Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules

International Journal of Geographical Information Science

Volumn 31, Issue 6, 2017, Pages 1198-1219

  Feng, Yongjiu ^a  

^a SHANGHAI OCEAN UNIVERSITY   (China)

Author keywords

cellular automata; generalized pattern search (GPS); global optimization; logistic regression (LR); simulation accuracy; Urban land use change

Indexed keywords

ACCURACY ASSESSMENT; CELLULAR AUTOMATON; GIS; LAND USE CHANGE; OPTIMIZATION; REGRESSION ANALYSIS; URBAN AREA;

CHINA; JIANGSU; KUNSHAN; YANGTZE DELTA;

EID: 85011805051     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2017.1287368     Document Type: Article

References (70)

1. Aljoufie, M., 2014. Toward integrated land use and transport planning in fast-growing cities:the case of Jeddah, Saudi Arabia. Habitat International, 41, 205–215. doi:10.1016/j.habitatint.2013.08.010
2. Al-Kheder, S., Wang, J., and Shan, J., 2008. Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images. International Journal of Geographical Information Science, 22 (11–12), 1271–1293. doi:10.1080/13658810701617292
3. Almeida, C., et al. 2008. Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics. International Journal of Geographical Information Science, 22 (9), 943–963. doi:10.1080/13658810701731168
4. Al-Shalabi, M., et al. 2013. Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models:the case of Sana’a metropolitan city, Yemen. Environmental Earth Sciences, 70 (1), 425–437. doi:10.1007/s12665-012-2137-6
5. Arsanjani, J.J., et al. 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275. doi:10.1016/j.jag.2011.12.014
6. Audet, C., and Dennis Jr, J.E., 2002. Analysis of generalized pattern searches. SIAM Journal on Optimization, 13 (3), 889–903. doi:10.1137/S1052623400378742
7. Batty, M., and Xie, Y., 1994. From cells to cities. Environment and Planning B:Planning and Design, 21 (7), S31–S48. doi:10.1068/b21S031
8. Batty, M., Xie, Y., and Sun, Z., 1999. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23 (3), 205–233. doi:10.1016/S0198-9715(99)00015-0
9. Cao, K., et al. 2014. Calibrating a cellular automata model for understanding rural-urban land conversion:a Pareto front-based multi-objective optimization approach. International Journal of Geographical Information Science, 28 (5), 1028–1046. doi:10.1080/13658816.2013.851793
10. Cao, M., et al. 2015. A new discovery of transition rules for cellular automata by using cuckoo search algorithm. International Journal of Geographical Information Science, 290 (ahead–of–print), 1–19. doi:10.1016/j.ins.2014.08.033
11. Cao, M., et al. 2016. A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion. International Journal of Geographical Information Science, 1–19. doi:10.1080/13658816.2016.1151521
12. Chakhar, S., and Mousseau, V., 2008. GIS‐based multicriteria spatial modeling generic framework. International Journal of Geographical Information Science, 22 (11–12), 1159–1196. doi:10.1080/13658810801949827
13. Chaudhuri, G., and Clarke, K.C., 2014. Temporal accuracy in urban growth forecasting:a study using the SLEUTH model. Transactions in GIS, 18 (2), 302–320. doi:10.1111/tgis.2014.18.issue-2
14. Clarke, K.C., and Gaydos, L.J., 1998. Loose-coupling a cellular automaton model and GIS:long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12 (7), 699–714. doi:10.1080/136588198241617
15. Dahal, K.R., and Chow, T.E., 2015. Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth. International Journal of Geographical Information Science, 29 (3), 475–497. doi:10.1080/13658816.2014.987779
16. Dietzel, C., and Clarke, K., 2006. The effect of disaggregating land use categories in cellular automata during model calibration and forecasting. Computers, Environment and Urban Systems, 30 (1), 78–101. doi:10.1016/j.compenvurbsys.2005.04.001
17. Feng, Y., et al. 2011b. 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
18. Feng, Y., and Liu, Y., 2013a. A cellular automata model based on nonlinear kernel principal component analysis for urban growth simulation. Environment and Planning B:Planning and Design, 40 (1), 116–134. doi:10.1068/b37142
19. Feng, Y., and Liu, Y., 2013b. A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing. International Journal of Geographical Information Science, 27 (3), 449–466. doi:10.1080/13658816.2012.695377
20. Feng, Y., and Liu, Y., 2016. Scenario prediction of emerging coastal city using CA modeling under different environmental conditions:a case study of Lingang New City, China. Environmental Monitoring and Assessment, 188 (9), 1–15. doi:10.1007/s10661-016-5558-y
