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Open Access 01-02-2021 | Original Paper

Predictive Extrinsic Factors in Multiple Victim Shootings

Authors: Daniel Ruderman, Ellen G. Cohn

Published in: Journal of Prevention | Issue 1/2021

Abstract

Although researchers have found support for a relationship between temperature and violence and evidence of temporal patterns in violent crime, research on homicide shows less consistent results and no research on mass murder has been conducted. We address this by examining predictive factors in multi-victim shootings (those with four or more victims, including injured), a more general crime category than mass murder, but one with likely similar predictive factors. We used data from the Gun Violence Archive to understand the relationship between multi-victim shootings and temperature as well as other extrinsic factors. To avoid the confound between season and temperature, we employed temperature anomaly (the difference between actual and expected temperature) as a predictor of daily shooting rate. Using a generalized linear model for the daily count of multi-victim shootings in the U.S., we found that these events are significantly more frequent on weekends, some major holidays, hotter seasons, and when the temperature is higher than usual. Like other crimes, rates of multi-victim shooting vary systematically.

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https://www.gunviolencearchive.org/reports, Downloaded 1/3/2020.

https://www.ncei.noaa.gov/data/local-climatological-data/, downloaded 11/19/2019 (2014–2018 data) and 1/4/20 (2019 data).

https://en.wikipedia.org/wiki/Wind_chill; we use the formula T_WC = 35.74 + 0.6215 * T_DB − 35.75 * v^0.16 + 0.4275 * T_DB * v^0.16, with T_DB the dry bulb temperature and v the wind speed in MPH. All temperatures are in °F. Wind chill correction was performed for T_DB < 50 °F and v > 3 MPH.

City apparent temperatures were estimated as averages of the up to nearest six weather stations within 250 miles of the city’s location (defined by the location of the lowest numerical zip code assigned to the city, found through the R package concensus). Daily apparent temperature for each station were evaluated on an hourly basis and averaged across 24 h in a day, accepting at most one data point as not available.

4/14/15, 11/19/16, 12/18/16, 2/9/17, 2/5/18, 4/8/18, and 2/16/19.

We also evaluated the Bayesian Information Criterion (BIC), which greatly penalizes additional parameters when modeling large data sets, such as this one. BIC very strongly selected the model M3, with a temperature factor but no temporal cyclical factor, above all others. We believe BIC to be too conservative with model parameters, and prefer AIC for model ranking.

https://www.r-project.org/.

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Title: Predictive Extrinsic Factors in Multiple Victim Shootings
Authors: Daniel Ruderman
Ellen G. Cohn
Publication date: 01-02-2021
Publisher: Springer US
Published in: Journal of Prevention / Issue 1/2021
Print ISSN: 2731-5533
Electronic ISSN: 2731-5541
DOI: https://doi.org/10.1007/s10935-020-00602-3

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