A Geographic Object-based Approach in Cellular Automata Modeling

Abstract

This paper describes an optimized implementation of an object-based cellular automata (CA) model recently developed to overcome the sensitivity of standard raster CA models to cell size and neighborhood configuration. In this CA model, space is partitioned using a vector structure in which the polygons correspond to meaningful geographical entities. The model allows the geometric transformation of each object based on the influence of its respective neighbors. In addition, it incorporates the concept of dynamic neighborhood where the neighborhood relationships among objects are expressed semantically, removing any restriction of distance in the neighborhood definition. The optimized implementation described in this paper makes use of a spatial database and spatial indexes to handle several spatial operations, which considerably reduces the computation time required for the simulations. The model is simple, flexible and robust, and can be easily adapted to various geographic areas at different scales.