A review of assessment methods for cellular automata models of land-use change and urban growth

References

• Aguilera, F., Valenzuela, L.M., and Botequilha-Leit, O.A., 2011. Landscape metrics in the analysis of urban land use patterns: a case study in a Spanish metropolitan area. Landscape and Urban Planning, 99 (3–4), 226–238. doi:10.1016/j.landurbplan.2010.10.004
   Web of Science ®Google Scholar
• Ahmed, B. and Ahmed, R., 2012. Modeling urban land cover growth dynamics using multi‑temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 1 (1), 3–31. doi:10.3390/ijgi1010003
   Web of Science ®Google Scholar
• Ahmed, B., Ahmed, R., and Zhu, X., 2013. Evaluation of model validation techniques in land cover dynamics. ISPRS International Journal of Geo-Information, 2 (3), 577–597. doi:10.3390/ijgi2030577
   Web of Science ®Google Scholar
• Akaike, H., 2011. Akaike’s information criterion. In: Lovric, Miodrag, eds. International encyclopedia of statistical science. Heidelberg: Springer, 25.
   Google Scholar
• Alaei moghadam, S., Karimi, M., and Habibi, K., 2018. Simulating urban growth in a megalopolitan area using a patch-based cellular automata. Transactions in GIS, 22 (1), 249–268.
   Web of Science ®Google Scholar
• Al-Ageili, M., Mouhoub, M., and Piwowar, J., 2017. Remote sensing, GIS and cellular automata for urban growth simulation. Computer and Information Science, 10 (4), 38.
   Google Scholar
• Al-Ahmadi, K., ALAHMADI, M., and Alahmadi, S., 2016. Spatial optimization of urban cellular automata model. In: Ming Hung Applications of Spatial Statistics, InTech. London
   Google Scholar
• Aldwaik, S.Z., Onsted, J.A., and Pontius, R.G., JR, 2015. Behavior-based aggregation of land categories for temporal change analysis. International Journal of Applied Earth Observation and Geoinformation, 35, 229–238.
   Web of Science ®Google Scholar
• Aljoufie, M., et al. 2013. A cellular automata-based land use and transport interaction model applied to Jeddah, Saudi Arabia. Landscape and Urban Planning, 112, 89–99.
   Web of Science ®Google Scholar
• Al-Shalabi, M., et al., 2013. Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environmental Earth Sciences, 70 (1), 425–437.
   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.
   Web of Science ®Google Scholar
• Anselin, L., 2019. A local indicator of multivariate spatial association: extending Geary’s c. Geographical Analysis, 51 (2), 133–150.
   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 Observations & Geoinformation, 21 (1), 265–275.
   Google Scholar
• Bachmann, A. and Allgöwer, B., 2002. Uncertainty propagation in wildland fire behaviour modelling. International Journal of Geographical Information Science, 16 (2), 115–127.
   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 (1), 65–84.
   Web of Science ®Google Scholar
• Barredo, J.I. and Demicheli, L., 2003. Urban sustainability in developing countries’ megacities: modelling and predicting future urban growth in Lagos. Cities, 20 (5), 297–310.
   Web of Science ®Google Scholar
• Barreira-Gonzalez, P. and Barros, J., 2017. Configuring the neighbourhood effect in irregular cellular automata based models. International Journal of Geographical Information Science, 31 (3), 617–636.
   Web of Science ®Google Scholar
• Barreira-GonzaLez, P., Gomez-Delgado, M., and Aguilera-Benavente, F., 2015. From raster to vector cellular automata models: a new approach to simulate urban growth with the help of graph theory. Computers, Environment and Urban Systems, 54, 119–131.
   Web of Science ®Google Scholar
• Basse, R.M., et al. 2014. Land use changes modelling using advanced methods: cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 53, 160–171.
   Web of Science ®Google Scholar
• Basse, R.M., Charif, O., and Bodis, K., 2016. Spatial and temporal dimensions of land use change in cross border region of Luxembourg. Development of a hybrid approach integrating GIS, cellular automata and decision learning tree models. Applied Geography, 67, 94–108.
