Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model

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While cellular automata (CA) models have been increasingly used over the last decades to simulate a wide range of spatial phenomena, recent studies have illustrated that they are sensitive to cell size and neighborhood configuration. In this paper, a new vector-based cellular automata (VecGCA) model is described to overcome the scale sensitivity of the raster-based CA models. VecGCA represents space as a collection of geographic objects of irregular shape and size corresponding to real-world entities. The neighborhood includes the whole geographic space; it is dynamic and specific to each geographic object. Two objects are neighbors if they are separated by objects whose states favor the land-use transition between them. The shape and area of the geographic objects change through time according to a transition function that incorporates the influence of the neighbors on the specific geographic object. The model was used to simulate land-use/land cover changes in two regions of different landscape complexity, in Quebec and Alberta, Canada. The results revealed that VecGCA produces realistic spatial patterns similar to reference land-use maps. The space definition removes the dependency of the model to cell size while the dynamic neighborhood removes the rigid, arbitrarily defined zone of influence around each geographic object.