Abstract
Cellular automata (CA) is a bottom-up self-organizing modeling tool for simulating contagion-like phenomena such as complex land-use change and urban growth. It is not known how CA modeling responds to changes in spatial observation scale when a larger-scale study area is partitioned into subregions, each with its own CA model. We examined the impact of changing observation scale on a model of urban growth at UA-Shanghai (a region within a one-hour high-speed rail distance from Shanghai) using particle swarm optimization-based CA (PSO-CA) modeling. Our models were calibrated with data from 1995 to 2005 and validated with data from 2005 to 2015 on spatial scales: (1) Regional-scale: UA-Shanghai was considered as a single study area; (2) meso-scale: UA-Shanghai was partitioned into three terrain-based subregions; and (3) city-scale: UA-Shanghai was partitioned into six cities based on administrative boundaries. All three scales yielded simulations averaging about 87% accuracy with an average Figure-of-Merit (FOM) of about 32%. Overall accuracy was reduced from calibration and validation. The regional-scale model yielded less accurate simulations as compared with the meso- and city-scales for both calibration and validation. Simulation success in different subregions is independent at the city-scale, when compared with regional- and meso-scale. Our observations indicate that observation scale is important in CA modeling and that smaller scales probably lead to more accurate simulations. We suggest smaller partitions, smaller observation scales and the construction of one CA model for each subregion to better reflect spatial variability and to produce more reliable simulations. This approach should be especially useful for large-scale areas such as huge urban agglomerations and entire nations.
Highlights
Cellular automata (CA) is a well-known bottom-up self-organizing model for simulation of contagion-like phenomena such as complex land-use change and urban sprawl [1]
CA models and their simulation results are substantially affected by partitioning of the study area and its observation scale
The impact of observation scale on modeling was examined by simulating urban growth in the UA-Shanghai region using particle swarm optimization-based CA (PSO-CA)
Summary
Cellular automata (CA) is a well-known bottom-up self-organizing model for simulation of contagion-like phenomena such as complex land-use change and urban sprawl [1]. By defining land transition rules, CA models can be used to explore long-term dynamics of land-use change on regional to global scales [6,7,8,9]. These land transition rules are determined by the combined effects of the current cell state, factors driving cell state change, neighborhood, spatial and quantitative constraints, and stochasticity. These rules are usually deployed in GIS-based software to simulate changing spatial phenomena. CA studies of scaling effects are important for accurate land-use modeling and more reliable future scenario projections
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