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Research Article

Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model

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Pages 2294-2316 | Received 21 Dec 2017, Accepted 16 Jul 2018, Published online: 03 Aug 2018

References

  • Al-Ahmadi, K., et al. 2009. A Fuzzy Cellular Automata Urban Growth Model (FCAUGM) for the City of Riyadh, Saudi Arabia. Part 2: scenario testing. Applied Spatial Analysis and Policy, 2 (2), 85–105. doi:10.1007/s12061-008-9019-z
  • Alcamo, J., et al. 2011. Evaluation of an integrated land use change model including a scenario analysis of land use change for continental Africa. Environmental Modelling & Software, 26 (8), 1017–1027. doi:10.1016/j.envsoft.2011.03.002
  • Arsanjani, J.J., Helbich, M., and de Noronha Vaz, E., 2013. Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran. Cities, 32, 33–42. doi:10.1016/j.cities.2013.01.005
  • Arsanjani, J.J., Kainz, W., and Mousivand, A.J., 2011. Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. International Journal of Image & Data Fusion, 2 (4), 329–345. doi:10.1080/19479832.2011.605397
  • 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., 2008. The size, scale, and shape of cities. Science, 319 (5864), 769–771. doi:10.1126/science.1151419
  • Batty, M., Xie, Y., and Sun, Z., 1999. Modeling urban dynamics through GIS-based cellular automata. Computers Environment & Urban Systems, 23 (3), 205–233. doi:10.1016/S0198-9715(99)00015-0
  • Chen, Y., et al. 2013. simulating urban form and energy consumption in the pearl river delta under different development strategies. Annals of the American Association of Geographers, 103 (6), 1567–1585. doi:10.1080/00045608.2012.740360
  • 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
  • Chen, Y., et al., 2016. Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata. Landscape & Urban Planning, 152, 59–71. doi:10.1016/j.landurbplan.2016.03.011
  • 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
  • Dai, E., et al. 2005. Modeling change-pattern-value dynamics on land use: an integrated GIS and artificial neural networks approach. Environmental Management, 36 (4), 576–591. doi:10.1007/s00267-004-0165-z
  • Dai, G., Salet, W., and Vries, D.J., 2013. Why high-speed railway stations continue China’s leapfrog urbanization: institutional parameters of urban development. China City Planning Review, 22 (1), 49–59.
  • 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
  • Gong, P. and Chen, J., 2002. Assessment of the urban development plan of Beijing by using a CA-based urban growth model. Photogrammetric Engineering & Remote Sensing, 68 (10), 1063–1072.
  • Gu, C., Xiaohui, Y., and Jing, G., 2017. China’s master planning system in transition case study on Beijing. Paper presented at the 46th ISOCARP Congress.
  • Guy, E., Roger, W., and Inge, U., 1997. Integrating constrained cellular automata models, GIS and decision support tools for urban planning and policy making. London: E&FN Spon, 125–155.
  • 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
  • He, J., et al., 2018. Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. International Journal of Geographical Information Science, 32 (10), 2076–2097.
  • He, J., Huang, J., and Li, C., 2017. The evaluation for the impact of land use change on habitat quality: a joint contribution of cellular automata scenario simulation and habitat quality assessment model. Ecological Modelling, 366, 58–67. doi:10.1016/j.ecolmodel.2017.10.001
  • Huang, J., et al., 2018. An ex-post evaluation approach to assess the impacts of accomplished urban structure shift on landscape connectivity. Science of the Total Environment, 622-623, 1143–1152. doi:10.1016/j.scitotenv.2017.12.094
  • Huang, Q., 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
  • Huang, Z., He, C., and Zhu, S., 2017. Do China’s economic development zones improve land use efficiency? The effects of selection, factor accumulation and agglomeration. Landscape and Urban Planning, 162, 145–156. doi:10.1016/j.landurbplan.2017.02.008
  • Jjumba, A. and Dragićević, S., 2012. High resolution urban land-use change modeling: agent iCity approach. Applied Spatial Analysis and Policy, 5 (4), 291–315. doi:10.1007/s12061-011-9071-y
  • Kamusoko, C. and Gamba, J., 2015. Simulating urban growth Using a Random Forest-Cellular Automata (RF-CA) model. Isprs International Journal of Geo-Information, 4 (2), 447–470. doi:10.3390/ijgi4020447
  • Ke, X., et al. 2017. A CA-based land system change model: LANDSCAPE. International Journal of Geographical Information Science, 31 (9), 1798–1817. doi:10.1080/13658816.2017.1315536
  • Li, S., et al. 2013. Simulation of spatial population dynamics based on labor economics and multi-agent systems: a case study on a rapidly developing manufacturing metropolis. International Journal of Geographical Information Science, 27 (12), 2410–2435. doi:10.1080/13658816.2013.826360
  • Li, X., et al. 2017. A new global land-use and land-cover change product at a 1-km resolution for 2010 to 2100 based on human–environment interactions. Annals of the American Association of Geographers, 107 (5), 1040–1059. doi:10.1080/24694452.2017.1303357
  • Li, X. and Liu, X., 2006. An extended cellular automaton 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
  • Li, X., Liu, X., and Yu, L., 2014. 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
  • Li, X. and Yeh, A.G., 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
  • Li, X. and Yeh, A.G., 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
  • Li, X. and Yeh, G.O., 2001. Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment & Planning A, 33 (8), 1445–1462. doi:10.1068/a33210
