Urban Crime Risk Prediction Using Point of Interest Data

Geographical information systems have found successful applications to prediction and decision-making in several areas of vital importance to contemporary society. This article demonstrates how they can be combined with machine learning algorithms to create crime prediction models for urban areas. Selected point of interest (POI) layers fromOpenStreetMapare used to derive attributes describing micro-areas, which are assigned crime risk classes based on police crime records. POI attributes then serve as input attributes for learning crime risk prediction models with classification learning algorithms. The experimental results obtained for four UK urban areas suggest that POI attributes have high predictive utility. Classification models using these attributes, without any form of location identification, exhibit good predictive performance when applied to new, previously unseen micro-areas. This makes them capable of crime risk prediction for newly developed or dynamically changing neighborhoods. The high dimensionality of the model input space can be considerably reduced without predictive performance loss by attribute selection or principal component analysis. Models trained on data from one area achieve a good level of prediction quality when applied to another area, which makes it possible to transfer or combine crime risk prediction models across different urban areas.