Modelling Regional Crime Risk using Directed Graph of Check-ins
This work addresses crime prediction for urban planners and law enforcement by leveraging social network data, though it appears incremental as it builds on existing mobility-based approaches.
The authors tackled the problem of predicting regional crime risk by constructing a directed graph from Foursquare check-in data to model human mobility, and they introduced DIFFER features that showed reliable correlations with crime counts, achieving verification through regression models in Chicago and New York City.
The location-based social network, Foursquare, reflects the human activities of a city. The mobility dynamics inferred from Foursquare helps us understanding urban social events like crime In this paper, we propose a directed graph from the aggregated movement between regions using Foursquare data. We derive region risk factor from the movement direction, quantity and crime history in different periods of the day. Later, we propose a new set of features, DIrected graph Flow FEatuRes (DIFFER) which are associated with region risk factor. The reliable correlations between DIFFER and crime count are observed. We verify the effectiveness of the DIFFER in monthly crime count using Linear, XGBoost, and Random Forest regression in two cities, Chicago and New York City.