# Modelling Regional Crime Risk using Directed Graph of Check-ins

## TL;DR

This paper models urban crime risk by constructing directed graphs from Foursquare check-in data, deriving risk factors from movement patterns and crime history, and validating the approach with regression models in Chicago and NYC.

## Contribution

It introduces a novel directed graph-based feature set, DIFFER, for predicting regional crime risk using social mobility data.

## Key findings

- DIFFER features correlate with crime counts
- Effective in predicting monthly crime with multiple regression models
- Validated on Chicago and New York City datasets

## Abstract

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.