A CA-based land system change model: LANDSCAPE

References

• 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
   Web of Science ®Google Scholar
• Barredo, J.I., et al., 2004. Modelling future urban scenarios in developing countries: an application case study in Lagos, Nigeria. Environment and Planning B: Planning and Design, 31, 65–84. doi:10.1068/b29103
   Web of Science ®Google Scholar
• Batty, M. and Xie, Y., 1994. From cells to cities. Environment and Planning B: Planning and Design, 21 (7), S31–48. doi:10.1068/b21S031
   Google Scholar
• Bellotti, T. and Crook, J., 2009. Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications, 36 (2), 3302–3308. doi:10.1016/j.eswa.2008.01.005
   Web of Science ®Google Scholar
• Britz, W., Verburg, P.H., and Leip, A., 2011. Modelling of land cover and agricultural change in Europe: combining the CLUE and CAPRI-Spat approaches. Agriculture, Ecosystems & Environment, 142 (1–2), 40–50. doi:10.1016/j.agee.2010.03.008
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Doswell, C.A.I., Davies-Jones, R., and Keller, D.L., 1990. On summary measures of skill in rare event forecasting based on contingency tables. Weather and Forecasting, 5, 576–585. doi:10.1175/1520-0434(1990)005<0576:OSMOSI>2.0.CO;2
   Web of Science ®Google Scholar
• Foley, J.A., et al., 2005. Global consequences of land use. Science, 309 (5734), 570–574. doi:10.1126/science.1111772
   PubMed Web of Science ®Google Scholar
• Geertman, S., Hagoort, M., and Ottens, H., 2007. Spatial‐temporal specific neighbourhood rules for cellular automata land‐use modelling. International Journal of Geographical Information Science, 21 (5), 547–568. doi:10.1080/13658810601064892
   Web of Science ®Google Scholar
• He, C., et al., 2013. 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
   Web of Science ®Google Scholar
• Hellmann, F. and Verburg, P.H., 2011. Spatially explicit modelling of biofuel crops in Europe. Biomass and Bioenergy, 35 (6), 2411–2424. doi:10.1016/j.biombioe.2008.09.003
   Web of Science ®Google Scholar
• Hiyoshi, H., et al., 2014. Pedestrian movement model based on voronoi cellular automata. Transportation Research Procedia, 2, 336–343. doi:10.1016/j.trpro.2014.09.027
   Google Scholar
• Huang, B., et al., 2009. Land-use-change modeling using unbalanced support-vector machines. Environment and Planning B: Planning and Design, 36, 398–416. doi:10.1068/b33047
   Web of Science ®Google Scholar
• 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. doi:10.1016/j.compenvurbsys.2009.08.003
   Web of Science ®Google Scholar
• Ke, X., Qi, L., and Zeng, C., 2015. A partitioned and asynchronous cellular automata model for urban growth simulation. International Journal of Geographical Information Science, 30 (4), 637–659. doi:10.1080/13658816.2015.1084510
   Web of Science ®Google Scholar
• Khan, M.A. and Syed, M.N.A., 2015. Image processing techniques for automatic detection of tumor in human brain using SVM. Ijarcce, 4 (4), 541–544. doi:10.17148/IJARCCE.2015.44125
   Google Scholar
• Li, X., et al., 2013. Early warning of illegal development for protected areas by integrating cellular automata with neural networks. Journal of Environmental Management, 130, 106–116. doi:10.1016/j.jenvman.2013.08.055
   PubMed Web of Science ®Google Scholar
• 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.
   Google Scholar
• Li, X., He, J., and Liu, X., 2009. Intelligent GIS for solving high‐dimensional site selection problems using ant colony optimization techniques. International Journal of Geographical Information Science, 23 (4), 399–416. doi:10.1080/13658810801918491
   Web of Science ®Google Scholar
• Liu, J., et al., 2010a. Spatial patterns and driving forces of land use change in China during the early 21st century. Journal of Geographical Sciences, 20 (4), 483–494. doi:10.1007/s11442-010-0483-4
   Web of Science ®Google Scholar
• Liu, X., et al., 2008a. 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
   Web of Science ®Google Scholar
• Liu, X., et al., 2008b. 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
   Web of Science ®Google Scholar
• Liu, X., et al., 2010b. 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
   Web of Science ®Google Scholar
• 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
   Google Scholar
• Liu, X., et al. forthcoming. An integrated model for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning.
