Experiences and issues of using cellular automata for assisting urban and regional planning in China

International Journal of Geographical Information Science

Volumn 31, Issue 8, 2017, Pages 1606-1629

  Li, Xia ^a   Chen, Yimin ^a   Liu, Xiaoping ^a   Xu, Xiaocong ^a   Chen, Guangliang ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

Cellular automata; China; developing countries; simulation; urban and regional planning; urban development

Indexed keywords

CELLULAR AUTOMATON; DEVELOPING WORLD; LAND USE PLANNING; LITERATURE REVIEW; REGIONAL PLANNING; URBAN DEVELOPMENT; URBAN GROWTH; URBAN PLANNING;

CHINA;

EID: 85014750446     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2017.1301457     Document Type: Review

References (131)

1. Batty, M., and Xie, Y., 1994. From cells to cities. Environment and Planning B:Planning and Design, 21 (7), 531–548. doi:10.1068/b21S031.
2. Chen, J., et al., 1994. The development of a knowledge-based geographical information system for the zoning of rural areas. Environment and Planning B, 21, 179–179. doi:10.1068/b210179.
3. Chen, J., et al., 2002. Assessment of the urban development plan of Beijing by using a CA-based urban growth model. Photogrammetric Engineering & Remote Sensing, 68 (10), 1063–1071.
4. Chen, J., 2007. Rapid urbanization in China:A real challenge to soil protection and food security. Catena, 69 (1), 1–15. doi:10.1016/j.catena.2006.04.019.
5. Chen, J.P., Zeng, M., and Duan, Y.J., 2011. Regional carrying capacity evaluation and prediction based on GIS in the Yangtze River Delta, China. International Journal of Geographical Information Science, 25 (2), 171–190. doi:10.1080/13658816.2010.495075.
6. Chen, X., Yu, S.X., and Zhang, Y.P., 2013a. Evaluation of spatiotemporal dynamics of simulated land use/cover in China using a probabilistic cellular automata-Markov model. Pedosphere, 23 (2), 243–255. doi:10.1016/S1002-0160(13)60013-2.
7. Chen, Y., et al., 2013b. Simulating urban form and energy consumption in the Pearl River Delta under different development strategies. Annals of the Association of American Geographers, 103 (6), 1567–1585. doi:10.1080/00045608.2012.740360.
8. Chen, Y., et al., 2014. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science, 28 (2), 234–255. doi:10.1080/13658816.2013.831868.
9. Chen, Y.H., et al., 2006. Land use pattern evolution and optimization in the farming-pastoral zone of Northern China. Science in China Series E, 132–140.
10. Cheng, J., and Masser, I., 2003. Urban growth pattern modeling:a case study of Wuhan city, PR China. Landscape and Urban Planning, 62 (4), 199–217. doi:10.1016/S0169-2046(02)00150-0.
11. Cheng, J., and Masser, I., 2004. Understanding spatial and temporal processes of urban growth:cellular automata modelling. Environment and Planning B, 31 (2), 167–194. doi:10.1068/b2975.
12. Chung, L.L.W., 1994. The economics of land-use zoning:a literature review and analysis of the work of Coase. Town Planning Review, 65 (1), 77. doi:10.3828/tpr.65.1.j15rh7037v511127.
13. Clarke, K., and Gaydos, L., 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.
14. Clarke, K., 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, 24, 247–262. doi:10.1068/b240247
15. Dahal, K. R., and Chow, T. E., 2014. An agent-integrated irregular automata model of urban land-use dynamics. International Journal of Geographical Information Science, 28 (11), 2281–2303.
16. Deng, X., et al., 2010. Economic growth and the expansion of urban land in China. Urban Studies, 47 (4), 813–843. doi:10.1177/0042098009349770.
17. Ding, W.J., et al., 2013. Cellular automata model as an intuitive approach to simulate complex land-use changes:an evaluation of two multi-state land-use models in the Yellow River Delta. Stochastic Environmental Research and Risk Assessment, 27 (4), 899–907. doi:10.1007/s00477-012-0624-7.
18. Du, J., et al., 2012a. Assessing the effects of urbanization on annual runoff and flood events using an integrated hydrological modeling system for Qinhuai River basin, China. Journal of Hydrology, 464 (465), 127–139. doi:10.1016/j.jhydrol.2012.06.057.