21. Feng, Y., Liu, Y., and Batty, M., 2016. Modeling urban growth with GIS based cellular automata and least squares SVM rules:a case study in Qingpu–Songjiang area of Shanghai, China. Stochastic Environmental Research and Risk Assessment, 30 (5), 1387–1400. doi:10.1007/s00477-015-1128-z
22. Feng, Y., Liu, Y., and Han, Z., 2011a. Land use simulation and landscape assessment by using genetic algorithm based on cellular automata under different sampling schemes. Chinese Journal of Applied Ecology, 22 (4), 957–963.
23. Guan, D., et al. 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222 (20), 3761–3772. doi:10.1016/j.ecolmodel.2011.09.009
24. He, C., et al. 2006. Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Applied Geography, 26 (3), 323–345. doi:10.1016/j.apgeog.2006.09.006
25. Huang, B., et al. 2009a. Land-use-change modeling using unbalanced support-vector machines. Environment and Planning B:Planning and Design, 36 (3), 398–416. doi:10.1068/b33047
26. Huang, B., Zhang, L., and Wu, B., 2009b. Spatiotemporal analysis of rural–urban land conversion. International Journal of Geographical Information Science, 23 (3), 379–398. doi:10.1080/13658810802119685
27. Kunshan Statistical Survey Public Website, 2016. Annual statistics from 2000 to 2015 [online]. Available from: http://www.kstj.gov.cn/tpw/home.action.
28. Li, L., Sato, Y., and Zhu, H., 2003. Simulating spatial urban expansion based on a physical process. Landscape and Urban Planning, 64 (1), 67–76. doi:10.1016/S0169-2046(02)00201-3
29. 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
30. 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
31. Li, X., and Yeh, A.G.-O., 2002a. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16 (4), 323–343. doi:10.1080/13658810210137004
32. Li, X., and Yeh, A.G.-O., 2002b. Urban simulation using principal components analysis and cellular automata for land-use planning. Photogrammetric Engineering and Remote Sensing, 68 (4), 341–351.
33. 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
34. Liao, J., et al. 2016. Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations. Environmental Modelling & Software, 75, 163–175. doi:10.1016/j.envsoft.2015.10.014
35. Liu, X., et al. 2008. A bottom-up approach to discover transition rules of cellular automata using ant intelligence. International Journal of Geographical Information Science, 22 (11–12), 1247–1269. doi:10.1080/13658810701757510
36. Liu, X., et al. 2014a. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. International Journal of Geographical Information Science, 28 (1), 148–163. doi:10.1080/13658816.2013.831097
37. Liu, X.P., et al. 2012a. A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas. International Journal of Geographical Information Science, 26 (7), 1325–1343. doi:10.1080/13658816.2011.635594
38. Liu, Y., 2012b. Modelling sustainable urban growth in a rapidly urbanising region using a fuzzy-constrained cellular automata approach. International Journal of Geographical Information Science, 26 (1), 151–167. doi:10.1080/13658816.2011.577434
39. Liu, Y., and Feng, Y., 2012. A logistic based cellular automata model for continuous urban growth simulation:A case study of the Gold Coast City, Australia. In A.J. Heppenstall et al., eds. Agent-based Models of Geographical Systems. Springer. 643–662.
40. Liu, Y., and Feng, Y., 2016. Simulating the impact of economic and environmental strategies on future urban growth scenarios in Ningbo, China. Sustainability, 8 (10), 1045. doi:10.3390/su8101045
41. Liu, Y., Feng, Y., and Pontius, R.G., 2014b. Spatially-explicit simulation of urban growth through self-adaptive genetic algorithm and cellular automata modelling. Land, 3 (3), 719–738. doi:10.3390/land3030719
42. Long, H., et al. 2007. Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China. Journal of Environmental Management, 83 (3), 351–364. doi:10.1016/j.jenvman.2006.04.003
43. Moghadam, H.S., and Helbich, M., 2013. Spatiotemporal urbanization processes in the megacity of Mumbai, India:a Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149. doi:10.1016/j.apgeog.2013.01.009
44. Munshi, T., et al. 2014. Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India. Cities, 39, 68–86. doi:10.1016/j.cities.2014.02.007
