Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data

• Published in: Sustainability Vol. 11; no. 10; p. 2848
• Main Authors: Matijosaitiene, Irina, McDowald, Anthony, Juneja, Vishal
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 19.05.2019
• Subjects: Access control; Algorithms; Artificial intelligence; Burglary; Collaboration; Crime; Crime prevention; Data processing; Data reduction; Datasets; Deep learning; Graffiti; Identification methods; International conferences; Learning algorithms; Machine learning; Pattern recognition; Pavements; Prediction models; Regression analysis; Robbery; Spatial data; Surveillance; Theft; Traffic flow; Urban areas; Urban planning; Vehicles; Video data; Workflow; Denmark; United States > US
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su11102848

This research aims to identify spatial and time patterns of theft in Manhattan, NY, to reveal urban factors that contribute to thefts from motor vehicles and to build a prediction model for thefts. Methods include time series and hot spot analysis, linear regression, elastic-net, Support vector machines SVM with radial and linear kernels, decision tree, bagged CART, random forest, and stochastic gradient boosting. Machine learning methods reveal that linear models perform better on our data (linear regression, elastic-net), specifying that a higher number of subway entrances, graffiti, and restaurants on streets contribute to higher theft rates from motor vehicles. Although the prediction model for thefts meets almost all assumptions (five of six), its accuracy is 77%, suggesting that there are other undiscovered factors making a contribution to the generation of thefts. As an output demonstrating final results, the application prototype for searching safer parking in Manhattan, NY based on the prediction model, has been developed.