   Web of Science ®Google Scholar
• Batisani, N. and Yarnal, B., 2009. Urban expansion in centre county, Pennsylvania: spatial dynamics and landscape transformations. Applied Geography, 29 (2), 235–249.
   Web of Science ®Google Scholar
• Blecic, I., Cecchini, A., and Trunfio, G.A., 2015. How much past to see the future: a computational study in calibrating urban cellular automata. International Journal of Geographical Information Science, 29 (3), 349–374.
   Web of Science ®Google Scholar
• Bozkaya, A.G., et al., 2015. Forecasting land-cover growth using remotely sensed data: a case study of the Igneada protection area in Turkey. Environmental Monitoring and Assessment, 187 (3), 59.
   PubMed Web of Science ®Google Scholar
• Bradley, A.V., et al. 2016. SimiVal, a multi-criteria map comparison tool for land-change model projections. Environmental Modelling & Software, 82, 229–240.
   Web of Science ®Google Scholar
• Brown, D.G., et al., 2005. Path dependence and the validation of agent‐based spatial models of land use. International Journal of Geographical Information Science, 19 (2), 153–174.
   Web of Science ®Google Scholar
• Brown, D.G. and Xie, Y., 2006. Spatial agent‐based modelling. International Journal of Geographical Information Science, 20 (9), 941–943.
   Web of Science ®Google Scholar
• Cao, M., et al., 2016. A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion. International Journal of Geographical Information Science, 30 (10), 1961–1979.
   Web of Science ®Google Scholar
• Carter, J.G., 2018. Urban climate change adaptation: exploring the implications of future land cover scenarios. Cities, 77, 73–80.
   Web of Science ®Google Scholar
• Charif, O., et al., 2017. A multi‐label cellular automata model for land change simulation. Transactions in GIS, 21 (6), 1298–1320.
   Web of Science ®Google Scholar
• Chaudhuri, G. and Clarke, K.C., 2013. How does land use policy modify urban growth? A case study of the Italo-Slovenian border. Journal of Land Use Science, 8 (4), 443–465.
   Google Scholar
• Chaudhuri, G. and Clarke, K.C., 2014. Temporal accuracy in urban growth forecasting: a study using the SLEUTH model. Transactions in GIS, 18 (2), 302–320.
   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.
   Web of Science ®Google Scholar
• Chowdhury, P.R. and Maithani, S., 2014. Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata. International Journal of Applied Earth Observation and Geoinformation, 33, 155–165.
   Web of Science ®Google Scholar
• Clarke, K.C., et al. 2018. Land use change modeling with SLEUTH: improving calibration with a genetic algorithm. In: M.T. Camacho Olmedo, ed. Geomatic approaches for modeling land change scenarios. Cham: Springer International Publishing, 139–161.
   Google Scholar
• Congalton, R.G. and Green, K., 2008. Assessing the accuracy of remotely sensed data: principles and practices.  Boca Raton: CRC press.
   Google Scholar
• Costanza, R., 1989. Model goodness of fit: A multiple resolution procedure. Ecological Modelling, 47 (3), 199–215.
   Web of Science ®Google Scholar
• Crols, T., et al., High-resolution simulations of population-density change with an activity-based cellular automata land-use model. ed. Proceedings of GeoComputation, London 2017, 2017.
   Google Scholar
• Dahal, K.R. and Chow, T.E., 2014. An agent-integrated irregular automata model of urban land-use dynamics. International Journal of Geographical Information Science, 28 (11), 2281–2303.
   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.
   Web of Science ®Google Scholar
• De 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.
   Google Scholar
• De Noronha Vaz, E. and Nijkamp, P., 2015. Gravitational forces in the spatial impacts of urban sprawl. Habitat International, 45 (2), 99–105.
   Google Scholar
• Dezhkam, S., et al., 2017. Performance evaluation of land change simulation models using landscape metrics. Geocarto International, 32 (6), 655–677.
   Web of Science ®Google Scholar
• Dietzel, C. and Clarke, K.C., 2004. Spatial differences in multi‐resolution urban automata modeling. Transactions in GIS, 8 (4), 479–492.
   Google Scholar
• Dietzel, C. and Clarke, K.C., 2007. Toward optimal calibration of the SLEUTH land use change model. Transactions in GIS, 11 (1), 29–45.
