Mixed-cell cellular automata: A new approach for simulating the spatio-temporal dynamics of mixed land use structures

Abstract

When used for land use change modeling, Cellular Automata (CA) usually assume that each cell has only one land use type at each time step, ignoring the mixed land use structures that are often found in land units. Mixed cells, composed of cover proportions of multiple land use types, provide a new perspective for modeling the spatio-temporal dynamics of mixed land use structures. Simulating land use change with mixed cells is challenging because mixed-cell CAs are fundamentally different from conventional CAs. This study develops a mixed-cell CA (MCCA). The structure of the CA is re-designed based on the cover proportion of land uses, including the representations of cell state, lattice, and neighborhood. The transition rules are automatically constructed by random-forest regression using historical data and a competition mechanism among multiple land use types at the sub-cell scale is proposed. In addition, a mixed-cell figure of merit (mcFoM) accuracy measure is proposed to validate the MCCA. The MCCA was applied to the Wuhan metropolitan area in China, and the results show that the MCCA was able to simulate the subtle changes of land use proportions within land units. The MCCA represents a new breed of geospatial CA models for spatio-temporal dynamics of mixed land use structures, which enables mixed land use research to leap from static analysis to dynamic simulation. The software for MCCA has been made available at https://github.com/HPSCIL/Mixed_Cell_Cellullar_Automata.