A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area

References

• Abadi, M., et al., 2016. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv Preprint arXiv, 1603.04467.
   Google Scholar
• Aburas, M.M., et al., 2016. The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: a review. International Journal of Applied Earth Observation and Geoinformation, 52, 380–389. doi:10.1016/j.jag.2016.07.007
   Web of Science ®Google Scholar
• Almeida, C.M., et al., 2003. Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation. Computers, Environment and Urban Systems, 27 (5), 481–509. doi:10.1016/S0198-9715(02)00042-X
   Google Scholar
• Almeida, C.M., et al., 2008. Using neural networks and cellular automata for modelling intraurban landuse dynamics. International Journal of Geographical Information Science, 22 (9), 943–963. doi:10.1080/13658810701731168
   Web of Science ®Google Scholar
• Al-Sharif, A.A. and Pradhan, B., 2015. A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS. Geocarto International, 30 (8), 858–881. doi:10.1080/10106049.2014.997308
   Web of Science ®Google Scholar
• 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 (1), 265–275. doi:10.1016/j.jag.2011.12.014
   Web of Science ®Google Scholar
• Ben-Gal, I., et al., 2014. Efficient construction of decision trees by the dual information distance method. Quality Technology & Quantitative Management, 11 (1), 133–147. doi:10.1080/16843703.2014.11673330
   Web of Science ®Google Scholar
• Bennett, N.D., et al., 2013. Characterising performance of environmental models. Environmental Modelling and Software, 40, 1–20. doi:10.1016/j.envsoft.2012.09.011
   Web of Science ®Google Scholar
• Berberoğlu, S., Akn, A., and Clarke, K.C., 2016. Cellular automata modeling approaches to forecast urban growth for Adana, Turkey: a comparative approach. Landscape and Urban Planning, 153, 11–27. doi:10.1016/j.landurbplan.2016.04.017
   Web of Science ®Google Scholar
• Breiman, L., et al., 1984. Classification and regression trees. London, UK: Chapman & Hall/CRC press.
   Google Scholar
• Breiman, L., 1996. Bagging predictors. Machine Learning, 24 (2), 123–140. doi:10.1007/BF00058655
   Web of Science ®Google Scholar
• Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32. doi:10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Camacho Olmedo, M.T., Paegelow, M., and Mas, J.F., 2013. Interest in intermediate soft-classified maps in land change model validation: suitability versus transition potential. International Journal of Geographical Information Science, 27 (12), 2343–2361. doi:10.1080/13658816.2013.831867
   Web of Science ®Google Scholar
• Caruana, R. and Niculescu-Mizil, A., 2006. An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on Machine learning, New York, NY: ACM, 161–168.
   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
• 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 and Urban Planning, 152, 59–71. doi:10.1016/j.landurbplan.2016.03.011
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Costanza, R., 1989. Model goodness of fit: a multiple resolution procedure. Ecological Modelling, 47 (3–4), 199–215. doi:10.1016/0304-3800(89)90001-X
   Web of Science ®Google Scholar
• Davis, J. and Goadrich, M., 2006. The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd international conference on Machine learning, New York, NY: ACM, 233–240.
   Google Scholar
• Deadman, P., Brown, R.D., and Gimblett, H.R., 1993. Modelling rural residential settlement patterns with cellular automata. Journal of Environmental Management, 37 (2), 147–160. doi:10.1006/jema.1993.1011
   Web of Science ®Google Scholar
• Friedman, J.H., 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38 (4), 367–378. doi:10.1016/S0167-9473(01)00065-2
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Geurts, P., Ernst, D., and Wehenkel, L., 2006. Extremely randomized trees. Machine Learning, 63 (1), 3–42. doi:10.1007/s10994-006-6226-1
   Web of Science ®Google Scholar
• Gong, Z., Thill, J.C., and Liu, W., 2015. ART-P-MAP neural networks modeling of land-use change: accounting for spatial heterogeneity and uncertainty. Geographical Analysis, 47 (4), 376–409. doi:10.1111/gean.12077
   Web of Science ®Google Scholar
• Guan, Q., Wang, L., and Clarke, K.C., 2005. An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science, 32 (4), 369–380. doi:10.1559/152304005775194746
   Google Scholar
• Hagen, A., 2003. Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science, 17 (3), 235–249. doi:10.1080/13658810210157822
   Web of Science ®Google Scholar
• Hagoort, M., Geertman, S., and Ottens, H., 2008. Spatial externalities, neighbourhood rules and CA land-use modelling. The Annals of Regional Science, 42 (1), 39–56. doi:10.1007/s00168-007-0140-8
   Web of Science ®Google Scholar
• Hirzel, A. and Guisan, A., 2002. Which is the optimal sampling strategy for habitat suitability modelling. Ecological Modelling, 157 (2), 331–341. doi:10.1016/S0304-3800(02)00203-X
   Web of Science ®Google Scholar
• Huang, B., Zhang, L., and Wu, B., 2009. Spatiotemporal analysis of rural–urban land conversion. International Journal of Geographical Information Science, 23 (3), 379–398. doi:10.1080/13658810802119685
   Web of Science ®Google Scholar
• Irwin, E.G. and Geoghegan, J., 2001. Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85 (1), 7–24. doi:10.1016/S0167-8809(01)00200-6
   Web of Science ®Google Scholar
• Jansen, L.J. and Veldkamp, T., 2012. Evaluation of the variation in semantic contents of class sets on modelling dynamics of land-use changes. International Journal of Geographical Information Science, 26 (4), 717–746. doi:10.1080/13658816.2011.609989
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Landis, J.R. and Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. doi:10.2307/2529310
   PubMed Web of Science ®Google Scholar
• Li, X., Liu, X., and Gong, P., 2015. Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model. International Journal of Geographical Information Science, 29 (5), 762–785. doi:10.1080/13658816.2014.997237
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Li, X. and Yeh, A.G., 2001. 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Li, X. and Yeh, A.G., 2004. Data mining of cellular automata’s transition rules. International Journal of Geographical Information Science, 18 (8), 723–744. doi:10.1080/13658810410001705325
   Web of Science ®Google Scholar
• Liao, J., et al., 2016. Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations. Environmental Modelling and Software, 75, 163–175. doi:10.1016/j.envsoft.2015.10.014
   Web of Science ®Google Scholar
• Matthews, R.B., et al., 2007. Agent-based land-use models: a review of applications. Landscape Ecology, 22 (10), 1447–1459. doi:10.1007/s10980-007-9135-1
   Web of Science ®Google Scholar
• Mozumder, C., Tripathi, N.K., and Losiri, C., 2016. Comparing three transition potential models: a case study of built-up transitions in North-East India. Computers, Environment and Urban Systems, 59, 38–49. doi:10.1016/j.compenvurbsys.2016.04.009
   Web of Science ®Google Scholar
• Munroe, D.K., Southworth, J., and Tucker, C.M., 2004. Modeling spatially and temporally complex land-cover change: the case of Western Honduras. The Professional Geographer, 56 (4), 544–559.
