{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:40:05Z","timestamp":1773740405635,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T00:00:00Z","timestamp":1537833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771414"],"award-info":[{"award-number":["41771414"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005\u20132015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today\u2019s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas.<\/jats:p>","DOI":"10.3390\/ijgi7100387","type":"journal-article","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T11:12:26Z","timestamp":1537873946000},"page":"387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Urban Growth Modeling and Future Scenario Projection Using Cellular Automata (CA) Models and the R Package Optimx"],"prefix":"10.3390","volume":"7","author":[{"given":"Yongjiu","family":"Feng","sequence":"first","affiliation":[{"name":"College of Marine Sciences &amp; National Distant-Water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China"},{"name":"School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia"}]},{"given":"Zongbo","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Marine Sciences &amp; National Distant-Water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Xiaohua","family":"Tong","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Jiafeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Marine Sciences &amp; National Distant-Water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Chen","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Marine Sciences &amp; National Distant-Water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Shurui","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Marine Sciences &amp; National Distant-Water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Zhenkun","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Marine Sciences &amp; National Distant-Water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0034-4257(03)00075-0","article-title":"The spatiotemporal form of urban growth: Measurement, analysis and modeling","volume":"86","author":"Herold","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rimal, B., Zhang, L., Keshtkar, H., Haack, B.N., Rijal, S., and Zhang, P. (2018). Land use\/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and markov chain. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040154"},{"key":"ref_3","first-page":"265","article-title":"Integration of logistic regression, markov chain and cellular automata models to simulate urban expansion","volume":"21","author":"Arsanjani","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.landurbplan.2010.03.001","article-title":"Cellular automata models for the simulation of real-world urban processes: A review and analysis","volume":"96","author":"Miranda","year":"2010","journal-title":"Landsc. Urban Plan."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Feng, Y. (2016). Simulating the impact of economic and environmental strategies on future urban growth scenarios in Ningbo, China. Sustainability, 8.","DOI":"10.3390\/su8101045"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.gloplacha.2018.05.007","article-title":"Projection of land surface temperature considering the effects of future land change in the Taihu Lake Basin of China","volume":"167","author":"Feng","year":"2018","journal-title":"Glob. Planet. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/136588100240886","article-title":"Modelling sustainable urban development by the integration of constrained cellular automata and GIS","volume":"14","author":"Li","year":"2000","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Toffoli, T., and Margolus, N. (1987). Cellular Automata Machines: A New Environment for Modeling, MIT Press.","DOI":"10.7551\/mitpress\/1763.001.0001"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/136588198242012","article-title":"Simland: A prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules","volume":"12","author":"Wu","year":"1998","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Feng, Y., Liu, M., Chen, L., and Liu, Y. (2016). Simulation of dynamic urban growth with partial least squares regression-based cellular automata in a GIS environment. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5120243"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, Y., He, J., Tang, W., and Li, C. (2018). Modeling urban collaborative growth dynamics using a multiscale simulation model for the Wuhan urban agglomeration area, China. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7050176"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1080\/13658816.2011.635594","article-title":"A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas","volume":"26","author":"Liu","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1068\/b2802ed","article-title":"Cellular automata and urban simulation: Where do we go from here?","volume":"28","author":"Torrens","year":"2001","journal-title":"Environ. Plan. B"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1080\/15481603.2018.1426262","article-title":"Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules","volume":"55","author":"Feng","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/S0198-9715(02)00042-X","article-title":"Stochastic cellular automata modeling of urban land use dynamics: Empirical development and estimation","volume":"27","author":"Batty","year":"2003","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1016\/j.ejor.2004.08.029","article-title":"An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method","volume":"169","author":"Laumanns","year":"2006","journal-title":"Eur. J. Oper. