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Article

Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model

1
Augurisk, Inc., Wilmington, DE 19802, USA
2
Velebit Artificial Intelligence LLC, 10000 Zagreb, Croatia
3
Independent Researcher, Dunedin, FL 34698, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 645; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110645
Received: 11 September 2020 / Revised: 13 October 2020 / Accepted: 23 October 2020 / Published: 29 October 2020
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. View Full-Text
Keywords: crime prediction; ensemble learning; machine learning; regression crime prediction; ensemble learning; machine learning; regression
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MDPI and ACS Style

Lamari, Y.; Freskura, B.; Abdessamad, A.; Eichberg, S.; de Bonviller, S. Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model. ISPRS Int. J. Geo-Inf. 2020, 9, 645. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110645

AMA Style

Lamari Y, Freskura B, Abdessamad A, Eichberg S, de Bonviller S. Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model. ISPRS International Journal of Geo-Information. 2020; 9(11):645. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110645

Chicago/Turabian Style

Lamari, Yasmine, Bartol Freskura, Anass Abdessamad, Sarah Eichberg, and Simon de Bonviller. 2020. "Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model" ISPRS International Journal of Geo-Information 9, no. 11: 645. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110645

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