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Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model
Content Provider | MDPI |
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Author | Lamari, Yasmine Freskura, Bartol Abdessamad, Anass Eichberg, Sarah Bonviller, Simon De |
Copyright Year | 2020 |
Description | While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases that tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73% and 77% when predicting property crimes and violent crimes, respectively. |
Starting Page | 645 |
e-ISSN | 22209964 |
DOI | 10.3390/ijgi9110645 |
Journal | ISPRS International Journal of Geo-Information |
Issue Number | 11 |
Volume Number | 9 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-10-29 |
Access Restriction | Open |
Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Criminology and Penology Crime Prediction Ensemble Learning Machine Learning Regression |
Content Type | Text |
Resource Type | Article |