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1111\/tgis.12331","article-title":"Calibration of cellular automata models using differential evolution to simulate present and future land use","volume":"22","author":"Feng","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"447","DOI":"10.3390\/ijgi4020447","article-title":"Simulating urban growth using a random forest-cellular automata (RF-CA) model","volume":"4","author":"Kamusoko","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"05015015","DOI":"10.1061\/(ASCE)UP.1943-5444.0000307","article-title":"Subjectivity versus objectivity: Comparative study between brute force method and genetic algorithm for calibrating the SLEUTH urban growth model","volume":"142","author":"Jafarnezhad","year":"2015","journal-title":"J. Urban Plan. Dev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.compenvurbsys.2018.03.003","article-title":"Comparison of metaheuristic cellular automata models: A case study of dynamic land use simulation in the Yangtze River Delta","volume":"70","author":"Feng","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1080\/13658816.2013.823498","article-title":"An intelligent method to discover transition rules for cellular automata using bee colony optimisation","volume":"27","author":"Yang","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ecolmodel.2012.03.011","article-title":"A spatiotemporal model of land use change based on ant colony optimization, markov chain and cellular automata","volume":"233","author":"Yang","year":"2012","journal-title":"Ecol. Model."},{"key":"ref_23","first-page":"1961","article-title":"A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion","volume":"30","author":"Cao","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1080\/13658810701731168","article-title":"Using neural networks and cellular automata for modelling intra-urban land-use dynamics","volume":"22","author":"Almeida","year":"2008","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compenvurbsys.2014.05.001","article-title":"Supporting sleuth\u2013enhancing a cellular automaton with support vector machines for urban growth modeling","volume":"49","author":"Rienow","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1504\/IJMHEUR.2014.068914","article-title":"An overview of metaheuristics: Accurate and efficient methods for optimisation","volume":"3","author":"Nesmachnow","year":"2014","journal-title":"Int. J. Metaheuristics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0169-2046(02)00218-9","article-title":"Modelling dynamic spatial processes: Simulation of urban future scenarios through cellular automata","volume":"64","author":"Barredo","year":"2003","journal-title":"Landsc. Urban Plan."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1080\/13658810903270551","article-title":"Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata","volume":"24","author":"Liu","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.apgeog.2014.06.016","article-title":"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","volume":"53","author":"Basse","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.envsoft.2017.11.012","article-title":"Multi-objective optimisation framework for calibration of cellular automata land-use models","volume":"100","author":"Newland","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v043.i09","article-title":"Unifying optimization algorithms to aid software system users: Optimx for R","volume":"43","author":"Nash","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v060.i02","article-title":"On best practice optimization methods in R","volume":"60","author":"Nash","year":"2014","journal-title":"J. Stat. Softw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1080\/13658810210157769","article-title":"Calibration of stochastic cellular automata: The application to rural-urban land conversions","volume":"16","author":"Wu","year":"2002","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1007\/s11430-007-0127-4","article-title":"Genetic algorithms for determining the parameters of cellular automata in urban simulation","volume":"50","author":"Li","year":"2007","journal-title":"Sci. China Ser. D Earth Sci."},{"key":"ref_35","unstructured":"Duthie, J., Kockelman, K., Valsaraj, V., and Zhou, B. (2007, January 7\u201311). Applications of integrated models of land use and transport: A comparison of itlup and urbansim land use models. Proceedings of the 54th Annual North American Meetings of the Regional Science Association International, Savannah, GA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1364\/OE.14.001767","article-title":"A computational method for the restoration of images with an unknown, spatially-varying blur","volume":"14","author":"Nagy","year":"2006","journal-title":"Opt. Express"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Brooks, C.P., Holmes, C., Kramer, K., Barnett, B., and Keitt, T.H. (2009). The role of demography and markets in determining deforestation rates near Ranomafana national park, Madagascar. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0005783"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1559\/152304005775194746","article-title":"An artificial-neural-network-based, constrained CA model for simulating urban growth","volume":"32","author":"Guan","year":"2005","journal-title":"Am. Cartogr."},{"key":"ref_39","first-page":"doi","article-title":"Spatial Context for Moving Vehicle Detection in Wide Area Motion Imagery with Multiple Kernel Learning","volume":"8751","author":"Shen","year":"2013","journal-title":"Proc. SPIE"},{"key":"ref_40","unstructured":"Price, K., Storn, R.M., and Lampinen, J.A. (2006). Differential Evolution: A Practical Approach to Global Optimization, Springer Science & Business Media."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1137\/S1052623499354242","article-title":"On the global convergence of the BFGS method for nonconvex unconstrained optimization problems","volume":"11","author":"Li","year":"2001","journal-title":"SIAM J. Optimiz."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0167-6377(96)00050-8","article-title":"Modifying the BFGS method","volume":"20","author":"Liao","year":"1997","journal-title":"Oper. Res. Lett."