   Google Scholar
• Du, G., et al., 2018. A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of greater Tokyo Area. International Journal of Geographical Information Science, 32 (4), 757–782.
   Web of Science ®Google Scholar
• Fan, C. and Myint, S., 2014. A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landscape and Urban Planning, 121, 117–128.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Feng, Y., et al., 2016. Simulation of dynamic urban growth with partial least squares regression-based cellular automata in a GIS environment. ISPRS International Journal of Geo-Information, 5 (12), 243.
   Web of Science ®Google Scholar
• Feng, Y., 2017. Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules. International Journal of Geographical Information Science, 31 (6), 1198–1219.
   Web of Science ®Google Scholar
• Feng, Y., et al., 2018a. The effect of observation scale on urban growth simulation using particle swarm optimization-based CA models. Sustainability, 10 (11), 4002.
   Web of Science ®Google Scholar
• Feng, Y., et al., 2018b. Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models. Geocarto International, 33 (5), 470–488.
   Web of Science ®Google Scholar
• Feng, Y., et al. 2019. How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth? Computers, Environment and Urban Systems, 76, 150–162.
   Web of Science ®Google Scholar
• Feng, Y. and Tong, X., 2017. Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change. Environmental Monitoring and Assessment, 189 (10), 515.
   PubMed Web of Science ®Google Scholar
• Feng, Y. and Tong, X., 2019. A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods. International Journal of Geographical Information Science, 1–24. https://doi.org/10.1080/13658816.2019.1648813
   Google Scholar
• Foody, G.M., 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80 (1), 185–201.
   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.
   Web of Science ®Google Scholar
• Getis, A., 2010. Spatial autocorrelation. In: M.M. Fischer and A. GETIS, eds. Handbook of applied spatial analysis: software tools, methods and applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 255–278.
   Google Scholar
• Getis, A. and Aldstadt, J., 2010. Constructing the spatial weights matrix using a local statistic. In: Luc Anselin; Sergio J. Rey, Perspectives on spatial data analysis. New York: Springer, 147–163.
   Google Scholar
• Gharbia, S.S., et al., 2016. Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms. Modeling Earth Systems and Environment, 2 (3), 151.
   Web of Science ®Google Scholar
• Goodarzi, M.S., Sakieh, Y., and Navardi, S., 2017. Scenario-based urban growth allocation in a rapidly developing area: a modeling approach for sustainability analysis of an urban-coastal coupled system. Environment, Development and Sustainability, 19 (3), 1103–1126.
   Web of Science ®Google Scholar
• Gounaridis, D., Chorianopoulos, I., and Koukoulas, S., 2018. Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Applied Geography, 90, 134–144.
   Web of Science ®Google Scholar
• Grekousis, G., Manetos, P., and Photis, Y.N., 2013. Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities, 30 (1), 193–203.
   Google Scholar
• Grinblat, Y., Gilichinsky, M., and Benenson, I., 2016. Cellular automata modeling of land-use/land-cover dynamics: questioning the reliability of data sources and classification methods. Annals of the American Association of Geographers, 106 (6), 1299–1320.
   Web of Science ®Google Scholar
• Guan, Q., et al., 2016. A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures. International Journal of Geographical Information Science, 30 (3), 494–514.
   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.
   Google Scholar
• Gwet, K.L., 2014. Handbook of inter-rater reliability: the definitive guide to measuring the extent of agreement among raters. Piedmont: Advanced Analytics, LLC.
   Google Scholar
• Hagen-Zanker, A., Multi-method assessment of map similarity. ed. Proceedings of the fifth AGILE conference on geographic information science, 2002, Palma, Spain, 171–182.
   Google Scholar
• Hagen-Zanker, A., 2003. Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science, 17 (3), 235–249.
   Web of Science ®Google Scholar
• Hagen-Zanker, A., 2006. Map comparison methods that simultaneously address overlap and structure. Journal of Geographical Systems, 8 (2), 165–185.
   Web of Science ®Google Scholar
• Hagen-Zanker, A., 2009. An improved fuzzy Kappa statistic that accounts for spatial autocorrelation. International Journal of Geographical Information Science, 23 (1), 61–73.
