Spatio Temporal Analysis for Urban Growth Modelling using Cellular Automata and Genetic Algorithm

Several cities are currently undergoing redevelopment for economic benefits by implementing new transportation systems, infrastructure improvements, and other development projects. The rapid urbanization of Bangalore poses significant challenges for urban planning and development. In this research paper, we explore the use of geo-spatial analysis to monitor and predict urban growth over time. We use historical data to train a cellular automata model, and the model’s parameters are obtained via the use of a genetic algorithm. We then simulate and predict future growth patterns for the city, up to 2025, taking 2017 as the base year. By adjusting the parameters of the model, we explore different growth scenarios and their potential impacts on the city. The paper emphasizes the importance of sustainable urban development, including good public infrastructure and considerations such as flood risk and proximity to public transportation. Urban growth modelling can help identify areas of potential growth and guide policymakers in designing sustainable urban development strategies for Bangalore.