Crime has been previously explained by social characteristics of the residential population and, as stipulated by crime pat- tern 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 mo- bility 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 incom- ing 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 lever- aging 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 con- firmatory 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 unprece- dented granularity when studying the connection between hu- man mobility and crime.