   Web of Science ®Google Scholar
• Hagen-Zanker, A., 2016. A computational framework for generalized moving windows and its application to landscape pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 44, 205–216.
   Web of Science ®Google Scholar
• Hagen-Zanker, A. and Lajoie, G., 2008. Neutral models of landscape change as benchmarks in the assessment of model performance. Landscape and Urban Planning, 86 (3), 284–296.
   Web of Science ®Google Scholar
• Hagen-Zanker, A. and Martens, P., 2008. Map comparison methods for comprehensive assessment of geosimulation models. In: O. Gervasi, et al., ed.. Computational Science and Its Applications: Berlin, Heidelberg. 194–209.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Herold, M., Couclelis, H., and Clarke, K.C., 2005. The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems, 29 (4), 369–399.
   Google Scholar
• Hewitt, R. and Diaz-Pacheco, J., 2017. Stable models for metastable systems? Lessons from sensitivity analysis of a cellular automata urban land use model. Computers, Environment and Urban Systems, 62, 113–124.
   Web of Science ®Google Scholar
• Hyandye, C. and Martz, L.W., 2017. A Markovian and cellular automata land-use change predictive model of the Usangu catchment. International Journal of Remote Sensing, 38 (1), 64–81.
   Web of Science ®Google Scholar
• Jafari, M., et al., 2016. Dynamic simulation of urban expansion based on cellular automata and logistic regression model: case study of the Hyrcanian region of Iran. Sustainability, 8 (8), 810.
   Web of Science ®Google Scholar
• Jantz, C.A. and Goetz, S.J., 2005. Analysis of scale dependencies in an urban land‐use‐change model. International Journal of Geographical Information Science, 19 (2), 217–241.
   Web of Science ®Google Scholar
• Jantz, C.A., Goetz, S.J., and Shelley, M.K., 2004. 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 (2), 251–271.
   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.
   Web of Science ®Google Scholar
• Ke, X., Qi, L., and Zeng, C., 2016. A partitioned and asynchronous cellular automata model for urban growth simulation. International Journal of Geographical Information Science, 30 (4), 637–659.
   Web of Science ®Google Scholar
• Kocabas, V. and Dragicevic, S., 2006. Assessing cellular automata model behaviour using a sensitivity analysis approach. Computers, Environment and Urban Systems, 30 (6), 921–953.
   Web of Science ®Google Scholar
• Kocabas, V. and Dragicevic, S., 2007. Enhancing a GIS cellular automata model of land use change: Bayesian networks, influence diagrams and causality. Transactions in GIS, 11 (5), 681–702.
   Google Scholar
• Ku, C.-A., 2016. Incorporating spatial regression model into cellular automata for simulating land use change. Applied Geography, 69, 1–9.
   Web of Science ®Google Scholar
• Lauf, S., et al. 2012. Uncovering land-use dynamics driven by human decision-making–A combined model approach using cellular automata and system dynamics. Environmental Modelling & Software, 27, 71–82.
   Web of Science ®Google Scholar
• Li, H., et al., 2018. Simulating urban cooperative expansion in a single-core metropolitan region based on improved CA model integrated information flow: case study of Wuhan urban agglomeration in China. Journal of Urban Planning and Development, 144 (2), 05018002.
   Web of Science ®Google Scholar
• Li, X., et al., 2012. Assimilating process context information of cellular automata into change detection for monitoring land use changes. International Journal of Geographical Information Science, 26 (9), 1667–1687.
   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.
   Web of Science ®Google Scholar
• Li, X., Yang, Q., and Liu, X., 2008. Discovering and evaluating urban signatures for simulating compact development using cellular automata. Landscape and Urban Planning, 86 (2), 177–186.
   Web of Science ®Google Scholar
• Liao, J., et al., 2014. A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence. International Journal of Geographical Information Science, 28 (4), 720–738.
   Web of Science ®Google Scholar
• Lin, Y.-P., 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.
   Web of Science ®Google Scholar
• Liu, D., et al. 2018. Interoperable scenario simulation of land-use policy for Beijing–Tianjin–Hebei region, China. Land Use Policy, 75, 155–165.
