4,070
Views
66
CrossRef citations to date
0
Altmetric
Original Articles

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 391-401 | Received 02 Apr 2017, Accepted 14 Feb 2018, Published online: 06 Mar 2018

References

  • Aburas, M.M., Ho, Y.M., Ramli, M.F., & Ash’aari, Z.H. (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.
  • Achmad, A., Hasyim, S., Dahlan, B., & Aulia, D.N. (2015). Modeling of urban growth in tsunami-prone city using logistic regression: Analysis of Banda Aceh, Indonesia. Applied Geography, 62, 237–246.
  • Aldrich, J.H., & Nelson, F.D. (1984). Quantitative Applications in the Social Sciences: Linear probability, logit, and probit models. Thousand Oaks, CA: SAGE Publications Ltd doi: 10.4135/9781412984744.
  • Bandemer, H., & Gottwald, S. (1995). Fuzzy sets, fuzzy logic, fuzzy methods. Chichester: Wiley.
  • Batty, M., Xie, Y., & Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23, 205–233.
  • Belgian Federal Government. 2013. Statistics Belgium [WWW Document]. Stat. Belg. URL. Retrieved from http://statbel.fgov.be/fr/statistiques/chiffres/ (Accessed April 29.14).
  • Ben-Hur, A., & Weston, J. (2010). A user’s guide to support vector machines, in: Data mining techniques for the life sciences, methods in molecular biology. Humana Pressure, 223–239. doi:10.1007/978-1-60327-241-4_13
  • Berberoğlu, S., Akın, A., & 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.
  • Boser, B.E., Guyon, I.M., & Vapnik, V.N., 1992. A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT ’92, ACM, New York, NY, USA, pp. 144–152. doi:10.1145/130385.130401
  • Burges, C.J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121–167.
  • Chang, -C.-C., & Lin, C.-J. (2001). LIBSVM: A library for support vector machines. National Taiwan University, Taipei, Taiwan.
  • Chen, Y., Li, X., Liu, X., & Ai, B. (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, 234–255.
  • Cheng, J., & Masser, I. (2003). Urban growth pattern modeling: A case study of Wuhan city, PR China. Landscape and Urban Planning, 62, 199–217.
  • Clark, W.A.V., & Hosking, P.L. (1986). Statistical methods for geographers (1 ed.). New York: Wiley.
  • Clarke, K.C., & 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, 699–714.
  • EEA. (2011). Landscape fragmentation in Europe (Publication). European Environment Agency, Copenhagen.
  • Feng, Y., Liu, Y., Tong, X., Liu, M., & Deng, S. (2011). Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102, 188–196.
  • García, A.M., Santé, I., Crecente, R., & Miranda, D. (2011). An analysis of the effect of the stochastic component of urban cellular automata models. Computers, Environment and Urban Systems, 35, 289–296.
  • Hagen, A. (2003). Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science, 17, 235–249.
  • Hallowell, G.D., & Baran, P.K. (2013). Suburban change: A time series approach to measuring form and spatial configuration. Journal Space Syntax, 4, 74–91.
  • Hsu, C., Chang, C., & Lin, C. (2010). A practical guide to support vector classification. National Taiwan University, Taipei, Taiwan
  • Hu, Z., & Lo, C.P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31, 667–688.
  • Huang, B., Xie, C., & Tay, R. (2010). Support vector machines for urban growth modeling. GeoInformatica, 14, 83–99.
  • Hughes, G. (1968). On the mean accuracy of statistical pattern recognizers. IEEE Transactions Information Theory, 14, 55–63.
  • Iannone, G., & Troisi, A. (2013). Ca-pri, a cellular automata phenomenological research investigation: Simulation results. International Journal of Modern Physics C, 24, 1350027.
  • Iannone, G., Troisi, A., Guarnaccia, C., D’agostino, P.P., & Quartieri, J. (2011). An urban growth model based on a cellular automata phenomenological framework. International Journal of Modern Physics C, 22, 543–561.
  • Jantz, C.A., Goetz, S.J., & Shelley, M.K. (2003). Using the Sleuth urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B: Planning and Design, 31, 251–271.
  • Jokar Arsanjani, J., Helbich, M., Kainz, W., & Darvishi Boloorani, A. (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.
  • Kryvobokov, M., Mercier, A., Bonnafous, A., & Bouf, D. (2015). Urban simulation with alternative road pricing scenarios. Case Studies Transp Policy, 3, 196–205.
  • Liu, X., Li, X., Shi, X., Wu, S., & Liu, T. (2008). Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling, 211, 169–181.
  • Martens, D., Baesens, B., Van, G., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183, 1466–1476.
  • Montgomery, D.C., & Runger, G.C. (2003). Applied statistics and probability for engineers (4th ed.). New York: John Wiley & Sons.
  • Munshi, T., Zuidgeest, M., Brussel, M., & van Maarseveen, M. (2014). Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India. Cities, 39, 68–86.
  • Mustafa, A., Cools, M., Saadi, I., & Teller, J. (2017). Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM). Land Use Policy, 69C, 529–540. https://doi.org/10.1016/j.landusepol.2017.10.009
  • Mustafa, A., Heppenstall, A., Omrani, H., Saadi, I., Cools, M., & Teller, J. (2018a). Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm. Computers, Environment and Urban Systems, 67, 147–156. https://doi.org/10.1016/j.compenvurbsys.2017.09.009
  • Mustafa, A., Rompaey, A.V., Cools, M., Saadi, I., & Teller, J. (2018b). Addressing the determinants of built-up expansion and densification processes at the regional scale. Urban Studies 1–20. Edinburgh, Scotland., doi:10.1177/0042098017749176
