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State population totals: 2010-2019: Technical report."},{"key":"10.1016\/j.compenvurbsys.2025.102322_b69","article-title":"A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation","volume":"137","author":"Xing","year":"2020","journal-title":"Computational Geosciences"},{"key":"10.1016\/j.compenvurbsys.2025.102322_b70","doi-asserted-by":"crossref","DOI":"10.1016\/j.compenvurbsys.2019.101402","article-title":"Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration","volume":"79","author":"Yang","year":"2020","journal-title":"Computers, Environment and Urban Systems"},{"issue":"4","key":"10.1016\/j.compenvurbsys.2025.102322_b71","doi-asserted-by":"crossref","DOI":"10.3390\/tropicalmed8040238","article-title":"An integrative explainable artificial intelligence approach to analyze fine-scale land-cover and land-use factors associated with spatial distributions of place of residence of reported dengue cases","volume":"8","author":"Yang","year":"2023","journal-title":"Tropical Medicine and Infectious Disease"},{"key":"10.1016\/j.compenvurbsys.2025.102322_b72","doi-asserted-by":"crossref","DOI":"10.1016\/j.compenvurbsys.2021.101689","article-title":"Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation","volume":"90","author":"Yu","year":"2021","journal-title":"Computers, Environment and Urban Systems"},{"issue":"7","key":"10.1016\/j.compenvurbsys.2025.102322_b73","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1080\/13658816.2020.1711915","article-title":"Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata","volume":"34","author":"Zhai","year":"2020","journal-title":"International Journal of Geographical Information Science"}],"container-title":["Computers, Environment and Urban Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0198971525000754?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0198971525000754?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T04:59:53Z","timestamp":1779771593000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0198971525000754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":73,"alternative-id":["S0198971525000754"],"URL":"https:\/\/doi.org\/10.1016\/j.compenvurbsys.2025.102322","relation":{},"ISSN":["0198-9715"],"issn-type":[{"value":"0198-9715","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights","name":"articletitle","label":"Article Title"},{"value":"Computers, Environment and Urban Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compenvurbsys.2025.102322","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. 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