonstrating that the selected driving factors have effective explanatory power for the spatial distribution of land use. Thus, this study’s results provide valuable insights into the complexity of land use changes and serve as a reference for relevant departments in land use management and planning.

Keywords: land use; driving forces; Dyna-CLUE; logistic regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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