RT Book
T1 Improving Crime Count Forecasts Using Twitter and Taxi Data
A1 Härdle, Wolfgang Karl
A2 Vomfell, Lara
A2 Lessmann, Stefan
LA English
YR 2020
UL https://krimdok.uni-tuebingen.de/Record/1866589210
AB Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns derived from taxi trip, Twitter and Foursquare data. Analysis of a six-month period of crime data for New York City shows that these data sources improve predictive accuracy for property crime by 19% compared to using only demographic data. This effect is strongest when the novel features are used together, yielding new insights into crime prediction. Notably and in line with social disorganization theory, the novel features cannot improve predictions for violent crimes
DO 10.1016/j.dss.2018.07.003