530
Views
23
CrossRef citations to date
0
Altmetric
Original Articles

How much past to see the future: a computational study in calibrating urban cellular automata

, &
Pages 349-374 | Received 13 Apr 2014, Accepted 21 Sep 2014, Published online: 09 Jan 2015

References

  • Avolio, M.V., et al., 2006. SCIARA γ2: an improved cellular automata model for lava flows and applications to the 2002 Etnean crisis. Computers & Geosciences, 32 (7), 876–889. doi:10.1016/j.cageo.2005.10.026
  • Barredo, J.I., et al., 2003. Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64 (3), 145–160. doi:10.1016/S0169-2046(02)00218-9
  • Batty, M. and Xie, Y., 1994. From cells to cities. Environment and Planning B: Planning and Design, 21, 31–48. doi:10.1068/b21s031
  • Blecic, I., et al., 2004. Modelling urban dynamics with cellular automata: a model of the city of Heraklion. In: 7th AGILE conference on geographic information science. Heraklion: University of Crete Press, 313–323.
  • Blečić, I., et al., 2014a. Urban metabolism and climate change: a planning support system. International Journal of Applied Earth Observation and Geoinformation, 26, 447–457. doi:10.1016/j.jag.2013.08.006
  • Blecic, I., Cecchini, A., and Trunfio, G.A., 2010. A comparison of evolutionary algorithms for automatic calibration of constrained cellular automata. In: Proceedings of international conference on computational science and its applications – ICCSA 2010, Part I, 23–26 March, Fukuoka. Springer, 166–181.
  • Blecic, I., Cecchini, A., and Trunfio, G.A., 2013. Cellular automata simulation of urban dynamics through GPGPU. The Journal of Supercomputing, 65 (2), 614–629. doi:10.1007/s11227-013-0913-z
  • Blecic, I., Cecchini, A., and Trunfio, G.A., 2014b. Fast and accurate optimization of a GPU-accelerated CA urban model through cooperative coevolutionary particle swarms. Procedia Computer Science, 29, 1631–1643. doi:10.1016/j.procs.2014.05.148
  • Cecchini, A., 1996. Urban modelling by means of cellular automata: generalised urban automata with the help on-line (AUGH) model. Environment and Planning B: Planning and Design, 23, 721–732. doi:10.1068/b230721
  • Cheng, J. and Masser, I., 2004. Understanding spatial and temporal processes of urban growth: cellular automata modelling. Environment and Planning B: Planning and Design, 31 (2), 167–194. doi:10.1068/b2975
  • Clarke, K.C., Hoppen, S., and Gaydos, L., 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24 (2), 247–261. doi:10.1068/b240247
  • Cohen, J., 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20 (1), 37–46. doi:10.1177/001316446002000104
  • Demsar, J., 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.
  • Derrac, J., et al., 2011. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1 (1), 3–18. doi:10.1016/j.swevo.2011.02.002
  • Engelen, G. and White, R., 2008. Validating and calibrating integrated cellular automata based models of land use change. In: S. Albeverio, et al., eds. The dynamics of complex urban systems. Heidelberg: Physica-Verlag HD, 185–211.
  • Engelen, G., White, R., and De Nijs, T., 2005. Environment explorer: spatial support system for the integrated assessment of socio-economic and environmental policies in the Netherlands. Integrated Assessment, 4, 2.
  • Feng, Y., et al., 2011. Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102 (3), 188–196. doi:10.1016/j.landurbplan.2011.04.004
  • Friedman, M., 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32 (200), 675–701. doi:10.1080/01621459.1937.10503522
  • Friedman, M., 1940. A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11 (1), 86–92. doi:10.1214/aoms/1177731944
  • García, A.M., et al., 2012. A comparative analysis of cellular automata models for simulation of small urban areas in Galicia, NW Spain. Computers, Environment and Urban Systems, 36 (4), 291–301. doi:10.1016/j.compenvurbsys.2012.01.001
  • García, S., et al., 2010. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Information Sciences, 180 (10), 2044–2064. doi:10.1016/j.ins.2009.12.010
  • Geertman, S. and Stillwell, J., 2004. Planning support systems: an inventory of current practice. Computers, Environment and Urban Systems, 28 (4), 291–310. doi:10.1016/S0198-9715(03)00024-3
  • Goldstein, N.C., 2003. Brains vs. Brawn-Comparative strategies for the calibration of a cellular automata-based urban growth model. In: Proceedings of the 7th international conference on geocomputation, 8–10 September, Southampton. University of Southampton.
