{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T23:15:48Z","timestamp":1791242148152,"version":"4.1.0"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T00:00:00Z","timestamp":1613952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20132018R1A2B2005528"],"award-info":[{"award-number":["NRF\u20132018R1A2B2005528"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Crime prediction research using AI has been actively conducted to predict potential crimes\u2014generally, crime locations or time series flows. It is possible to predict these potential crimes in detail if crime characteristics, such as detailed techniques, targets, and environmental factors affecting the crime\u2019s occurrence, are considered simultaneously. Therefore, this study aims to categorize theft by performing k-modes clustering using crime-related characteristics as variables and to propose an ANN model that predicts the derived categorizations. As the prediction of theft types allows people to estimate the features of the possibly most frequent thefts in random areas in advance, it enables the efficient deployment of police and the most appropriate tactical measures. Dongjak District was selected as the target area for analysis; thefts in the district showed four types of clusters. Environmental factors, representative elements affecting theft occurrence, were used as input data for a prediction model, while the factors affecting each cluster were derived through multiple linear regression analysis. Based on the results, input variables were selected for the ANN model training per cluster, and the model was implemented to predict theft type based on environmental factors. This study is significant for providing diversity to prediction methods using ANN.<\/jats:p>","DOI":"10.3390\/ijgi10020099","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T14:23:27Z","timestamp":1614003807000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors"],"prefix":"10.3390","volume":"10","author":[{"given":"Eunseo","family":"Kwon","sequence":"first","affiliation":[{"name":"Department of Architecture, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungwon","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Architecture, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8036-4204","authenticated-orcid":false,"given":"Jaewook","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Architectural Engineering, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MC.2004.1297301","article-title":"Crime data mining: A general framework and some examples","volume":"37","author":"Chen","year":"2004","journal-title":"Computer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/S0169-2070(03)00094-3","article-title":"Criminal incident prediction using a point-pattern-based density model","volume":"19","author":"Liu","year":"2003","journal-title":"Int. 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