   Web of Science ®Google Scholar
• Liu, X., et al., 2008. Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling, 211 (1–2), 169–181.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Liu, X., et al. 2017. 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.
   Web of Science ®Google Scholar
• Liu, Y. and Feng, Y., 2012. A logistic based cellular automata model for continuous urban growth simulation: a case study of the Gold coast city, Australia. In:  Alison J. Heppenstall, Andrew T. Crooks, Linda M. See and Michael Batty, Agent-based models of geographical systems. Springer, 643–662.
   Google Scholar
• Liu, Y. and Feng, Y., 2016. Simulating the impact of economic and environmental strategies on future urban growth scenarios in Ningbo, China. Sustainability, 8 (10), 1045.
   Web of Science ®Google Scholar
• Mantelas, L., et al., 2012. Using fuzzy cellular automata to access and simulate urban growth. GeoJournal, 77 (1), 13–28.
   Google Scholar
• Mcgarigal, K. 2014. Fragstats v4: spatial pattern analysis program for categorical and continuous maps-help manual. Amherst: University of Massachusetts. Available fr http://www.umass.edu/landeco/research/fragstats/fragstats.html
   Google Scholar
• McGarigal, K., et al. 2018. Modeling non-stationary urban growth: the SPRAWL model and the ecological impacts of development. Landscape and Urban Planning, 177, 178–190.
   Web of Science ®Google Scholar
• Meentemeyer, R.K., et al., 2013. FUTURES: multilevel simulations of emerging urban–rural landscape structure using a stochastic patch-growing algorithm. Annals of the Association of American Geographers, 103 (4), 785–807.
   Web of Science ®Google Scholar
• Ménard, A. and Marceau, D.J., 2007. Simulating the impact of forest management scenarios in an agricultural landscape of southern Quebec, Canada, using a geographic cellular automata. Landscape and Urban Planning, 79 (3), 253–265.
   Web of Science ®Google Scholar
• Mitsova, D., Shuster, W., and Wang, X., 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99 (2), 141–153.
   Web of Science ®Google Scholar
• Mondal, B., Das, D.N., and Bhatta, B., 2017. Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration. Geocarto International, 32 (4), 401–419.
   Web of Science ®Google Scholar
• Moreno, N., Ménard, A., and Marceau, D.J., 2008. VecGCA: a vector-based geographic cellular automata model allowing geometric transformations of objects. Environment and Planning B: Planning and Design, 35 (4), 647–665.
   Web of Science ®Google Scholar
• Moreno, N., Wang, F., and Marceau, D.J., 2009. Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model. Computers, Environment and Urban Systems, 33 (1), 44–54.
   Web of Science ®Google Scholar
• Mundia, C.N. and Murayama, Y., 2010. Modeling spatial processes of urban growth in African cities: a case study of Nairobi city. Urban Geography, 31 (2), 259–272.
   Web of Science ®Google Scholar
• Musa, S.I., Hashim, M., and Reba, M.N.M., 2017. A review of geospatial-based urban growth models and modelling initiatives. Geocarto International, 32 (8), 813–833.
   Web of Science ®Google Scholar
• Mustafa, A., et al., 2018a. Comparing support vector machines with logistic regression for calibrating cellular automata land use change models. European Journal of Remote Sensing, 51 (1), 391–401.
   Web of Science ®Google Scholar
• Mustafa, A., et al., 2018b. A time Monte Carlo method for addressing uncertainty in land-use change models. International Journal of Geographical Information Science, 32 (11), 2317–2333.
   Web of Science ®Google Scholar
• Myint, S.W. and Wang, L., 2006. Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 32 (6), 390–404.
   Web of Science ®Google Scholar
• Newland, C.P., et al. 2018a. Multi-objective optimisation framework for calibration of cellular automata land-use models. Environmental Modelling & Software, 100, 175–200.
   Web of Science ®Google Scholar
• Newland, C.P., et al. 2018b. Empirically derived method and software for semi-automatic calibration of cellular automata land-use models. Environmental Modelling & Software, 108, 208–239.
   Web of Science ®Google Scholar
• Omrani, H., Tayyebi, A., and Pijanowski, B., 2017. Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based land transformation model: an integrated ML-CA-LTM modeling framework. GIScience & Remote Sensing, 54 (3), 283–304.
