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Sustainability 2018, 10(12), 4758;

Is Big Good or Bad?: Testing the Performance of Urban Growth Cellular Automata Simulation at Different Spatial Extents

College of Natural Resources and Technology, Sichuan Agricultural University, 211 Huimin Road, Wenjiang District, Chengdu 611130, China
Department of Land Economy, University of Cambridge, 19 Silver Street, Cambridge CB3 9EP, UK
Author to whom correspondence should be addressed.
Received: 8 November 2018 / Revised: 7 December 2018 / Accepted: 9 December 2018 / Published: 13 December 2018
(This article belongs to the Special Issue Spatial Analysis of Urbanization towards Urban Sustainability)
PDF [2232 KB, uploaded 13 December 2018]


The accurate prediction of urban growth is pivotal for managing urbanization, especially in fast-urbanizing countries. For this purpose, cellular automata-based (CA) simulation tools have been widely developed and applied. Previous studies have extensively discussed various model building and calibration techniques to improve simulation performance. However, it has been a common practice that the simulation is conducted at and only at the spatial extent where the results are needed, while as we know, urban development in one place can also be influenced by the situations in the broader contexts. To tackle this gap, in this paper, the impact of the simulation of spatial extent on simulation performance is tested and discussed. We used five villages at the rural–urban fringe in Chengdu, China as the case study. Urban growth CA models are built and trained at the spatial extent of the village and the whole city. Comparisons between the simulation results and the actual urban growth in the study area from 2005 to 2015 show that the accuracy of the city model was 7.33% higher than the village model and the latter had more errors in simulating the growth of small clusters. Our experiment suggests that, at least in some cases, urban growth modeling at a larger spatial extent can yield better results than merely modeling the area of interest, and the impacts of the spatial extent of simulation should be considered by modelers. View Full-Text
Keywords: urbanization; urban growth modelling; rural-urban fringe; Markov–CA model; Chengdu urbanization; urban growth modelling; rural-urban fringe; Markov–CA model; Chengdu

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Gao, X.; Liu, Y.; Liu, L.; Li, Q.; Deng, O.; Wei, Y.; Ling, J.; Zeng, M. Is Big Good or Bad?: Testing the Performance of Urban Growth Cellular Automata Simulation at Different Spatial Extents. Sustainability 2018, 10, 4758.

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