45. Mustafa, A., et al. 2014. Measuring the effect of stochastic perturbation component in cellular automata urban growth model. Procedia Environmental Sciences, 22, 156–168. doi:10.1016/j.proenv.2014.11.016
46. Neumann, J.V., 1966. Theory of self-reproducing automata. Champaign, IL, USA:University of Illinois Press.
47. Onsted, J.A., and Chowdhury, R.R., 2014. Does zoning matter? A comparative analysis of landscape change in Redland, Florida using cellular automata. Landscape and Urban Planning, 121, 1–18. doi:10.1016/j.landurbplan.2013.09.007
48. Pontius, R.G., Huffaker, D., and Denman, K., 2004a. Useful techniques of validation for spatially explicit land-change models. Ecological Modelling, 179 (4), 445–461. doi:10.1016/j.ecolmodel.2004.05.010
49. Pontius, R.G., and Millones, M., 2011. Death to Kappa:birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32 (15), 4407–4429. doi:10.1080/01431161.2011.552923
50. Pontius, R.G., Peethambaram, S., and Castella, J.-C., 2011. Comparison of three maps at multiple resolutions:a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers, 101 (1), 45–62. doi:10.1080/00045608.2010.517742
51. Pontius, R.G., Shusas, E., and McEachern, M., 2004b. Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment, 101 (2), 251–268. doi:10.1016/j.agee.2003.09.008
52. R Development Core Team, 2014. R:A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. 2013. ISBN 3-900051-07-0. Availble from:http://www.gbif.org/resource/81287.
53. Stevens, D., Dragicevic, S., and Rothley, K., 2007. iCity:a GIS–CA modelling tool for urban planning and decision making. Environmental Modelling & Software, 22 (6), 761–773. doi:10.1016/j.envsoft.2006.02.004
54. Tobler, W., 1979. Cellular geography. In S. Gale and G. Olsson, eds. Philosophy in geography. Springer. 379–386.
55. Tokan, F., and Gunes, F., 2011. Interference suppression by optimising the positions of selected elements using generalised pattern search algorithm. Microwaves, Antennas & Propagation, IET, 5 (2), 127–135. doi:10.1049/iet-map.2009.0374
56. United Nations, 2007. World Urbanization Prospects The 2007 Revision. New York, http://www.un.org/esa/population/publications/wup2007/2007WUP_Highlights_web.pdf.
57. Valdez, F., Melin, P., and Castillo, O., 2011. Bio-inspired optimization methods for minimization of complex mathematical functions. In I. Batyrshin and G. Sidorov,eds. Advances in Soft Computing. Springer. 131–142.
58. Van Vliet, J., White, R., and Dragicevic, S., 2009. Modeling urban growth using a variable grid cellular automaton. Computers, Environment and Urban Systems, 33 (1), 35–43. doi:10.1016/j.compenvurbsys.2008.06.006
59. Verburg, P.H., et al. 2002. Modeling the spatial dynamics of regional land use:the CLUE-S model. Environmental Management, 30 (3), 391–405. doi:10.1007/s00267-002-2630-x
60. Verburg, P.H., et al. 2004. Land use change modelling:current practice and research priorities. GeoJournal, 61 (4), 309–324. doi:10.1007/s10708-004-4946-y
61. Wei, Y.D., 2002. Beyond the Sunan model:trajectory and underlying factors of development in Kunshan, China. Environment and Planning A, 34 (10), 1725–1747. doi:10.1068/a3567
62. 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 (8), 1175–1199. doi:10.1068/a251175
63. 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 (5), 383–400. doi:10.1016/S0198-9715(00)00012-0
64. Wu, F., 1996. A linguistic cellular automata simulation approach for sustainable land development in a fast growing region. Computers, Environment and Urban Systems, 20 (6), 367–387. doi:10.1016/S0198-9715(97)00003-3
65. 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
66. Wu, F., 2002. Calibration of stochastic cellular automata:the application to rural-urban land conversions. International Journal of Geographical Information Science, 16 (8), 795–818. doi:10.1080/13658810210157769
67. Wu, F., and Webster, C.J., 1998. Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B:Planning and Design, 25, 103–126. doi:10.1068/b250103
68. Xie, Y., et al. 2006. Ecological analysis of an emerging urban landscape pattern—desakota:a case study in Suzhou, China. Landscape Ecology, 21 (8), 1297–1309. doi:10.1007/s10980-006-0024-9
69. Yang, Q., Li, X., and Shi, X., 2008. Cellular automata for simulating land use changes based on support vector machines. Computers & Geosciences, 34 (6), 592–602. doi:10.1016/j.cageo.2007.08.003
70. Zhang, T., 2002. Urban development and a socialist pro-growth coalition in Shanghai. Urban Affairs Review, 37 (4), 475–499. doi:10.1177/10780870222185432