   Web of Science ®Google Scholar
• Onsted, J. and Clarke, K.C., 2012. The inclusion of differentially assessed lands in urban growth model calibration: a comparison of two approaches using SLEUTH. International Journal of Geographical Information Science, 26 (5), 881–898.
   Web of Science ®Google Scholar
• Oreskes, N., 1998. Evaluation (not validation) of quantitative models. Environmental Health Perspectives, 106 (suppl 6), 1453–1460.
   PubMed Web of Science ®Google Scholar
• Oreskes, N., Shrader-Frechette, K., and Belitz, K., 1994. Verification, validation, and confirmation of numerical models in the earth sciences. Science, 263 (5147), 641–646.
   PubMed Web of Science ®Google Scholar
• Padmanaban, R., et al., 2017. Modelling urban sprawl using remotely sensed data: a case study of Chennai city, Tamilnadu. Entropy, 19 (4), 163.
   Web of Science ®Google Scholar
• Peng, J., et al., 2010. Evaluating the effectiveness of landscape metrics in quantifying spatial patterns. Ecological Indicators, 10 (2), 217–223.
   Web of Science ®Google Scholar
• Pérez-Molina, E., et al. 2017. Developing a cellular automata model of urban growth to inform spatial policy for flood mitigation: a case study in Kampala, Uganda. Computers, Environment and Urban Systems, 65, 53–65.
   Web of Science ®Google Scholar
• Petrov, L.O., Lavalle, C., and Kasanko, M., 2009. Urban land use scenarios for a tourist region in Europe: applying the Moland model to Algarve, Portugal. Landscape and Urban Planning, 92 (1), 10–23.
   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.
   Google Scholar
• Pinto, N., Antunes, A.P., and Roca, J., 2017. Applicability and calibration of an irregular cellular automata model for land use change. Computers, Environment and Urban Systems, 65, 93–102.
   Web of Science ®Google Scholar
• Poelmans, L. and 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 (1), 10–19.
   Web of Science ®Google Scholar
• Pontius, R.G., 2000. Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing, 66 (8), 1011–1016.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Pontius, R.G. and Malanson, J., 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19 (2), 243–265.
   Web of Science ®Google Scholar
• Pontius, R.G. and Malizia, N.R., 2004. Effect of category aggregation on map comparison. In: M.J. Egenhofer, C. Freksa, and H.J. Miller, eds. Geographic Information Science. Berlin, Heidelberg: Geographic Information Science, 251–268.
   Google Scholar
• Pontius, R.G. and Millones, M., 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32 (15), 4407–4429.
   Web of Science ®Google Scholar
• Pontius, R.G., Peethambaram, S., and Castella, J.-C., 2011. Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don district, Vietnam. Annals of the Association of American Geographers, 101 (1), 45–62.
   Web of Science ®Google Scholar
• Pontius, R.G. and Si, K., 2014. The total operating characteristic to measure diagnostic ability for multiple thresholds. International Journal of Geographical Information Science, 28 (3), 570–583.
   Web of Science ®Google Scholar
• Puertas, O.L., Henriquez, 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.
   Web of Science ®Google Scholar
• Qiao, W., et al., 2017. Evaluation of intensive urban land use based on an artificial neural network model: a case study of Nanjing city, China. Chinese Geographical Science, 27 (5), 735–746.
   Web of Science ®Google Scholar
• Rabbani, A., Aghababaee, H., and Rajabi, M.A., 2012. Modeling dynamic urban growth using hybrid cellular automata and particle swarm optimization. Journal of Applied Remote Sensing, 6 (1), 063582.
   Google Scholar
• Rienow, A. and 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.
   Web of Science ®Google Scholar
• Rimal, B., et al., 2018. Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and Markov Chain. ISPRS International Journal of Geo-Information, 7 (4), 154.
   Web of Science ®Google Scholar
• Rykiel, E.J., 1996. Testing ecological models: the meaning of validation. Ecological Modelling, 90 (3), 229–244.
   Web of Science ®Google Scholar
• Sakieh, Y., et al., 2015a. Scenario-based evaluation of urban development sustainability: an integrative modeling approach to compromise between urbanization suitability index and landscape pattern. Environment, Development and Sustainability, 17 (6), 1343–1365.