  • Mustafa, A., Saadi, I., Cools, M., & Teller, J., 2014. Measuring the effect of stochastic perturbation component in cellular automata urban growth model. Procedia Environmental Sciences, 12th International Conference on Design and Decision Support Systems in Architecture and Urban Planning, DDSS 2014 22, 156–168. https://doi.org/10.1016/j.proenv.2014.11.016
  • Mustafa, A., Saadi, I., Cools, M., & Teller, J. (2018c). Understanding urban development types and drivers in Wallonia. A multi-density approach. International Journal of Business Intelligence and Data Mining, 13, 309–330. https://doi.org/10.1504/IJBIDM.2017.10004788
  • Nguyen, M.H., & De La Torre, F. (2010). Optimal feature selection for support vector machines. Pattern Recognition, 43, 584–591.
  • Pace, R.K., & LeSage, J.P. (2002). Semiparametric maximum likelihood estimates of spatial dependence. Geographical Analysis, 34, 76–90.
  • Platt, J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 10, 61–74.
  • Poelmans, L., & Van Rompaey, A. (2009). Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flanders–Brussels region. Landscape and Urban Planning, 93, 10–19.
  • Poelmans, L., & Van Rompaey, A. (2010). Complexity and performance of urban expansion models. Computers, Environment and Urban Systems, 34, 17–27.
  • Power, C., Simms, A., & White, R. (2001). Hierarchical fuzzy pattern matching for the regional comparison of land use maps. International Journal of Geographical Information Science, 15, 77–100.
  • Puertas, O.L., Henríquez, C., & 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.
  • Raczko, E., & Zagajewski, B. (2017). Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. European Journal of Remote Sensing, 50, 144–154.
  • Rienow, A., & Goetzke, R. (2015). Supporting SLEUTH – Enhancing a cellular automaton with support vector machines for urban growth modeling. Computers, Environment and Urban Systems, 49, 66–81.
  • Santé, I., García, A.M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96, 108–122.
  • Schölkopf, B., Burges, C., & Vapnik, V., 1996. Incorporating invariances in support vector learning machines, in: Artificial Neural Networks — ICANN 96. Presented at the International Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, pp. 47–52. doi:10.1007/3-540-61510-5_12
  • Ševčíková, H., Raftery, A.E., & Waddell, P.A. (2007). Assessing uncertainty in urban simulations using Bayesian melding. Transportation Research Part B: Methodological, 41, 652–669.
  • SPW. (2013). Schéma de Développement de l’Espace Régional-Une vision pour le territoire wallon (Regional space development plan – A vision for Wallonia).Service Public de Wallonie. Namur, Belgium.
  • Tannier, C., & Thomas, I. (2013). Defining and characterizing urban boundaries: A fractal analysis of theoretical cities and Belgian cities. Computers, Environment and Urban Systems, 41, 234–248.
  • Thomas, I., Frankhauser, P., & Biernacki, C. (2008). The morphology of built-up landscapes in Wallonia (Belgium): A classification using fractal indices. Landscape and Urban Planning, 84, 99–115.
  • Troisi, A. (2015). Can CA describe collective effects of polluting agents? International Journal of Modern Physics C, 26, 1550114.
  • Van Gestel, T., Suykens, J.A., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., … Vandewalle, J. (2001). Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12, 809–821.
  • Vapnik, V.N. (1995). The nature of statistical learning theory. New York, NY, USA: Springer-Verlag New York, Inc..
  • Vapnik, V.N., & Vapnik, V. (1998). Statistical learning theory. New York: Wiley.
  • Verburg, P.H., van Eck, J.R.R., de Nijs, T.C.M., Dijst, M.J., & Schot, P. (2004). Determinants of land-use change patterns in the Netherlands. Environment and Planning B: Planning and Design, 31, 125–150.
  • Vermeiren, K., Van Rompaey, A., Loopmans, M., Serwajja, E., & Mukwaya, P. (2012). Urban growth of Kampala, Uganda: Pattern analysis and scenario development. Landscape and Urban Planning, 106, 199–206.
  • Verplancke, T., Van Looy, S., Benoit, D., Vansteelandt, S., Depuydt, P., De Turck, F., & Decruyenaere, J. (2008). Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Medica Informatics Decisions Mak, 8, 56.
  • Vogel, R. 2013. Entwicklung eines automatisierten Wolkendetektions- und Wolkenklassifizierungsverfahrens mit Hilfe von Support Vector Machines angewendet auf METEOSAT-SEVIRI-Daten für den Raum Deutschland ( Text.PhDThesis).
  • Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68, 297–314.
  • Wang, H., He, S., Liu, X., Dai, L., Pan, P., Hong, S., & Zhang, W. (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.
  • Wang, J., & Maduako, I.N. (2018). Spatio-temporal urban growth dynamics of Lagos metropolitan region of Nigeria based on hybrid methods for LULC modeling and prediction. European Journal of Remote Sensing, 51, 251–265.
  • Waske, B., Linden, S.V.D., Benediktsson, J.A., Rabe, A., & Hostert, P. (2010). Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 48, 2880–2889.
  • White, R., & 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: Economy and Space, 25, 1175–1199.
  • Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, 16, 795–818.
  • Xie, C. (2006). Support vector machines for land use change modeling. University of Calgary, Calgary, Canada.
  • Yang, Q., Li, X., & Shi, X. (2008). Cellular automata for simulating land use changes based on support vector machines. Computers & Geosciences, 34, 592–602.
  • Zhang, H., Zeng, Y., Bian, L., & Yu, X. (2010). Modelling urban expansion using a multi agent-based model in the city of Changsha. Journal of Geographical Sciences, 20, 540–556.