},{"key":"ref_43","unstructured":"Lewis, A.S., and Overton, M.L. (2018, April 28). Nonsmooth Optimization via Bfgs. Available online: https:\/\/www.semanticscholar.org\/paper\/Nonsmooth-Optimization-via-Bfgs-Overton\/9e9db481d16bc409abc95853687f3fd7e1784641."},{"key":"ref_44","unstructured":"Gay, D.M. (1990). Usage Summary for Selected Optimization Routines, AT&T Bell Laboratories. Computing Science Technical Report No. 153."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1002\/wilm.10034","article-title":"Jump-diffusion calibration using differential evolution","volume":"2011","author":"Ardia","year":"2011","journal-title":"Wilmott"},{"key":"ref_46","first-page":"609","article-title":"Sensitivity analysis in correlated bivariate continuous and binary responses","volume":"10","author":"Thmasebinejad","year":"2015","journal-title":"Appl. Appl. Math. Int. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1137\/S0895479893247679","article-title":"Variable block cg algorithms for solving large sparse symmetric positive definite linear systems on parallel computers, i: General iterative scheme","volume":"16","author":"Nikishin","year":"1995","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s11075-013-9713-z","article-title":"Composite convergence bounds based on chebyshev polynomials and finite precision conjugate gradient computations","volume":"65","author":"Gergelits","year":"2014","journal-title":"Numer. Algorithms"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v060.i03","article-title":"Spectral projected gradient methods: Review and perspectives","volume":"60","author":"Birgin","year":"2014","journal-title":"J. Stat. Softw."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10980-013-9984-8","article-title":"Recommendations for using the relative operating characteristic (ROC)","volume":"29","author":"Pontius","year":"2014","journal-title":"Landsc. Ecol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1080\/13658816.2013.862623","article-title":"The total operating characteristic to measure diagnostic ability for multiple thresholds","volume":"28","author":"Pontius","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.agee.2003.09.008","article-title":"Detecting important categorical land changes while accounting for persistence","volume":"101","author":"Pontius","year":"2004","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s00168-007-0138-2","article-title":"Comparing the input, output, and validation maps for several models of land change","volume":"42","author":"Pontius","year":"2008","journal-title":"Ann. Reg. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.stamet.2008.06.001","article-title":"A note on the linearly weighted kappa coefficient for ordinal scales","volume":"6","author":"Vanbelle","year":"2009","journal-title":"Stat. Methodol."},{"key":"ref_56","unstructured":"McGarigal, K. (2014). Fragstats v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps-Help Manual, University of Massachusetts."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1037\/0096-1523.9.2.242","article-title":"Spatial metrics of integral and separable dimensions","volume":"9","author":"Dunn","year":"1983","journal-title":"J. Exp. Psychol. Hum. Percept. Perform."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1007\/s00267-014-0247-5","article-title":"Land resources allocation strategies in an urban area involving uncertainty: A case study of suzhou, in the Yangtze River Delta of China","volume":"53","author":"Lu","year":"2014","journal-title":"Environ. Manag."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s00704-010-0263-1","article-title":"Modeling the impact of urbanization on the local and regional climate in Yangtze River Delta, China","volume":"102","author":"Zhang","year":"2010","journal-title":"Theor. Appl. Climatol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s10661-017-6224-8","article-title":"Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change","volume":"189","author":"Feng","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_61","unstructured":"(2018, April 28). National Earth System Science Data Sharing Infrastructure. Available online: www.geodata.cn."},{"key":"ref_62","unstructured":"(2018, April 28). Geospatial Data Cloud. Available online: www.gscloud.cn."},{"key":"ref_63","unstructured":"(2018, April 28). WorldPop. Available online: worldpop.org.uk."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.landurbplan.2017.09.019","article-title":"A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects","volume":"168","author":"Liu","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.apgeog.2017.12.001","article-title":"Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: The case of Athens","volume":"90","author":"Gounaridis","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_66","unstructured":"Liberti, L., and Drazic, M. (2005). Variable Neighbourhood Search for the Global Optimization of Constrained NLPs, Proceedings of the GO Workshop."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1038\/311419a0","article-title":"Cellular automata as models of complexity","volume":"311","author":"Wolfram","year":"1984","journal-title":"Nature"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chowdhury, S., Zhang, J., Messac, A., and Castillo, L. (2011, January 28\u201331). Characterizing the influence of land configuration on the optimal wind farm performance. Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Washington, DC, USA.","DOI":"10.1115\/DETC2011-48731"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/10\/387\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:26Z","timestamp":1760196146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/10\/387"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,25]]},"references-count":68,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["ijgi7100387"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7100387","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,25]]}}}