Supervised Model for Peri-Urban Area Demarcation in Hyderabad, India

The peripheries of metropolitans are undergoing rapid socio-spatial changes due to economic transformations, especially in the global south, leading to the emergence of peri-urban areas. However, there are limited studies analyzing peri-urban expansion in the past decade in countries like India. This could be attributed to the absence of socio-economic datasets that are generally derived from census-related exercises. In this context, this study introduces a machine-learning (ML)-based model to demarcate peri-urban areas around Hyderabad, India. Our model integrates multiple spatial parameters and domain knowledge-driven thresholds using a radial basis function support vector machine (RBF-SVM). Notably, the findings reveal a remarkable 107.96% increase in peri-urban regions around Hyderabad between 2013 and 2020. Furthermore, this study performs a robustness analysis, assessing the model's sensitivity to threshold adjustments. It is important to underscore that the proposed model holds relevance for developing countries, where the timely availability and accuracy of socio-economic data pose notable challenges for peri-urban classification.