Modelling spatial patterns of urban growth in Pune metropolitan region, India

Explaining urban growth patterns is a fundamental need to understand the recent rapid urbanization globally. This study identifies geographic features explaining the spatial patterns of urban land expansion (ULE) in the rapidly urbanizing Pune metropolitan region (India). ULE maps were derived from Landsat Thematic Mapper and Operational Land Imager images using support vector machine (SVM) classification. Relation between geographic features and spatial patterns of ULE was analyzed using statistical modelling including ordinary least squares (OLS) regression, spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). SEM specification best modeled ULE patterns. High density of existing urban areas is identified to negatively affect ULE, suggesting dominant dispersed urban growth. In addition, proximity to special economic zones and transportation infrastructure explains multicentric growth in the region. GWR model was identified inappropriate due to the presence of high local collinearity. Models accounting for spatial dependencies are recommended while studying ULE patterns.