The Changing Face of Urbanization, Population Expansion, and its Driving Forces: a Case Study of an Indian City

In this study, we utilized Landsat time-series data from 2000 to 2020 to examine urban sprawl and Urban Density (UD) at five-year time-steps. To perform supervised classification and extract urban areas, we employed the Random Forest (RF) machine learning algorithm. We compared the expansion of urban density with the corresponding growth in Population Density (PD). PD was categorized into six classes - very low, low, moderate, high, very high, and crowded - based on a standard classification criterion. The percentage area lying in each class of PD was calculated for each year, allowing us to correlate the percentage growth in urban areas with population growth in different regions of the city. The findings clearly indicate a direct relationship between population increase and expansion of built-up areas. Notably, the largest population growth and urban land extension occurred in proximity to the city's major transportation networks, highways, marketplaces, and educational institutions.