Integration of Cellular Automata and Gis for Simulating Land Use Changes

Cellular automata have been used as a simulation technique in the study of an impressively wide range of urban phenomena, including regional growth, urban sprawl, gentrification, residential growth, population dynamics, economic activity and employment, historical urbanization, land use evolution, and polycentricity to name but a few. A spatial model consists of a collection of processes performed on spatial data that will produce information, usually in the form of a map. These models can often be represented as process flow diagrams, like showing how the output from one process can be the input to a subsequent process. C A are ideal for simulating static entities in spatial models and processes that operate by diffusion. They are ideal for encoding spatial structures into simulation models. The application of CA in land use/land cover/ urban modeling can give insights into a wide variety of urban phenomena. Urban CA models have better performance in simulating urban growth than conventional urban models because they are much simpler than complex mathematical equations, but produce results that are more meaningful and useful. Temporal and spatial complexities of urban systems can be well modeled by properly defining transition rules in CA models. CA simulation provides important information for understanding urban theories, such as the evolution of forms and structures. GIS is a technology that is used to view and analyze data from a geographic perspective. The spatial representation of an object and its related non-spatial attribute are merged into a unified data file. In practice the area under study is covered by a fine mesh or matrix of grid cells and particular ground surface attribute value of interest occurring at the center of each cell point is recorded as the value for that cell. It should be noted that while some raster models support the assignment of values to multiple attribute per discrete cell, others strictly to a single attribute per cell structure