  Crime has been previously explained by social characteristics of the
residential population and, as stipulated by crime pattern theory, might also
be linked to human movements of non-residential visitors. Yet a full empirical
validation of the latter is lacking. The prime reason is that prior studies are
limited to aggregated statistics of human visitors rather than mobility flows
and, because of that, neglect the temporal dynamics of individual human
movements. As a remedy, we provide the first work which studies the ability of
granular human mobility in describing and predicting crime concentrations at an
hourly scale. For this purpose, we propose the use of data from location
technology platforms. This type of data allows us to trace individual
transitions and, therefore, we succeed in distinguishing different mobility
flows that (i) are incoming or outgoing from a neighborhood, (ii) remain within
it, or (iii) refer to transitions where people only pass through the
neighborhood. Our evaluation infers mobility flows by leveraging an anonymized
dataset from Foursquare that includes almost 14.8 million consecutive check-ins
in three major U.S. cities. According to our empirical results, mobility flows
are significantly and positively linked to crime. These findings advance our
theoretical understanding, as they provide confirmatory evidence for crime
pattern theory. Furthermore, our novel use of digital location services data
proves to be an effective tool for crime forecasting. It also offers
unprecedented granularity when studying the connection between human mobility
and crime.
