Simulating Land Use Changes Driven by Various Factors Using Cellular Automata

Cellular Automata (CA) is a computational model based on discrete space and time, widely used for simulating dynamic changes in complex systems. In land use studies, this approach effectively captures the impact of various driving factors—such as population growth, economic development, and policy adjustments—on land cover changes.

The core of the model lies in defining state transition rules for each cell. Each cell represents a land parcel, with potential states including farmland, urban area, forest, etc. The influence of driving forces is implemented by calculating interactions with neighboring cells, external factors, and probabilistic transition rules. For instance, urbanization may gradually convert farmland cells into urban land use.

MATLAB serves as an ideal platform for implementing such models due to its robust matrix operations and visualization capabilities. Users can define rule matrices, initialize cell states, and iteratively update states through functions like conv2 for neighborhood analysis and rand for probabilistic transitions. The simulation typically involves looping through time steps using for or while constructs, with spatial results visualized via imagesc or pcolor. This method not only supports academic research but also provides decision-making insights for urban planning and ecological conservation applications.