19. Du, Y., et al., 2012b. Comparison between CBR and CA methods for estimating land use change in Dongguan, China. Journal of Geographical Sciences, 22 (4), 716–736. doi:10.1007/s11442-012-0958-6.
20. Fan, F.L., Wang, Y.P., and Wang, Z.S., 2008. Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental Monitoring and Assessment, 137 (1–3), 127–147. doi:10.1007/s10661-007-9734-y.
21. Fang, M., Chan, C., and Yao, X., 2009. Managing air quality in a rapidly developing nation:China. Atmospheric Environment, 43 (1), 79–86. doi:10.1016/j.atmosenv.2008.09.064.
22. Feng, H., Liu, H., and Lü, Y., 2012. Scenario prediction and analysis of urban growth using SLEUTH model. Pedosphere, 22 (2), 206–216. doi:10.1016/S1002-0160(12)60007-1.
23. Feng, Y., and Liu, Y., 2013a. 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.
24. Feng, Y., Liu, Y., and Liu, D., 2015. Shoreline mapping with cellular automata and the shoreline progradation analysis in shanghai, china from 1979 to 2008. Arabian Journal Of Geosciences, 8 (7), 4337–4351.
25. Feng, Y.J., 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.
26. Feng, Y.J., and Liu, Y., 2013b. A cellular automata model based on nonlinear kernel principal component analysis for urban growth simulation. Environment and Planning B, 40 (1), 117–134. doi:10.1068/b37142.
27. Gong, J., et al., 2009. Urban ecological security assessment and forecasting, based on a cellular automata model:A case study of Guangzhou, China. Ecological Modelling, 220 (24), 3612–3620. doi:10.1016/j.ecolmodel.2009.10.018.
28. Gregg, J.W., Jones, C.G., and Dawson, T.E., 2003. Urbanization effects on tree growth in the vicinity of New York City. Nature, 424 (6945), 183–187. doi:10.1038/nature01728.
29. Guo, H., et al., 2006. Regional and local contributions to ambient non-methane volatile organic compounds at a polluted rural/coastal site in Pearl River Delta, China. Atmospheric Environment, 40 (13), 2345–2359. doi:10.1016/j.atmosenv.2005.12.011.
30. Han, J., et al., 2009. Application of an integrated system dynamics and cellular automata model for urban growth assessment:A case study of Shanghai, China. Landscape and Urban Planning, 91 (3), 133–141. doi:10.1016/j.landurbplan.2008.12.002.
31. He, C., et al., 2005a. Developing land use scenario dynamics model by the integration of system dynamics model and cellular automata model. Science in China Series D, 48 (11), 1979–1989. doi:10.1360/04yd0248.
32. He, C., et al., 2005b. Zoning grassland protection area using remote sensing and cellular automata modeling—a case study in Xilingol steppe grassland in northern China. Journal of Arid Environments, 63 (4), 814–826. doi:10.1016/j.jaridenv.2005.03.028.
33. He, C., et al., 2006. Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Applied Geography, 26 (3–4), 323–345. doi:10.1016/j.apgeog.2006.09.006.
34. 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.
35. He, C., et al., 2011. Simulation of the spatial stress due to urban expansion on the wetlands in Beijing, China using a GIS-based assessment model. Landscape and Urban Planning, 101 (3), 269–277. doi:10.1016/j.landurbplan.2011.02.032.
36. He, C., et al., 2013a. Modeling the urban landscape dynamics in a megalopolitan cluster area by incorporating a gravitational field model with cellular automata. Landscape and Urban Planning, 113, 78–89. doi:10.1016/j.landurbplan.2013.01.004.
37. He, J., et al., 2013b. A counterfactual scenario simulation approach for assessing the impact of farmland preservation policies on urban sprawl and food security in a major grain-producing area of China. Applied Geography, 37, 127–138. doi:10.1016/j.apgeog.2012.11.005.
38. Hua, L., et al., 2014. Simulating urban growth using the SLEUTH model in a coastal peri-urban district in China. Sustainability, 6 (6), 3899–3914. doi:10.3390/su6063899.
39. Huang, B., and Zhang, W.T., 2014. Sustainable land-use planning for a downtown lake area in central China:multiobjective optimization approach aided by urban growth modeling. Journal of Urban Planning and Development, 140 (2), 04014002. doi:10.1061/(ASCE)UP.1943-5444.0000186.
