Citation

Bowen DA, Mercer Kollar LM, Wu DT, Fraser DA, Flood CE, Moore JC, Mays EW, Sumner SA. Int. J. Inj. Control Safe. Promot. 2018; 25(4): 443-448.

Affiliation

Division of Violence Prevention, National Center For Injury Prevention and Control , U.S. Centers for Disease Control and Prevention (CDC) , Atlanta , GA , USA.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/17457300.2018.1467461

PMID

29792563

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.

Language: en

Keywords

Violence; crime forecasting; machine learning; violent injuries