Assessing Urban Land Use Change through Geographical Weighted Regression: Implications for Sustainable Environmental Planning

Urbanization significantly impacts land use patterns and environmental sustainability. This study uses Geographically Weighted Regression (GWR) to assess urban land use change in a metropolitan area, employing spatially explicit data and GWR modeling techniques to identify local factors influencing land use dynamics. The GWR 4.0 software was utilized for model evaluation and analysis processing, while ArcGIS 10.8 was used for spatial analysis and mapping. Goodness-of-fit criteria were applied to assess the GWR model's performance. The analysis showed improvements in model fit: the Akaike Information Criterion (AICc) decreased from 1509.10 to 1297.31, the Bayesian Information Criterion (BIC) dropped from 1525.77 to 721.24, and the R-squared increased from 0.01 to 0.23, indicating a better fit for localized measurements. The findings reveal that proximity to Mass Rapid Transit (MRT) stations is a significant factor influencing land use changes in the study area. This highlights the need for localized planning strategies that address urban challenges and support sustainable development.