Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model
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.
dynamic neighborhood vector-based cellular automata land-use/land-cover changes