  • He, C., et al., 2008. Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning, 86 (1), 79–91. doi:10.1016/j.landurbplan.2007.12.010
  • Kennedy, J. and Clerc, M., 2006. Standard PSO 2006 [online]. Available from: http://www.particleswarm.info/Standard_PSO_2006.c [Accessed January 2014].
  • Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Vol. 4, 27 November–1 December, Perth. University of Western Australia, 1942–1948.
  • Lavalle, C., et al., 2011. A high resolution land use/cover modelling framework for Europe: introducing the EU-ClueScanner100 model. In: ICCSA 2011, LNCS, Santander: Springer-Verlag, 60–75.
  • Li, X., et al., 2012. Assimilating process context information of cellular automata into change detection for monitoring land use changes. International Journal of Geographical Information Science, 26 (9), 1667–1687. doi:10.1080/13658816.2011.643803
  • Li, X., et al., 2013. Calibrating cellular automata based on landscape metrics by using genetic algorithms. International Journal of Geographical Information Science, 27 (3), 594–613. doi:10.1080/13658816.2012.698391
  • Li, X. and Yeh, A.G.O., 2000. Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14 (2), 131–152. doi:10.1080/136588100240886
  • Liao, J., et al., 2014. A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence. International Journal of Geographical Information Science, 28 (4), 720–738. doi:10.1080/13658816.2013.869820
  • Nemenyi, P., 1963. Distribution-free multiple comparisons. Thesis (PhD). Princeton University.
  • Omidvar, M.N., et al., 2010. Cooperative co-evolution for large scale optimization through more frequent random grouping. In: Proceedings of the IEEE congress on evolutionary computation, CEC 2010, 18–23 July, Barcelona. IEEE, 1–8.
  • Potter, M.A. and De Jong, K.A., 1994. A cooperative coevolutionary approach to function optimization. In: Parallel problem solving from nature – PPSN III, international conference on evolutionary computation, 9–14 October, Jerusalem. Springer-Verlag, 249–257.
  • R Core Team, 2013. A language and environment for statistical computing [online]. Available from: http://www.R-project.org/ [Accessed September 2014].
  • Rabbani, A., Aghababaee, H., and Rajabi, M.A., 2012. Modeling dynamic urban growth using hybrid cellular automata and particle swarm optimization. Journal of Applied Remote Sensing, 6, 1. doi:10.1117/1.JRS.6.063582
  • Santé, I., et al., 2010. Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landscape and Urban Planning, 96 (2), 108–122. doi:10.1016/j.landurbplan.2010.03.001
  • Straatman, B., White, R., and Engelen, G., 2004. Towards an automatic calibration procedure for constrained cellular automata. Computers, Environment and Urban Systems, 28 (1–2), 149–170. doi:10.1016/S0198-9715(02)00068-6
  • Trunfio, G.A., 2014. Enhancing the firefly algorithm through a cooperative coevolutionary approach: an empirical study on benchmark optimisation problems. Ijbic, 6 (2), 108–125. doi:10.1504/IJBIC.2014.060621
  • Van Vliet, J., Bregt, A.K., and Hagen-Zanker, A., 2011. Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecological Modelling, 222 (8), 1367–1375. doi:10.1016/j.ecolmodel.2011.01.017
  • Vanden Bergh, F. and Engelbrecht, A.P., 2004. A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8 (3), 225–239. doi:10.1109/TEVC.2004.826069
  • Welch, B.L., 1947. The generalization of ‘student’s’ problem when several different population variances are involved. Biometrika, 34 (1/2), 28–35.
  • White, R. and Engelen, G., 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and Planning A, 25, 1175–1199. doi:10.1068/a251175
  • White, R. and Engelen, G., 2000. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 24, 383–400. doi:10.1016/S0198-9715(00)00012-0
  • White, R., Engelen, G., and Uljee, I., 1997. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design, 24 (3), 323–343. doi:10.1068/b240323
  • Wu, F., 1998. SimLand: a prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules. International Journal of Geographical Information Science, 12 (1), 63–82. doi:10.1080/136588198242012
  • Yang, Z., Tang, K., and Yao, X., 2008. Large scale evolutionary optimization using cooperative coevolution. Information Sciences, 178 (15), 2985–2999. doi:10.1016/j.ins.2008.02.017
  • Yeh, A.G.O. and Li, X., 2006. Errors and uncertainties in urban cellular automata. Computers, Environment and Urban Systems, 30 (1), 10–28. doi:10.1016/j.compenvurbsys.2004.05.007

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.