40. Huang, G., and Gao, W., 2011. Simulation study on CA model based on parameter optimization of genetic algorithm and urban development. Procedia Engineering, 15, 2175–2179. doi:10.1016/j.proeng.2011.08.407
41. Huang, Q., and Cai, Y., 2007. Simulation of land use change using GIS-based stochastic model:the case study of Shiqian County, Southwestern China. Stochastic Environmental Research and Risk Assessment, 21 (4), 419–426. doi:10.1007/s00477-006-0074-1.
42. Huang, Q.X., et al., 2014. Modeling the impacts of drying trend scenarios on land systems in northern China using an integrated SD and CA model. Science China Earth Sciences, 57 (4), 839–854. doi:10.1007/s11430-013-4799-7.
43. Jantz, C. A., et al., 2010. Designing and implementing a regional urban modeling system using the sleuth cellular urban model. Computers, Environment and Urban Systems, 34 (1), 1–16.
44. Ke, X.L., Wu, F., and Ma, C.X., 2013. Scenario analysis on climate change impacts of urban land expansion under different urbanization patterns:a case study of Wuhan metropolitan. Advances in Meteorology. doi:10.1155/2013/293636.
45. Kuang, W., 2011. Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area. Journal of Geographical Sciences, 21 (2), 317–330. doi:10.1007/s11442-011-0847-4.
46. Li, D., et al., 2012a. GPU-CA model for large-scale land-use change simulation. Chinese Science Bulletin, 57 (19), 2442–2452. doi:10.1007/s11434-012-5085-3.
47. Li, L., Sato, Y., and Zhu, H., 2003. Simulating spatial urban expansion based on a physical process. Landscape and Urban Planning, 64 (1–2), 67–76. doi:10.1016/S0169-2046(02)00201-3.
48. Li, W.L., Wu, C.S., and Zang, S.Y., 2014a. Modeling urban land use conversion of Daqing City, China:a comparative analysis of “top-down” and “bottom-up” approaches. Stochastic Environmental Research and Risk Assessment, 28 (4), 817–828. doi:10.1007/s00477-012-0671-0.
49. Li, X., et al., 2011a. Concepts, methodologies, and tools of an integrated geographical simulation and optimization system. International Journal of Geographical Information Science, 25 (4), 633–655. doi:10.1080/13658816.2010.496370.
50. Li, X., et al., 2011b. Coupling urban cellular automata with ant colony optimization for zoning protected natural areas under a changing landscape. International Journal of Geographical Information Science, 25 (4), 575–593. doi:10.1080/13658816.2010.481262.
51. Li, X., et al., 2011c. Coupling simulation and optimization to solve planning problems in a fast-developing area. Annals of the Association of American Geographers, 101 (5), 1032–1048. doi:10.1080/00045608.2011.577366.
52. Li, X., et al., 2012b. 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.
53. Li, X., et al., 2013a. Early warning of illegal development for protected areas by integrating cellular automata with neural networks. Journal of Environmental Management, 130 (30), 106–116. doi:10.1016/j.jenvman.2013.08.055.
54. Li, X., et al., 2013b. 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.
55. Li, X., et al., Forthcoming. A new global land-use and land-cover change product at a 1-km resolution for 2010-2100 based on human-environment interactions. Annals of the American Association of Geographers.
56. Li, X., Liu, X., and Yu, L., 2014b. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science, 28 (7), 1317–1335. doi:10.1080/13658816.2014.883079.
57. Li, X., and Liu, X.P., 2006. An extended cellular automation using case-based reasoning for simulating urban development in a large complex region. International Journal of Geographical Information Science, 20 (10), 1109–1136. doi:10.1080/13658810600816870.
58. Li, X., Yang, Q.S., and Liu, X.P., 2008. Discovering and evaluating urban signatures for simulating compact development using cellular automata. Landscape and Urban Planning, 86 (2), 177–186. doi:10.1016/j.landurbplan.2008.02.005.
59. 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.
60. Li, X., and Yeh, A.G.O., 2001a. Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment and Planning A, 33 (8), 1445–1462. doi:10.1068/a33210.
61. Li, X., and Yeh, A.G.O., 2001b. Zoning land for agricultural protection by the integration of remote sensing, GIS, and cellular automata. Photogrammetric Engineering and Remote Sensing, 67 (4), 471–477.
62. Li, X., and Yeh, A.G.O., 2002. 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.
63. Li, X., and Yeh, A.G.O., 2004. Data mining of cellular automata’s transition rules. International Journal of Geographical Information Science, 18 (8), 723–744. doi:10.1080/13658810410001705325.
