In this research, a new vector-based cellular automata model is proposed to overcome the cell size and neighbourhood configuration sensitivity of the classical raster based CA models and the limitations of the previous implementations of vector-based CA models. In this model, space is represented as a collection of geographic objects where each object corresponds to a real-world entity of irregular shape and size that evolves through time. This evolution is expressed by the change of shape and size of the objects and it is performed using a geometric transformation procedure. This procedure reduces the area of an object in the region nearest to the neighbour that exerts an influence on it and increases the area of that neighbour. The neighborhood is not rigid and restricted to a set of adjacent polygons; rather, it is dynamic, it includes the whole geographic space, and it is specific to each object. Their results showed that the geometrical transformations defined in the model generate an adequate evolution of the geometry of the objects, producing more realistic spatial patterns than the ones produced by a raster-based CA model.

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