The effect of disaggregating land use categories in cellular automata during model calibration and forecasting

Spatial models of urban growth have the ability to play an important role in the planning process; if not in aiding in policy decisions, then in processes such as visioning, storytelling, and scenario evaluation. One question that has not adequately been addressed is to what degree does disaggregating land use types from urban/non-urban categories add to these simulations? This paper aims to answer this question by modeling urbanization in San Joaquin County (CA) using the SLEUTH urban growth model with two equal, but different datasets; one with urban/non-urban data, and the other with the same data, but the non-urban data disaggregated in nine land uses. The results show that there is an explicit link between the likelihood of urbanization, and the type of land use that will be converted to urban, and suggest that future exercises using spatial models should not ignore the impact of aggregating individual land use categories into urban–non-urban classes.