Leveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Cities

by   Cristina Kadar, et al.

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.


page 1

page 2

page 3

page 4


Mobility signatures: a tool for characterizing cities using intercity mobility flows

Understanding the patterns of human mobility between cities has various ...

Leveraging Language Foundation Models for Human Mobility Forecasting

In this paper, we propose a novel pipeline that leverages language found...

Predictability states in human mobility

Spatio-temporal constraints coupled with social constructs have the pote...

Field theory for recurrent mobility

Understanding human mobility is crucial for applications such as forecas...

Transportation Scenario Planning with Graph Neural Networks

Providing efficient human mobility services and infrastructure is one of...

FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation

Generating realistic human flows across regions is essential for our und...

Forecasting racial dynamics at the neighborhood scale using Density-functional Fluctuation Theory

Racial residential segregation is a defining and enduring feature of U.S...

Please sign up or login with your details

Forgot password? Click here to reset