64. Li, Y., et al., 2013c. Spatiotemporal simulation and risk analysis of dam-break flooding based on cellular automata. International Journal of Geographical Information Science, 27 (10), 2043–2059. doi:10.1080/13658816.2013.786081.
65. Li, Y., and He, C., 2008. Scenario simulation and forecast of land use/cover in northern China. Chinese Science Bulletin, 53 (9), 1401–1412.
66. Lin, G., and Ho, S.P.S., 2003. China’s land resources and land-use change:insights from the 1996 land survey. Land Use Policy, 20 (2), 87–107. doi:10.1016/S0264-8377(03)00007-3.
67. Lin, J., et al., 2014. Modeling urban vertical growth using cellular automata—Guangzhou as a case study. Applied Geography, 53, 172–186. doi:10.1016/j.apgeog.2014.06.007.
68. Liu, K., et al., 2008a. Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands, 28 (2), 336–346. doi:10.1672/06-91.1.
69. Liu, X., et al., 2008b. 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.
70. Liu, X., et al., 2008c. Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling, 211 (1–2), 169–181. doi:10.1016/j.ecolmodel.2007.08.024.
71. Liu, X., et al., 2010. Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science, 24 (5), 783–802. doi:10.1080/13658810903270551.
72. Liu, X., et al., 2012a. Simulating urban expansion using an improved SLEUTH model. Journal of Applied Remote Sensing, 6 (1), 061709–061709. doi:10.1117/1.JRS.6.061709.
73. Liu, X., et al., 2015a. A normalized urban areas composite index (nuaci) based on combination ofdmsp-ols and modis for mapping impervious surface area. Remote Sensing, 7, (12), 17168–17189. doi:10.3390/rs71215863
74. Liu, X.P., et al., 2007. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China Series D, 50 (10), 1578–1588. doi:10.1007/s11430-007-0083-z.
75. Liu, X.P., et al., 2014. 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
76. Liu, X.S., et al., 2012b. Simulating urban expansion using an improved SLEUTH model. Journal of Applied Remote Sensing, 6 (1), 061709–061709.
77. Liu, Y., et al., 2013. Simulating the conversion of rural settlements to town land based on multi-agent systems and cellular automata. Plos One, 8 (11), e79300. doi:10.1371/journal.pone.0079300.
78. Liu, Y., Dai, L., and Xiong, H., 2015b, Simulation of urban expansion patterns by integrating auto-logistic regression. Markov Chain And Cellular Automata Models. Journal Of Environmental Planning And Management, 58 (6), 1113-1136.
79. Liu, Y., Yang, S., and Chen, J.N., 2012c. Modeling environmental impacts of urban expansion:a systematic method for dealing with uncertainties. Environmental Science & Technology, 46 (15), 8236–8243. doi:10.1021/es300766a.
80. Long, Y., et al., 2013. Urban growth boundaries of the Beijing Metropolitan Area:comparison of simulation and artwork. Cities, 31, 337–348. doi:10.1016/j.cities.2012.10.013.
81. Long, Y., et al., 2014. Reconstruction of historical arable land use patterns using constrained cellular automata:A case study of Jiangsu, China. Applied Geography, 52, 67–77. doi:10.1016/j.apgeog.2014.05.001.
82. Long, Y., Gu, Y., and Han, H., 2012a. Spatiotemporal heterogeneity of urban planning implementation effectiveness:evidence from five urban master plans of Beijing. Landscape and Urban Planning, 108 (2), 103–111. doi:10.1016/j.landurbplan.2012.08.005.
83. Long, Y., Mao, Q., and Dang, A., 2009. Beijing urban development model:urban growth analysis and simulation. Tsinghua Science & Technology, 14 (6), 782–794. doi:10.1016/S1007-0214(09)70149-X.
84. Long, Y., Shen, Z., and Mao, Q., 2012b. Retrieving spatial policy parameters from an alternative plan using constrained cellular automata and regionalized sensitivity analysis. Environment and Planning B, 39 (3), 586. doi:10.1068/b37122.
85. Long, Y., Shen, Z., and Mao, Q., 2012c. An urban growth control planning support system for the Beijing metropolitan area. In:Shen, Z. ed. Advances in Geographic Information Science. Springer, 285–306.
