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The location-based social network, Foursquare, reflects the human activitiesof a city. The mobility dynamics inferred from Foursquare helps usunderstanding urban social events like crime In this paper, we propose adirected graph from the aggregated movement between regions using Foursquaredata. We derive region risk factor from the movement direction, quantity andcrime history in different periods of the day. Later, we propose a new set offeatures, DIrected graph Flow FEatuRes (DIFFER) which are associated withregion risk factor. The reliable correlations between DIFFER and crime countare observed. We verify the effectiveness of the DIFFER in monthly crime countusing Linear, XGBoost, and Random Forest regression in two cities, Chicago andNew York City.

Modelling Regional Crime Risk using Directed Graph of Check-ins