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Murphy, B., 2014. PyKrige: development of a kriging toolkit for Python. In: AGU fall meeting abstracts, San Francisco, CA: AGU press.
   Google Scholar
• Opitz, D.W. and Maclin, R., 1999. Popular ensemble methods: an empirical study. Journal Artificial Intelligent Researcher (JAIR), 11, 169–198.
   Google Scholar
• Pedregosa, F., et al., 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12 (Oct), 2825–2830.
   Google Scholar
• Pérez-Vega, A., Mas, J.F., and Ligmann-Zielinska, A., 2012. Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling and Software, 29 (1), 11–23. doi:10.1016/j.envsoft.2011.09.011
   Web of Science ®Google Scholar
• Pijanowski, B.C., et al., 2002. Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, Environment and Urban Systems, 26 (6), 553–575. doi:10.1016/S0198-9715(01)00015-1
   Google Scholar
• Pontius, R.G. and Schneider, L.C., 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85 (1), 239–248. doi:10.1016/S0167-8809(01)00187-6
   Web of Science ®Google Scholar
• Puertas, O.L., Henrquez, C., and Meza, F.J., 2014. Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, 2010–2045. Land Use Policy, 38, 415–425. doi:10.1016/j.landusepol.2013.11.024
   Web of Science ®Google Scholar
• Quinlan, J.R., et al., 1996. Bagging, boosting, and C4. 5. In: Proceedings of the thirteen national conference on artificial intelligence. Cambridge, Massachusetts: AAAI press / MIT press.
   Google Scholar
• Rey, S.J. and Anselin, L., 2010. PySAL: A Python library of spatial analytical methods. Handbook of Applied Spatial Analysis, Berlin: Springer, 175–193.
   Google Scholar
• Rossetti, L.A., Sergio dos Anjos, F.P., and De Almeida, C.M., 2013. Cellular automata-based spatial dynamic modeling for analyzing urban land use change. In: Urban Remote Sensing Event (JURSE), Sao Paulo, Brazil, 2013 Joint. New York: IEEE, 1–4.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Soares-Filho, B.S., Coutinho Cerqueira, G., and Lopes Pennachin, C., 2002. DINAMICA - A stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling, 154 (3), 217–235. doi:10.1016/S0304-3800(02)00059-5
   Web of Science ®Google Scholar
• Tayyebi, A. and Pijanowski, B.C., 2014. Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. International Journal of Applied Earth Observation and Geoinformation, 28 (1), 102–116. doi:10.1016/j.jag.2013.11.008
   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
• Verburg, P.H., et al., 2004. A method to analyse neighbourhood characteristics of land use patterns. Computers, Environment and Urban Systems, 28 (6), 667–690. doi:10.1016/j.compenvurbsys.2003.07.001
   Google Scholar
• Wang, J. and Mountrakis, G., 2011. Developing a multi-network urbanization model: a case study of urban growth in Denver, Colorado. International Journal of Geographical Information Science, 25 (2), 229–253. doi:10.1080/13658810903473213
   Web of Science ®Google Scholar
• White, R., Uljee, I., and Engelen, G., 2012. Integrated modelling of population, employment and land-use change with a multiple activity-based variable grid cellular automaton. International Journal of Geographical Information Science, 26 (7), 1251–1280. doi:10.1080/13658816.2011.635146
   Web of Science ®Google Scholar
• Xie, C., et al., 2005. Spatial logistic regression and GIS to model rural-urban land conversion. In: Proceedings of PROCESSUS second international colloquium on the behavioural foundations of integrated land-use and transportation models: frameworks, models and applications, Toronto: University of Toronto, 12–15.
   Google Scholar
• Xu, X., Du, Z., and Zhang, H., 2016. Integrating the system dynamic and cellular automata models to predict land use and land cover change. International Journal of Applied Earth Observation and Geoinformation, 52, 568–579. doi:10.1016/j.jag.2016.07.022
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Zheng, H.W., et al., 2015. Simulating land use change in urban renewal areas: a case study in Hong Kong. Habitat International, 46, 23–34. doi:10.1016/j.habitatint.2014.10.008
   Web of Science ®Google Scholar