An object-based cellular automata model to mitigate scale dependency

Abstract

Cellular automata (CA) are individual-based spatial models increasingly used to simulate the dynamics of natural and human systems and forecast their evolution. Despite their simplicity, they can exhibit extraordinary rich behavior and are remarkably effective at generating realistic simulations of land-use patterns and other spatial structures. However, recent studies have demonstrated that the standard raster-based CA models are sensitive to spatial scale, more specifically to the cell size and neighborhood configuration used for the simulation. To mitigate cell size dependency, a novel object-based CA model has been developed where space is represented using a vector structure in which the polygons correspond to meaningful geographical entities composing the landscape under study. In addition, the proposed object-based CA model allows the geometric transformation of each polygon, expressed as a change of state in part or in totality of its surface, based on the influence of its respective neighbors. The implementation and testing of this model on real data reveals that it generates spatial configurations of landscape patches that are more realistic than the conventional raster-based CA model.