A user-friendly assessment of six commonly used urban growth models

Abstract

An accurate grasp of urban expansion patterns is conducive to efficient urban management and planning. Various urban growth models have been developed to meet this need in the last two decades. As more models become available, users increasingly face the challenge of choosing the right one for their purposes. In this study, we first reviewed the recent usage pattern of urban growth models (UGMs) and identified the top ten UGMs accounting for 73.3% of total usage from 2000 to 2021. We then compared the performance of six commonly used UGMs in simulating urban expansion, including the Cellular Automata-Markov model (CA-Markov), Slope, land use, excluded layer, urban extent, transportation, hillshade (SLEUTH), Conversion of Land Use and its Effects at Small extent model (CLUE-S), Future land use simulation model (FLUS), Land Use Scenario Dynamics model (LUSD), and Land Change Modeler (LCM). The behaviors of the six models were verified against descriptions in the model's documentation. We also analyzed the models' documentation, focusing on data requirements and the user's flexibility in the modeling process. The results showed that the validation accuracies of the models varied with the inputted data, indicating a model does not have an intrinsic accuracy. CA-Markov, FLUS, LUSD, and LCM could be verified, while CLUE-S and SLEUTH failed to meet some verification criteria. In addition, SLEUTH has the highest requirement for input data among all studied models. FLUS and LCM allow for higher user flexibility in modeling than others. This study's findings can help users decide which of the six urban growth models suits them.