Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth

The neighborhood definition, which determines the influence on a cell from its nearby cells within a localized region, plays a critical role in the performance of a cellular automaton (CA) model. Raster CA models use a cellular grid to represent geographic space, and are sensitive to the cell size and neighborhood configuration. However, the sensitivity of vector-based CAs, an alternative to the raster-based counterpart, to neighborhood type and size remains uninvestigated. The present article reports the results of a detailed sensitivity analysis of an irregular CA model of urban land use dynamics. The model uses parcel data at the cadastral scale to represent geographic space, and was implemented to simulate urban growth in Central Texas, USA. Thirty neighborhood configurations defined by types and sizes were considered in order to examine the variability in the model outcome. Results from accuracy assessments and landscape metrics confirmed the model’s sensitivity to neighborhood configurations. Furthermore, the centroid intercepted neighborhood with a buffer of 120 m produced the most accurate simulation result. This neighborhood produced scattered development while the centroid extent-wide neighborhood resulted in a clustered development predominantly near the city center.