   Web of Science ®Google Scholar
• Sakieh, Y., et al., 2015b. Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj city, Iran. Journal of Housing and the Built Environment, 30 (4), 591–611.
   Web of Science ®Google Scholar
• Sakieh, Y. and Salmanmahiny, A., 2016. Performance assessment of geospatial simulation models of land-use change—a landscape metric-based approach. Environmental Monitoring and Assessment, 188 (3), 169.
   PubMed Web of Science ®Google Scholar
• Shafizadeh-Moghadam, H., et al. 2017. Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Computers, Environment and Urban Systems, 64, 297–308.
   Web of Science ®Google Scholar
• Soares-Filho, B., Rodrigues, H., and Follador, M., 2013. A hybrid analytical-heuristic method for calibrating land-use change models. Environmental Modelling & Software, 43, 80–87.
   Web of Science ®Google Scholar
• Soares-Filho, B.S., Cerqueira, G.C., and Pennachin, C.L., 2002. DINAMICA—a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling, 154 (3), 217–235.
   Web of Science ®Google Scholar
• Stevens, D. and Dragićević, S., 2007. A GIS-based irregular cellular automata model of land-use change. Environment and Planning B: Planning and Design, 34 (4), 708–724.
   Web of Science ®Google Scholar
• Stevens, D., Dragicevic, S., and Rothley, K., 2007. iCity: A GIS–CA modelling tool for urban planning and decision making. Environmental Modelling & Software, 22 (6), 761–773.
   Web of Science ®Google Scholar
• Straatman, B., White, R., and Engelen, G., 2004. Towards an automatic calibration procedure for constrained cellular automata. Computers, Environment and Urban Systems, 28 (1), 149–170.
   Google Scholar
• Tayyebi, A., Perry, P.C., and Tayyebi, A.H., 2014a. Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS. International Journal of Geographical Information Science, 28 (4), 639–659.
   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, 102–116.
   Web of Science ®Google Scholar
• Tayyebi, A.H., Tayyebi, A., and Khanna, N., 2014b. Assessing uncertainty dimensions in land-use change models: using swap and multiplicative error models for injecting attribute and positional errors in spatial data. International Journal of Remote Sensing, 35 (1), 149–170.
   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.
   Web of Science ®Google Scholar
• Thapa, R.B. and Murayama, Y., 2012. Scenario based urban growth allocation in Kathmandu valley, Nepal. Landscape and Urban Planning, 105 (1–2), 140–148.
   Web of Science ®Google Scholar
• Van Vliet, J., et al. 2013a. A fuzzy set approach to assess the predictive accuracy of land use simulations. Ecological Modelling, 261–262, 32–42.
   Web of Science ®Google Scholar
• Van Vliet, J., et al. 2013b. Measuring the neighbourhood effect to calibrate land use models. Computers, Environment and Urban Systems, 41, 55–64.
   Web of Science ®Google Scholar
• Van Vliet, J., et al. 2016. A review of current calibration and validation practices in land-change modeling. Environmental Modelling & Software, 82, 174–182.
   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.
   Web of Science ®Google Scholar
• Van Vliet, J., White, R., and Dragicevic, S., 2009. Modeling urban growth using a variable grid cellular automaton. Computers, Environment and Urban Systems, 33 (1), 35–43.
   Web of Science ®Google Scholar
• Verburg, P., et al., 1999. A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecological Modelling, 116 (1), 45–61.
   Web of Science ®Google Scholar
• Verburg, P.H., et al., 2002. Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management, 30 (3), 391–405.
   PubMed 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.
   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.
   Web of Science ®Google Scholar
• Verburg, P.H. and Veldkamp, A., 2001. The role of spatially explicit models in land-use change research: a case study for cropping patterns in China. Agriculture, Ecosystems & Environment, 85 (1–3), 177–190.
   Web of Science ®Google Scholar
• Vermeiren, K., et al., 2016. ASSURE: a model for the simulation of urban expansion and intra-urban social segregation. International Journal of Geographical Information Science, 30 (12), 2377–2400.
   Web of Science ®Google Scholar
• Waddell, P., 2002. UrbanSim: modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68 (3), 297–314.
