Study Of Cellular Automata Models For Urban Growth

Differential equations, partial differential equations and, in some instances, empirical equations have been the underlying mathematical tools behind spatial simulation models. Approaches based on cellular automata models are proposed herein to replace the conventional tools. Issues such as the definition of transition rules, computer implementation with raster geographical information systems and model verification are discussed
Cellular Automata (CA) models were originally conceived by Ulam and Von Neumann in the 1940s to provide a formal framework for investigating the behavior of complex, extended systems. CA are dynamic, discrete space and time systems. A cellular automaton system consists of a regular grid of cells, each of which can be in one of a finite number of k possible states, updated synchronously in discrete time steps according to a local, identical interaction rule. The state of a cell is determined by the previous states of a surrounding neighborhood of cells.
Transition probabilities for the typical CA model depend on the state of a cell, the state of its surrounding cells, the physical characteristics of the cell (e.g., terrain, soil quality, vegetation, hydrology, and demographic characteristics), and the weights associated with the neighborhood context of the cell (e.g., proximity to other villages and the time since settlement). These weights and neighborhood conditions are determined from empirical analyses of LUCC based on social survey data, the GIS database that represents resource endowments of a site, and the spatial linkages between villages, land parcels, and other critical landscape features.
Keywords Geographic Information Systems; Simulation ,CellularAutomata