Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics

This paper highlights the potential societal benefits derived from big data applications with a focus on citizen safety and crime prevention. Authors detail a case study tackling the problem of crime hotspot classification, that is, the classification of which areas in a city are more likely to witness crimes based on past data. In the proposed approach demographic information is used along with human mobility characteristics as derived from anonymized and aggregated mobile network data.

Topics

Geographies of Inequalities

Author(s)

Author(s): 
Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Emmanuel Letouzé, Nuria Oliver, Alex "Sandy" Pentland, Fabio Pianesi

Partner Organization(s)

Data-Pop Alliance

Share

Recommendations

Screenshot 2026-04-14 at 9.41
Mali Gender Profile 2024
Social Media Post 2
Towards Caring Cities: a Geospatial Analysis in Viet Nam
Cover English
Technology-Facilitated Gender-Based Violence Against Women in Politics in Brazil
Cover Portugues
Violência de Gênero Facilitada pela Tecnologia contra Mulheres na Política no Brasil
1
Study on the Future Drivers of Sustainable and Inclusive Development in Liberia
Nepal
Towards Caring Cities: A Geospatial Analysis in Nepal
HumanDev
Making Human Measurement Matter for Human Development: Towards a Theory of Change for the Data Revolution
FosCover
Recommendations Report: Increasing and Strengthening the Availability of Digital Sexual and Reproductive Health Services with Youth-Friendly Approaches
[P124] cover Bangladesh_Report
Towards Caring Cities: A Geospatial Analysis in Dhaka City
2 (9)
Mapping and Dynamics of Violence involving Youth and Children in Urban Areas in Port-au-Prince