Spatial Analysis of Land Cover Degradation Processes Associated with Aridity in Northwestern Mexico Using Geographically Weighted Regression

Aridity is a key climatic factor influencing ecosystem dynamics and land degradation in arid and semi-arid regions. This study analyzes the spatial relationship between aridity and land cover degradation in northwestern Mexico during 2005–2020 using a Geographically Weighted Regression (GWR) model, complemented by spatial autocorrelation techniques including Moran’s I and Local Indicators of Spatial Association (LISA). Aridity was derived from climatic data, and land cover transitions were used as proxies for degradation. The results indicate that the study area is predominantly characterized by arid and semi-arid conditions, where degradation-related transitions are strongly concentrated. In particular, transitions from shrubland to grassland (59.53%) and from shrubland to bare soil (93.60%) occur primarily under arid conditions, highlighting the high vulnerability of these ecosystems to water deficit. The GWR model explains approximately 49.5% of the spatial variability in degradation. However, residual analysis shows strong spatial autocorrelation (Moran’s I = 0.72, p < 0.001), indicating spatially structured patterns not fully captured by the model. These findings demonstrate that, although aridity is a key driver, additional factors influence degradation patterns.