   Web of Science ®Google Scholar
• Wang, F. and Marceau, D.J., 2013. A patch‐based cellular automaton for simulating land‐use changes at fine spatial resolution. Transactions in GIS, 17 (6), 828–846.
   Web of Science ®Google Scholar
• Wang, H., et al. 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.
   Web of Science ®Google Scholar
• Wang, R., Derdouri, A., and Murayama, Y., 2018. Spatiotemporal simulation of future land use/cover change scenarios in the Tokyo metropolitan area. Sustainability, 10 (6), 2056.
   Web of Science ®Google Scholar
• Wang, R. and Murayama, Y., 2017. Change of land use/cover in Tianjin city based on the Markov and cellular automata models. ISPRS International Journal of Geo-Information, 6 (5), 150.
   Web of Science ®Google Scholar
• Wang, Y. and Li, S., 2011. Simulating multiple class urban land-use/cover changes by RBFN-based CA model. Computers & Geosciences, 37 (2), 111–121.
   Web of Science ®Google Scholar
• Whitsed, R. and Smallbone, L.T., 2017. A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model. International Journal of Geographical Information Science, 31 (4), 717–737.
   Web of Science ®Google Scholar
• Wickramasuriya, R.C., et al., 2009. The dynamics of shifting cultivation captured in an extended constrained cellular automata land use model. Ecological Modelling, 220 (18), 2302–2309.
   Web of Science ®Google Scholar
• Williams, B., Petrov, L., and Ustaoglu, E., 2017. Scenario analysis of alternative land development patterns for the Leipzig-Halle region: implications for transport-land-use sustainability. Urban Planning, 2 (1), 108–129.
   Web of Science ®Google Scholar
• Wu, F., 2002. Calibration of stochastic cellular automata: the application to rural-urban land conversions. International Journal of Geographical Information Science, 16 (8), 795–818.
   Web of Science ®Google Scholar
• Wu, H., et al., 2012. Quantifying and analyzing neighborhood configuration characteristics to cellular automata for land use simulation considering data source error. Earth Science Informatics, 5 (2), 77–86.
   Web of Science ®Google Scholar
• Wu, H., et al., 2019. Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. International Journal of Geographical Information Science, 33 (5), 1040–1061.
   Web of Science ®Google Scholar
• Xia, C., et al., 2018. A high-performance cellular automata model for urban simulation based on vectorization and parallel computing technology. International Journal of Geographical Information Science, 32 (2), 399–424.
   Web of Science ®Google Scholar
• Xia, C., et al., 2019. Modeling urban growth in a metropolitan area based on bidirectional flows, an improved gravitational field model, and partitioned cellular automata. International Journal of Geographical Information Science, 33 (5), 877–899.
   Web of Science ®Google Scholar
• Xie, W., et al. 2018. Projecting the impacts of urban expansion on simultaneous losses of ecosystem services: A case study in Beijing, China. Ecological Indicators, 84, 183–193.
   Web of Science ®Google Scholar
• Xu, H. and Brown, D.G., 2017. Sensitivity of a stochastic land-cover change model to pixel versus polygonal land units. International Journal of Geographical Information Science, 31 (4), 738–762.
   Web of Science ®Google Scholar
• Yang, X., Zheng, X.-Q., and Lv, L.-N., 2012. A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233, 11–19.
   Web of Science ®Google Scholar
• Yeh, A.G.-O. and Li, X., 2006. Errors and uncertainties in urban cellular automata. Computers, Environment and Urban Systems, 30 (1), 10–28.
   Web of Science ®Google Scholar
• Yin, H., et al. 2018. Exploring zoning scenario impacts upon urban growth simulations using a dynamic spatial model. Cities, 81, 214–229.
   Web of Science ®Google Scholar
• Zhang, Q., et al., 2011. Simulation and analysis of urban growth scenarios for the greater Shanghai area, China. Computers, Environment and Urban Systems, 35 (2), 126–139.
   Web of Science ®Google Scholar
• Zhou, D., Lin, Z., and Liu, L., 2012. Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Science of the Total Environment, 439, 260–274.
   PubMed Web of Science ®Google Scholar