86. Luo, J., and Wei, Y., 2009. Modeling spatial variations of urban growth patterns in Chinese cities:the case of Nanjing. Landscape and Urban Planning, 91 (2), 51–64. doi:10.1016/j.landurbplan.2008.11.010.
87. Mao, X.Y., Meng, J.J., and Xiang, Y.Y., 2013. Cellular automata-based model for developing land use ecological security patterns in semi-arid areas:a case study of Ordos, Inner Mongolia, China. Environmental Earth Sciences, 70 (1), 269–279. doi:10.1007/s12665-012-2125-x.
88. Meng, X.Q., and Chen, Y.J., 2013. The analysis and evaluation of land cover change in Xining City based on CA-Markov model. Applied Mechanics and Materials, 253, 207–210.
89. Nan, L., et al., 2006. A spatial-temporal system for dynamic cadastral management. Journal of Environmental Management, 78 (4), 373–381. doi:10.1016/j.jenvman.2005.05.004.
90. Pan, Y., et al., 2010. The impact of variation in scale on the behavior of a cellular automata used for land use change modeling. Computers Environment and Urban Systems, 34 (5), 400–408. doi:10.1016/j.compenvurbsys.2010.03.003.
91. 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.
92. 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.
93. Seto, K., and Fragkias, M., 2005. Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecology, 20 (7), 871–888. doi:10.1007/s10980-005-5238-8.
94. Seto, K.C., et al., 2002. Monitoring land-use change in the Pearl River Delta using Landsat TM. International Journal of Remote Sensing, 23 (10), 1985–2004. doi:10.1080/01431160110075532.
95. Seto, K.C., et al., 2011. A meta-analysis of global urban land expansion. Plos One, 6 (8), e23777. doi:10.1371/journal.pone.0023777.
96. Seto, K.C., Kaufmann, R.K., and Woodcock, C.E., 2000. Landsat reveals China’s farmland reserves, but they’re vanishing fast. Nature, 406 (6792), 121–121. doi:10.1038/35018267.
97. Smil, V., 1995. Who will feed China. The China Quarterly, 143 (September), 801–813. doi:10.1017/S0305741000015058.
98. Son, W., and Pijanowski, B. C., 2014. The effects of china's cultivated land balance program on potential land productivity at a national scale. Applied Geography, 46, 158–170.
99. Su, L., et al., 2012. Landscape pattern change prediction of Jinhu coastal area based on logistic-CA-Markov model. AISS:Advances in Information Sciences and Service Sciences, 4 (11), 1–10.
100. Sui, D.Z., and Zeng, H., 2001. Modeling the dynamics of landscape structure in Asia’s emerging desakota regions:a case study in Shenzhen. Landscape and Urban Planning, 53 (1), 37–52. doi:10.1016/S0169-2046(00)00136-5.
101. Tang, W.S., and Chung, H., 2002. Rural–urban transition in China:illegal land use and construction. Asia Pacific Viewpoint, 43 (1), 43–62. doi:10.1111/1467-8373.00157.
102. Wang, G., et al., 2006. Quantitative assessment of land degradation factors based on remotely-sensed data and cellular automata:a case study of Beijing and its neighboring areas. Environmental Sciences, 3 (4), 239–253. doi:10.1080/15693430601063463.
103. Wang, G.Q., et al., 2012a. Land use and land cover change of Hulun lake nature reserve in inner Mongolia, China:A modeling analysis. Journal of the Faculty of Agriculture Kyushu University, 57 (1), 219–225.
104. Wang, H., et al., 2011. Development and application of a simulation model for changes in land-use patterns under drought scenarios. Computers & Geosciences, 37 (7), 831–843. doi:10.1016/j.cageo.2010.11.014.
105. Wang, H., et al., 2013. Simulating urban expansion using a cloud-based cellular automata model:A case study of Jiangxia, Wuhan, China. Landscape and Urban Planning, 110, 99–112. doi:10.1016/j.landurbplan.2012.10.016.
106. Wang, L., et al., 2012b. China’s urban expansion from 1990 to 2010 determined with satellite remote sensing. Chinese Science Bulletin, 57 (22), 2802–2812. doi:10.1007/s11434-012-5235-7.
107. Wang, S., Zheng, X., and Zang, X., 2012c. Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238–1245. doi:10.1016/j.proenv.2012.01.117
108. Wang, Y., and Li, S., 2010. Simulating multiple class urban land-use/cover changes by RBFN-based CA model. Computers & Geosciences, 37 (2), 111–121. doi:10.1016/j.cageo.2010.07.006.