   Google Scholar
• Lu, C.-J., Lee, T.-S., and Chiu, -C.-C., 2009. Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47 (2), 115–125. doi:10.1016/j.dss.2009.02.001
   Web of Science ®Google Scholar
• Poelmans, L. and Van Rompaey, A., 2010. Complexity and performance of urban expansion models. Computers, Environment and Urban Systems, 34 (1), 17–27. doi:10.1016/j.compenvurbsys.2009.06.001
   Web of Science ®Google Scholar
• Pontius, R.G., Shusas, E., and Mceachern, M., 2004. Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment, 101 (2–3), 251–268. doi:10.1016/j.agee.2003.09.008
   Web of Science ®Google Scholar
• Renwick, A., et al., 2013. Policy reform and agricultural land abandonment in the EU. Land Use Policy, 30 (1), 446–457. doi:10.1016/j.landusepol.2012.04.005
   Web of Science ®Google Scholar
• Rykiel, E.J., 1996. Testing ecological models: the meaning of validation. Ecological Modelling, 90, 229–244. doi:10.1016/0304-3800(95)00152-2
   Web of Science ®Google Scholar
• Sant, 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
   Web of Science ®Google Scholar
• Schaldach, R., et al., 2011. An integrated approach to modelling land-use change on continental and global scales. Environmental Modelling & Software, 26 (8), 1041–1051. doi:10.1016/j.envsoft.2011.02.013
   Web of Science ®Google Scholar
• Seto, K.C., Guneralp, B., and Hutyra, L.R., 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the United States of America, 109 (40), 16083–16088. doi:10.1073/pnas.1211658109
   PubMed Web of Science ®Google Scholar
• Souty, F., et al., 2012. The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use. Geoscientific Model Development, 5, 1297–1322. doi:10.5194/gmd-5-1297-2012
   Web of Science ®Google Scholar
• Strck, J., Schulp, C.J.E., and Verburg, P.H., 2015. Spatio-temporal dynamics of regulating ecosystem services in Europe – the role of past and future land use change. Applied Geography, 63, 121–135. doi:10.1016/j.apgeog.2015.06.009
   Web of Science ®Google Scholar
• Thapa, R.B. and Murayama, Y., 2011. Urban growth modeling of Kathmandu metropolitan region, Nepal. Computers, Environment and Urban Systems, 35 (1), 25–34. doi:10.1016/j.compenvurbsys.2010.07.005
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• van Vliet, J., et al., 2013. Measuring the neighbourhood effect to calibrate land use models. Computers, Environment and Urban Systems, 41, 55–64. doi:10.1016/j.compenvurbsys.2013.03.006
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Veerbeek, W., et al., 2015. Enhancing the calibration of an urban growth model using a memetic algorithm. Computers, Environment and Urban Systems, 50, 53–65. doi:10.1016/j.compenvurbsys.2014.11.003
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Verburg, P.H., Tabeau, A., and Hatna, E., 2013. Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: a study for land use in Europe. Journal of Environmental Management, 127 (Suppl), S132–144. doi:10.1016/j.jenvman.2012.08.038
   PubMed Web of Science ®Google Scholar
• Visser, H. and de Nijs, T., 2006. The map comparison kit. Environmental Modelling & Software, 21 (3), 346–358. doi:10.1016/j.envsoft.2004.11.013
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• White, R. and Engelen, G., 1997. Cellular automata as the basis of integrated dynamic regional modelling. Environment and Planning B: Planning and Design, 24, 235–246. doi:10.1068/b240235
   Web of Science ®Google Scholar
• Xie, Y., 1996. A generalized model for cellular urban dynamics. Geographical Analysis, 28, 350–373. doi:10.1111/j.1538-4632.1996.tb00940.x
   Web of Science ®Google Scholar
• Zhang, Y., et al., 2015. Self-modifying CA model using dual ensemble Kalman filter for simulating urban land-use changes. International Journal of Geographical Information Science, 29 (9), 1612–1631. doi:10.1080/13658816.2015.1037305
   Web of Science ®Google Scholar