Monitoring Volatile Homicide Trends Across U.S. Cities

• Author: Andrew P. Wheeler,Tomislav V. Kovandzic
• Date: 01 May 2018
• DOI: 10.1177/1088767917740171
• Published date: 01 May 2018
• Subject Matter: Articles

https://doi.org/10.1177/1088767917740171

Homicide Studies

2018, Vol. 22(2) 119 –144

© 2017 SAGE Publications

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DOI: 10.1177/1088767917740171

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Article

Monitoring Volatile Homicide

Trends Across U.S. Cities

Andrew P. Wheeler1 and Tomislav V. Kovandzic1

Abstract

While U.S. homicide rates remain at near historical lows, large percentage increases

in homicide in 2015 have generated much speculation about its causes among policy

makers, academics, criminal justice practitioners, media representatives, and other

social commentators. In this article, we show how two data visualization tools, funnel

charts, and time series fan charts can show the typical volatility in homicide rates in

different cities over time. Many of the recent increases are not out of the norm given

historical patterns, and so one need not rely on various ex ante hypotheses to explain

homicide spikes occurring in many U.S. cities.

Keywords

data visualization, funnel charts, fan charts, homicide rates, time series

Introduction

In the aftermath of several incidents involving police officer shootings of minority

citizens, the media has focused on increases in homicide rates in several cities includ-

ing St. Louis, Chicago, and Baltimore. These increases have been linked to particular

events occurring within those cities such as civil unrest and a reduction of police activ-

ity (Dewan, 2017; Fagan & Richman, 2017; Morgan & Pally, 2016; Rosenfeld, 2015;

Towers & White, 2017; University of Chicago Crime Lab, 2017). To explore the valid-

ity of such claims, this article introduces two graphical methods, funnel charts, and

time series fan charts to evaluate whether homicide rates are atypical between cities in

any particular year or within cities over time. The application of these techniques

1The University of Texas at Dallas, Richardson, USA

Corresponding Author:

Andrew P. Wheeler, Criminology Program, School of Economic, Political & Policy Sciences,

The University of Texas at Dallas, 800 West Campbell Road, Mail Station GR 31, Richardson,

TX 75080-3021, USA.

Email: apwheele@gmail.com

740171HSXXXX10.1177/1088767917740171Homicide StudiesWheeler and Kovandzic

research-article2017

120 Homicide Studies 22(2)

allows us to advance the understanding of homicide trends and to offer researchers

better tools for analyzing crime data. Both of which may serve to better inform the

media, public, and policy makers.

Here, we show that homicide rates are a volatile measure, and prior attempts to

identify extreme changes over time, in particular percent changes in homicides, pro-

duce exaggerated evidence of change. Homicides are rare events, thus rates in cities

with smaller populations will tend to have more volatile fluctuations over time. Using

funnel charts, we will show that several cities, such as Chicago, Baltimore, and St.

Louis, have had historically high homicide rates compared with the cities of similar

population sizes consistently since at least the 1980s. Generating prediction intervals

using a random effects binomial regression model will also show that recent increases

in those cities are not atypical given historical homicide rates over time.

Identifying Changes in Homicide Rates Over Time

One of the most common ways to identify recent increases in homicides over time is

to calculate a percent change. This approach is common both in the popular press,1 as

well as in academic articles (Baumer & Wolff, 2014; Parker, Mancik, & Stansfield,

2016; Rosenfeld, 2016; Stamatel, 2014). For example, given a homicide rate of 10 (per

100,000) in the year 2000 and a homicide rate of 12 in the year 2010, the percent

change over the 10-year period would be (12 − 10) / 10 = 0.2, or 20%. Although per-

cent change is simple to calculate and interpret, it has particularly poor properties

when examining low-rate homicide trends. First, the variance of the metric is not

defined (Wheeler, 2016), so an actual outlying value is difficult to identify. Second, the

variance of the metric is influenced by the denominator, so places with lower homicide

rates will have more volatile changes from year to year. Third, the variance of the

metric is asymmetric; a change of a homicide rate from 4 to 5 is a 25% increase,

whereas a change from 5 to 4 is only a 20% decrease. This subsequently will result in

larger percentage increases than percentage decreases.

Highly varying percent changes can be seen in reported samples of homicide rates.

Stamatel (2014) shows decreases of 406% to increases of 100% for homicide rates in

various countries from 1956 through 1998. Baumer and Wolff (2014) show percent

decreases in large U.S. cities of up to 80% from 1993 to 2001. Parker et al. (2016)

show percent changes of –90% to +200% in a sample of 199 U.S. cities for more

recent crime trends from 2007 through 2011. Rosenfeld (2016) shows percent changes

in a selected sample of large U.S. cities of +120% to –40% for changes in homicides

from 2014 to 2015. Such ranges suggest that even percent changes of over 100% for

large polities are not uncommon and should not per se be considered unexpected sim-

ply by chance. Because of this year-to-year variability researchers often aggregate

homicide rates over multiple year spans and analyze those (see Beaulieu & Messner,

2010, for one example).

A more formal, statistical approach researchers have used to identify changes in

homicide trends is to estimate structural breaks in trends over time (Cook & Cook,

2011; Messner, Deane, Anselin, & Pearson-Nelson, 2005; Piehl, Cooper, Braga, &