109. Wu, D., et al., 2010. Simulating urban expansion by coupling a stochastic cellular automata model and socioeconomic indicators. Stochastic Environmental Research and Risk Assessment, 24 (2), 235–245. doi:10.1007/s00477-009-0313-3.
110. 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.
111. 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.
112. Wu, F., and Webster, C.J., 1998. Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B, 25, 103–126. doi:10.1068/b250103
113. Wu, H., et al., 2012a. Quantifying and analyzing neighborhood configuration characteristics to cellular automata for land use simulation considering data source error. Earth Science Informatics, 5 (2), 77–86. doi:10.1007/s12145-012-0097-8.
114. Wu, X., et al., 2009. Performance evaluation of the SLEUTH model in the Shenyang metropolitan area of Northeastern China. Environmental Modeling and Assessment, 14 (2), 221–230. doi:10.1007/s10666-008-9154-6.
115. Wu, Y., et al. 2012b. Simulation of land use/cover change for Wuhan City based on GIS and CA. Journal of Convergence Information Technology, 7, 1.
116. Xi, F., et al., 2010. Simulating the impacts of ecological protection policies on urban land use sustainability in Shenyang-Fushun, China. International Journal of Urban Sustainable Development, 1 (1–2), 111–127. doi:10.1080/19463130903458326.
117. Xi, F.M., et al., 2012. The potential impacts of sprawl on farmland in Northeast China—evaluating a new strategy for rural development. Landscape and Urban Planning, 104 (1), 34–46.
118. Xie, H., et al., 2012. Simulation of regionally ecological land based on a cellular automation model:A case study of Beijing, China. International Journal of Environmental Research and Public Health, 9 (8), 2986–3001. doi:10.3390/ijerph9082986.
119. Yang, J., et al., 2013. An intelligent method to discover transition rules for cellular automata using bee colony optimisation. International Journal of Geographical Information Science, 27 (10), 1849–1864. doi:10.1080/13658816.2013.823498.
120. Yang, L., and Xue, C., 2009. Study on urban cellar automata model based on geo-objects. Journal of Beijing Institute of Technology (Social Sciences Edition), 1, 19.
121. 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.
122. Yang, W., et al., 2011. Ecological benefits assessment and spatial modeling of urban ecosystem for controlling urban sprawl in Eastern Beijing, China. Ecological Complexity, 8 (2), 153–160. doi:10.1016/j.ecocom.2011.01.004.
123. Yang, X., Zheng, X., and Chen, R., 2014. A land use change model:integrating landscape pattern indexes and Markov-CA. Ecological Modelling, 283, 1–7. doi:10.1016/j.ecolmodel.2014.03.011
124. Yang, X., Zheng, X., and Lv, L., 2012. A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233, 11–19. doi:10.1016/j.ecolmodel.2012.03.011
125. Yeh, A.G.O., and Li, X., 2003. Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning. Photogrammetric Engineering and Remote Sensing, 69 (9), 1043–1052. doi:10.14358/PERS.69.9.1043.
126. Zeng, C., et al., 2014. Urban sprawl and related problems:bibliometric analysis and refined analysis from 1991 to 2011. Chinese Geographical Science, 24 (2), 245–257. doi:10.1007/s11769-013-0619-4.
127. Zhang, Q., et al., 2011. Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Computers, Environment and Urban Systems, 35 (2), 126–139. doi:10.1016/j.compenvurbsys.2010.12.002.
128. Zhang, X., Mount, T.D., and Boisvert, R.N., 2004. Industrialization, urbanization and land use in China. Journal of Chinese Economic and Business Studies, 2 (3), 207–224. doi:10.1080/1476528042000276132.
129. Zhao, P., 2011. Managing urban growth in a transforming China:evidence from Beijing. Land Use Policy, 28 (1), 96–109. doi:10.1016/j.landusepol.2010.05.004.
130. Zhou, D., Lin, Z., and Liu, L., 2012. Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Science of the Total Environment, 439, 260–274. doi:10.1016/j.scitotenv.2012.09.013
131. Zhuang, Y.H., et al., 2013. Simulation of the spatial and temporal changes of complex non-point source loads in a lake watershed of central China. Water Science and Technology, 67 (9), 2050–2058. doi:10.2166/wst.2013.083.