The project titled “City Growth Prediction Using Economic and Infrastructure Indicators” focuses on analyzing and forecasting urban development through a data-driven approach. With the rapid expansion of urban areas and increasing population density, accurate prediction of city growth has become essential for effective planning and resource management. Traditional forecasting methods often depend on limited datasets and manual analysis, which may lead to less reliable outcomes. To address these limitations, this study integrates diverse data sources, including economic indicators such as GDP, employment rates, and income levels, along with infrastructure-related factors like transportation networks, utilities, healthcare, and educational facilities. In addition, spatial and geographic data obtained from platforms such as OpenStreetMap are analyzed using Geographic Information System (GIS) techniques to identify patterns and relationships in urban expansion. The collected data undergoes preprocessing and transformation to ensure quality and consistency before analysis. A machine learning approach, specifically the Random Forest algorithm, is employed to model complex relationships among variables and generate accurate predictions of city growth. The results are presented through an interactive web-based application developed using Django, enabling users to visualize and interpret insights effectively. This system supports informed decision-making for urban planners and policymakers, contributing to sustainable development and smarter city management.
KRISHNA et al. (2026) studied this question.