Ability of crime, demographic and business data to forecast areas of increased violence

ABSTRACT

Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011–2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.

KEYWORDS:

• Violence
• violent injuries
• crime forecasting
• machine learning

Authors' contributions

D. A. Bowen, L. M. Mercer Kollar, D. T. Wu, D. A. Fraser, C. E. Flood, J. C. Moore, E. W. Mays, and S. A. Sumner helped conceptualize the study. L. M. Mercer Kollar, D. A. Fraser, and C. E. Flood collected data. D. A. Bowen and S. A. Sumner analyzed data and completed the initial draft of the manuscript. All authors interpreted results, edited the manuscript, and approved the manuscript.

Disclosure statement

We declare no competing interests.

Additional information

Funding

This research was partially supported by a grant from the Robert Wood Johnson Foundation: “Piloting the Cardiff Model for Violence Prevention.” The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.