Cellular automata and geographic information systems
Contemporary geographic information systems (GIS) suffer from a variety of problems. These include poor performance for many operators, poor ability to handle dynamic spatial models, and poor handling of the temporal dimension. Cellular automata (CA) have much in common with raster GIS and also excel in many of the areas in which GIS are deficient. Specifically, CA provide explicit handling of dynamic spatial models and time. In addition, if special hardware -- cellular automata machines -- are used, the potential for considerable performance benefits exists. Many spatial analytical operators behave, in effect, as CA, with the specific GIS functions representing the CA transition rules. Examples of such operations include filtering and diffusion. If the spatial operators are considered to be CA, an improved ability to characterize the operators mathematically is achieved, resulting in an improved dynamic spatial modeling ability. In this research the similarities between the two models (GIS and CA) are examined and the ability to implement each in the other is demonstrated. In addition, the advantages of integration of the two systems, by means of a cellular automata machine as the analytical engine for GIS, are discussed.
When requesting a correction, please mention this item's handle: RePEc:pio:envirb:v:24:y:1997:i:2:p:219-234. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Neil Hammond)
If references are entirely missing, you can add them using this form.