  • Liang, X., et al., 2018. Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method. Landscape and Urban Planning, 177, 47–63. doi:10.1016/j.landurbplan.2018.04.016
  • Lin, Y., et al. 2011. Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling - a case study. International Journal of Geographical Information Science, 25 (1), 65–87. doi:10.1080/13658811003752332
  • 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, 1247–1269. doi:10.1080/13658810701757510
  • 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
  • Liu, X., 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
  • Liu, X., et al., 2017a. Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics. International Journal of Geographical Information Science, 32 (1), 73–101.
  • Liu, X., et al., 2017b. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94–116. doi:10.1016/j.landurbplan.2017.09.019
  • Liu, Y. and Liu, X., 2008. Applying SLEUTH for simulating urban expansion of Hangzhou. Journal of Natural Resources, 7471 (5), 797–807.
  • 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
  • Lu, C., et al., 2013. Driving force of urban growth and regional planning: a case study of China’s Guangdong Province. Habitat International, 40, 35–41. doi:10.1016/j.habitatint.2013.01.006
  • Pekel, J., et al. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540 (7633), 418–422. doi:10.1038/nature20584
  • Pijanowski, B.C., et al. 2005. Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19 (2), 197–215. doi:10.1080/13658810410001713416
  • Pontius, R.G., et al. 2008. Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science, 42 (1), 11–37. doi:10.1007/s00168-007-0138-2
  • Pontius, R.G., Huffaker, D., and Denman, K., 2004. Useful techniques of validation for spatially explicit land-change models. Ecological Modelling, 179 (4), 445–461. doi:10.1016/j.ecolmodel.2004.05.010
  • Sohl, T. and Sayler, K., 2008. Using the FORE-SCE model to project land-cover change in the southeastern United States. Ecological Modelling, 219 (1–2), 49–65. doi:10.1016/j.ecolmodel.2008.08.003
  • Sun, H., 2016. Study on the correlation between the hierarchical urban system and high-speed railway network planning in China. Frontiers of Architectural Research, 5 (3), 301–318. doi:10.1016/j.foar.2016.04.003
  • Tan, R., et al., 2015. A game-theory based agent-cellular model for use in urban growth simulation: a case study of the rapidly urbanizing Wuhan area of central China. Computers, Environment and Urban Systems, 49, 15–29. doi:10.1016/j.compenvurbsys.2014.09.001
  • Tayyebi, A., Pijanowski, B.C., and Tayyebi, A.H., 2011. An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Landscape and Urban Planning, 100 (1–2), 35–44. doi:10.1016/j.landurbplan.2010.10.007
  • Tian, L. and Shen, T., 2011. Evaluation of plan implementation in the transitional China: a case of Guangzhou city master plan. Cities, 28 (1), 11–27. doi:10.1016/j.cities.2010.07.002
  • van Asselen, S. and Verburg, P.H., 2013. Land cover change or land-use intensification: simulating land system change with a global-scale land change model. Global Change Biology, 19 (12), 3648–3667. doi:10.1111/gcb.12331
  • Verburg, P.H. and Overmars, K.P., 2009. Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 24 (9), 1167–1181. doi:10.1007/s10980-009-9355-7
  • Wang, Y.X., et al., 2016. Simulation of land use dynamic change using selected driving factors based on the method of feature selection. In International Conference on Materials Engineering, Manufacturing Technology and Control.
  • Wang, Z., et al., 2017. Spatiotemporal variability of reference evapotranspiration and contributing climatic factors in China during 1961–2013. Journal of Hydrology, 544, 97–108. doi:10.1016/j.jhydrol.2016.11.021
  • White, R. and Engelen, G., 2000. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers Environment & Urban Systems, 24 (5), 383–400. doi:10.1016/S0198-9715(00)00012-0
  • Yang, Q., Li, X., and Shi, X., 2006. Cellular automata for simulating land use changes based on support vector machines. Journal of Remote Sensing, 34 (6), 592–602.
  • Yang, X., Zheng, X.Q., and Chen, R., 2014. A land use change model: integrating landscape pattern indexes and Markov-CA. Ecological Modelling, 283 (7), 1–7. doi:10.1016/j.ecolmodel.2014.03.011
  • Yao, Y., et al. 2017a. Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata. International Journal of Geographical Information Science, 31 (12), 2452–2479. doi:10.1080/13658816.2017.1360494
  • Yao, Y., et al. 2017b. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science, 31 (4), 825–848. doi:10.1080/13658816.2016.1244608
  • Yeh, A.G. and Li, X., 1999a. Economic development and agricultural land loss in the pearl river delta, China. Habitat International, 23 (3), 373–390. doi:10.1016/S0197-3975(99)00013-2
  • Yeh, A.G. and Li, X.Y., 1998. Sustainable land development model for rapid growth areas using GIS. International Journal of Geographical Information Science, 12 (2), 169–189. doi:10.1080/136588198241941
  • Yeh, G.O. and Li, X., 1999b. Economic development, urban sprawl, and agricultural land loss in the pearl river delta,China. Economic Geography, 19 (1), 67–72.
  • Yu, Le., Wang, J., and Gong, P., 2013. Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach. International Journal for Remote Sensing, 34 (16), 5851–5867. doi:10.1080/01431161.2013.798055
  • Zhang, X., et al., 2015. Proximate control stream assisted video transcoding for heterogeneous content delivery network. In IEEE International Conference on Image Processing, 2552–2555.

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