{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:46Z","timestamp":1760145946201,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD is organized into a spatial (raster) grid and data are available in a consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs and Grasses, there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will not be EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.<\/jats:p>","DOI":"10.3390\/e26090784","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:25:22Z","timestamp":1726201522000},"page":"784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Information-Theoretic Modeling of Categorical Spatiotemporal GIS Data"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4019-093X","authenticated-orcid":false,"given":"David","family":"Percy","sequence":"first","affiliation":[{"name":"Geology Department, Portland State University, Portland, OR 97207, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7247-6636","authenticated-orcid":false,"given":"Martin","family":"Zwick","sequence":"additional","affiliation":[{"name":"Complex Systems Department, Portland State University, Portland, OR 97207, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of Land Cover Classification Accuracy Assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_2","unstructured":"(2024, January 01). USGS National Land Cover Database 2023, Available online: https:\/\/www.usgs.gov\/data\/national-land-cover-database-nlcd-2021-products."},{"key":"ref_3","unstructured":"Zwick, M. (2020). Reconstructability Analysis and Its OCCAM Implementation. Proceedings of the International Conference on Complex Systems, New England Complex Systems Institute."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1108\/03684920410533958","article-title":"An Overview of Reconstructability Analysis","volume":"33","author":"Zwick","year":"2004","journal-title":"Kybernetes"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Altieri, L., and Cocchi, D. (2024). Entropy Measures for Environmental Data: Description, Sampling and Inference for Data with Dependence Structures, Springer.","DOI":"10.1007\/978-981-97-2546-5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1538-4632.1974.tb01014.x","article-title":"Spatial Entropy","volume":"6","author":"Batty","year":"1974","journal-title":"Geogr. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Klir, G.J. (2005). Uncertainty and Information: Foundations of Generalized Information Theory, Wiley. [1st ed.].","DOI":"10.1002\/0471755575"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1002\/sres.3850030410","article-title":"Reconstructability Analysis: An Offspring of Ashby\u2019s Constraint Theory","volume":"3","author":"Klir","year":"1986","journal-title":"Syst. Res."},{"key":"ref_9","unstructured":"Krippendorff, K. (1986). Information Theory: Structural Models for Qualitative Data, Sage. Sage University Paper Series on Quantitative Applications in the Social Sciences."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_11","first-page":"99","article-title":"Constraint Analysis of Many-Dimensional Relations","volume":"9","author":"Ashby","year":"1964","journal-title":"Gen. Syst. Yearb."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Klir, G. (1985). The Architecture of Systems Problem Solving, Plenum Press.","DOI":"10.1007\/978-1-4757-1168-4"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Harris, M., and Zwick, M. (2021). Graphical Models in Reconstructability Analysis and Bayesian Networks. Entropy, 23.","DOI":"10.3390\/e23080986"},{"key":"ref_14","unstructured":"Percy, D., and Zwick, M. (2018, January 22\u201327). Beyond Spatial Autocorrelation: A Novel Approach Using Reconstructability Analysis. Proceedings of the International Society for the Systems Sciences 2018, Corvallis, OR, USA."},{"key":"ref_15","first-page":"154","article-title":"Information-Theoretic Mask Analysis of Rainfall Time Series Data","volume":"1","author":"Zwick","year":"1995","journal-title":"Adv. Sys Sci. Apps."},{"key":"ref_16","unstructured":"Zwick, M., Alexander, H., Fusion, J., and Willett, K. (2024, June 12). OCCAM: A Reconstructability Analysis Program. Available online: https:\/\/dmit.sysc.pdx.edu\/Occam_manual_v3.4.1_2021-01-19.pdf."},{"key":"ref_17","unstructured":"(2024, September 01). Occam-RA GitHub Site 2024. Available online: https:\/\/github.com\/occam-ra\/occam."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/9\/784\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:55:21Z","timestamp":1760111721000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/9\/784"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":17,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["e26090784"],"URL":"https:\/\/doi.org\/10.3390\/e26090784","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2024,9,13]]}}}