Visualization of urban growth modelling land use change using cellular automata

Abstract The phenomenon of urbanization, which is driven by a significant growth in population, leads to the expansion of cities at the cost of reducing Land Cover areas. The necessity of monitoring and predicting urban growth trends lies in the establishment and maintenance of sustainability. The objective of this work is to utilize the Spatial Lag Model to analyze the urban growth pattern of the Asansol-Durgapur region. The aim is to investigate the interconnections between the factors contributing to urban growth. Additionally, The Cellular Automata-based SLEUTH model will be employed to forecast the likelihood of future growth in the region. The accurate modeling and prediction of urban areas necessitate the consideration of multiple variables and the utilization of big datasets, hence posing a significant challenge to researchers. The research reveals that the existence of multimodal infrastructure, such as well-connected roads, highways, and railroads, that offer convenient accessibility, along with neighbourhood development and, the presence of vacant lands in the vicinity, has a crucial role in driving population migration toward urban regions, hence contributing to urban expansion. These results aid in developing policy measures and sustainable actions to mitigate urbanization’s anthropogenic effects. Integrating machine learning with the models and continuous data updates are recommended for better urban growth predictions.