Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors
Crime prediction research using AI has been actively conducted to predict potential crimes—generally, 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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/2/99 |
| _version_ | 1797395623869153280 |
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| author | Eunseo Kwon Sungwon Jung Jaewook Lee |
| author_facet | Eunseo Kwon Sungwon Jung Jaewook Lee |
| author_sort | Eunseo Kwon |
| collection | DOAJ |
| description | Crime prediction research using AI has been actively conducted to predict potential crimes—generally, 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’s 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. |
| first_indexed | 2024-03-09T00:38:10Z |
| format | Article |
| id | doaj.art-1d6baa9245fe490cbec86b02961a5c4e |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T00:38:10Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-1d6baa9245fe490cbec86b02961a5c4e2023-12-11T17:59:37ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-02-011029910.3390/ijgi10020099Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental FactorsEunseo Kwon0Sungwon Jung1Jaewook Lee2Department of Architecture, Sejong University, Seoul 05006, KoreaDepartment of Architecture, Sejong University, Seoul 05006, KoreaDepartment of Architectural Engineering, Sejong University, Seoul 05006, KoreaCrime prediction research using AI has been actively conducted to predict potential crimes—generally, 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’s 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.https://www.mdpi.com/2220-9964/10/2/99artificial neural networkk-modes clusteringcrime predictionsmart cityurban security |
| spellingShingle | Eunseo Kwon Sungwon Jung Jaewook Lee Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors ISPRS International Journal of Geo-Information artificial neural network k-modes clustering crime prediction smart city urban security |
| title | Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors |
| title_full | Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors |
| title_fullStr | Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors |
| title_full_unstemmed | Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors |
| title_short | Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors |
| title_sort | artificial neural network model development to predict theft types in consideration of environmental factors |
| topic | artificial neural network k-modes clustering crime prediction smart city urban security |
| url | https://www.mdpi.com/2220-9964/10/2/99 |
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