Local spatial modeling of paddy soil landscape patterns in response to urbanization across the urban agglomeration around Hangzhou Bay, China

This study employed geographically weighted regression (GWR), a local spatial modeling approach, to characterize the local varying response of paddy soils to urbanization at the landscape scale in the urban agglomeration around Hangzhou Bay, China. Spatial non-stationary and scale-dependent relationships between three geographical urbanization indices (the common urbanization intensity index, distance to road corridors and distance to urban centers) and four spatial metrics depicting paddy soil landscape patterns (total area, edge density, aggregation index, and perimeter area ratio distribution) were captured at two block scales (4 km × 4 km and 7 km × 7 km) because the predictive ability of GWR and the nature and strength of the identified relationships all varied significantly in space and scale. Moreover, GWR models have achieved better performance than their corresponding global ordinary least squares (OLS) models given their greater ability to (1) describe data (higher adjusted R2 and lower Akaike Information Criterion values) and (2) address spatial autocorrelation in model residuals. This study highlighted scale effects when applying GWR. Both bandwidth and block size can impact the predictive ability of GWR as well as estimated parameters for GWR analysis. This study demonstrates a novel joint application of GWR and associated metrics in applied geography.