  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.
