Land 2014, 3(3), 719-738; doi:10.3390/land3030719
Article

Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling

1,* , 2
 and Jr. 3
Received: 21 April 2014; in revised form: 3 July 2014 / Accepted: 8 July 2014 / Published: 18 July 2014
(This article belongs to the Special Issue Land Change Modeling: Connecting to the Bigger Picture)
View Full-Text   |   Download PDF [4181 KB, uploaded 18 July 2014]
Abstract: This paper presents a method to optimise the calibration of parameters and land use transition rules of a cellular automata (CA) urban growth model using a self-adaptive genetic algorithm (SAGA). Optimal calibration is achieved through an algorithm that minimises the difference between the simulated and observed urban growth. The model was applied to simulate land use change from non-urban to urban in South East Queensland’s Logan City, Australia, from 1991 to 2001. The performance of the calibrated model was evaluated by comparing the empirical land use change maps from the Landsat imagery to the simulated land use change produced by the calibrated model. The simulation accuracies of the model show that the calibrated model generated 86.3% correctness, mostly due to observed persistence being simulated as persistence and some due to observed change being simulated as change. The 13.7% simulation error was due to nearly equal amounts of observed persistence being simulated as change (7.5%) and observed change being simulated as persistence (6.2%). Both the SAGA-CA model and a logistic-based CA model without SAGA optimisation have simulated more change than the amount of observed change over the simulation period; however, the overestimation is slightly more severe for the logistic-CA model. The SAGA-CA model also outperforms the logistic-CA model with fewer quantity and allocation errors and slightly more hits. For Logan City, the most important factors driving urban growth are the spatial proximity to existing urban centres, roads and railway stations. However, the probability of a place being urbanised is lower when people are attracted to work in other regions.
Keywords: self-adaptive genetic algorithm (SAGA); cellular automata (CA); urban land transition; simulation accuracies; Logan City
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Liu, Y.; Feng, Y.; Pontius, R.G., Jr. Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling. Land 2014, 3, 719-738.

AMA Style

Liu Y, Feng Y, Pontius RG, Jr. Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling. Land. 2014; 3(3):719-738.

Chicago/Turabian Style

Liu, Yan; Feng, Yongjiu; Pontius, Robert G., Jr. 2014. "Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling." Land 3, no. 3: 719-738.

Land EISSN 2073-445X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert