Modeling urban regions: Comparing random forest and support vector machines for cellular automata

This is an open access article under the terms of the Creative Commons AttributionNonCommercialNoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is noncommercial and no modifications or adaptations are made. © 2021 The Authors. Transactions in GIS published by John Wiley & Sons Ltd. 1Institute of Geography, RuhrUniversity Bochum, Bochum, Germany 2Urban Systems Lab, The New School, New York, NY, USA

[1]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[2]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[3]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[4]  Min Chen,et al.  A grey wolf optimizer-cellular automata integrated model for urban growth simulation and optimization , 2019, Trans. GIS.

[5]  Lisa J. Graumlich,et al.  Global Land-Cover Change: Recent Progress, Remaining Challenges , 2006 .

[6]  Carlo Lavalle,et al.  Towards an Urban Atlas: Assessment of Spatial Data on 25 European Cities and Urban Areas , 2002 .

[7]  Kyoumars Habibi,et al.  Simulating urban growth in a megalopolitan area using a patch‐based cellular automata , 2018, Trans. GIS.

[8]  Xiaohua Tong,et al.  Calibration of cellular automata models using differential evolution to simulate present and future land use , 2018, Trans. GIS.

[9]  R. Pontius,et al.  Modeling Land-Use and Land-Cover Change , 2006 .

[10]  D. Ludlow Urban Sprawl in Europe - The Ignored Challenge , 2006 .

[11]  Timothy A. Warner,et al.  Implementation of machine-learning classification in remote sensing: an applied review , 2018 .

[12]  Andreas Rienow,et al.  Supporting SLEUTH - Enhancing a cellular automaton with support vector machines for urban growth modeling , 2015, Comput. Environ. Urban Syst..

[13]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[14]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[15]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[16]  Suzana Dragicevic,et al.  Modeling Urban Land Use Changes Using Support Vector Machines , 2016, Trans. GIS.

[17]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[18]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[19]  Michael Batty,et al.  Cities and complexity - understanding cities with cellular automata, agent-based models, and fractals , 2007 .

[20]  O. Okwuashi,et al.  Stochastic GIS cellular automata for land use change simulation : application of a kernel based model , 2009 .

[21]  Ehsan Momeni,et al.  Pattern‐based calibration of cellular automata by genetic algorithm and Shannon relative entropy , 2020, Trans. GIS.

[22]  P. Torrens,et al.  Geosimulation: Automata-based modeling of urban phenomena , 2004 .

[23]  Vladimir Naumovich Vapni The Nature of Statistical Learning Theory , 1995 .

[24]  Eric C. Grunsky,et al.  Predictive lithological mapping of Canada's North using Random Forest classification applied to geophysical and geochemical data , 2015, Comput. Geosci..

[25]  Andrew Zisserman,et al.  Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[26]  Elisabete A. Silva Cellular Automata and Agent Base Models for Urban Studies: From Pixels to Cells to Hexa‐dpi's , 2011 .

[27]  I. Thomas,et al.  The morphology of built-up landscapes in Wallonia (Belgium): A classification using fractal indices , 2008 .

[28]  Mario Cools,et al.  Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm , 2018, Comput. Environ. Urban Syst..

[29]  Elisabete A. Silva,et al.  Complexity, emergence and cellular urban models: lessons learned from applying SLEUTH to two Portuguese metropolitan areas , 2005 .

[30]  Xia Li,et al.  Knowledge Transfer for Large‐Scale Urban Growth Modeling Based on Formal Concept Analysis , 2016, Trans. GIS.

[31]  J. Teller,et al.  Self-Reinforcing Processes Governing Urban Sprawl in Belgium: Evidence over Six Decades , 2020 .

[32]  Xia Li,et al.  Cellular automata for simulating land use changes based on support vector machines , 2008, Comput. Geosci..

[33]  O. Edenhofer,et al.  Renewable Energy Sources and Climate Change Mitigation , 2011 .

[34]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[35]  Onisimo Mutanga,et al.  Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers , 2014 .

[36]  Mohammad Taleai,et al.  Assessing the effect of temporal dynamics on urban growth simulation: Towards an asynchronous cellular automata , 2019, Trans. GIS.

[37]  Fernando De la Torre,et al.  Optimal feature selection for support vector machines , 2010, Pattern Recognit..

[38]  Helen Couclelis,et al.  Macrostructure and Microbehavior in a Metropolitan Area , 1989 .

[39]  Elisabete A. Silva,et al.  The DNA of our regions: artificial intelligence in regional planning , 2004 .

[40]  I. Benenson,et al.  Geographic Automata : From Paradigm to Software and Back to Paradigm , 2005 .

[41]  Jon Atli Benediktsson,et al.  Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[42]  K. Seto,et al.  Modeling Land-Use and LandCover Change 1 MODELING LAND-USE AND LANDCOVER CHANGE , 2004 .

[43]  John E. Abraham,et al.  Microsimulating urban systems , 2004, Comput. Environ. Urban Syst..

[44]  Mohammad Karimi Firozjaei,et al.  An urban growth simulation model based on integration of local weights and decision risk values , 2020, Trans. GIS.

[45]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[46]  R. Gil Pontius,et al.  Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA , 2001 .

[47]  Chang Xia,et al.  Analyzing the effects of stochastic perturbation and fuzzy distance transformation on Wuhan urban growth simulation , 2020, Trans. GIS.

[48]  Abbas Alimohammadi,et al.  Land cover mapping based on random forest classification of multitemporal spectral and thermal images , 2015, Environmental Monitoring and Assessment.

[49]  Elisabete A. Silva,et al.  Artificial Intelligence Solutions for Urban Land Dynamics: A Review , 2010 .

[50]  J. Neumann The General and Logical Theory of Au-tomata , 1963 .

[51]  Mario Cools,et al.  Comparing support vector machines with logistic regression for calibrating cellular automata land use change models , 2018 .

[52]  Lakshmi N. Kantakumar,et al.  Spatiotemporal urban expansion in Pune metropolis, India using remote sensing , 2016 .

[53]  Richard Tay,et al.  Support vector machines for urban growth modeling , 2010, GeoInformatica.

[54]  Millenium Ecosystem Assessment Ecosystems and human well-being: synthesis , 2005 .

[55]  Jonah Gamba,et al.  Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model , 2015, ISPRS Int. J. Geo Inf..

[56]  Shuting Zhai,et al.  A comparison of proximity and accessibility drivers in simulating dynamic urban growth , 2020, Trans. GIS.

[57]  Abbas Alimohammadi,et al.  Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models , 2017, Trans. GIS.

[58]  Fahed Abdallah,et al.  A multi‐label cellular automata model for land change simulation , 2017, Trans. GIS.

[59]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..