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Gun victimization in the line of duty

Article  in  Criminology & Public Policy · July 2020

DOI: 10.1111/1745-9133.12507

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DOI: 10.1111/1745-9133.12507

SPECIAL ISSUE ARTICLE

CUTTING-EDGE RESEARCH IN POLICE POLICY AND PRACTICE

Gun victimization in the line of duty Fatal and nonfatal firearm assaults on police officers in the United States, 2014–2019

Michael Sierra-Arévalo1 Justin Nix2

1 University of Texas at Austin 2 University of Nebraska Omaha

Correspondence MichaelSierra-Arévalo,Department ofSociology,TheUniversityofTexasat Austin,305E23rdSt,A1700,RLP3.306, Austin,TX78712. Email:[email protected]

Research Summary: Using open-source data from the Gun Violence Archive (GVA), we analyze national- and state-level trends in fatal and nonfatal firearm assaults of U.S. police officers from 2014 to 2019 (N = 1,467). Results show that (a) most firearm assaults are nonfatal, (b) thereis nocompelling evidencethat thenational rate of firearm assault on police has substantially increased during the last 6 years, and (c) there is substantial state-level variation in rates of firearm assault on police officers. Policy Implications: GVA has decided strengths rela- tive to existing data sources on police victimization and danger in policing. We consider the promises and pit- falls of this and other open-source data sets in polic- ing research and recommend that recent state-level improvements in use-of-force data collection be repli- cated and expanded to include data on violence against police.

KEYWORDS danger, firearm assault, gun violence, policing

After more than 50 years of social science research on policing in the United States, the dan- ger of police work remains a salient feature of police officers’ occupational environment (Lof- tus, 2010; Marenin, 2016; Sierra-Arévalo, 2019). Scholarly attention to the danger of policing has been renewed by recent discussion of a “war on cops” that began after the 2014 police killing of Michael Brown in Ferguson, Missouri. Proponents of this hypothesized war posit that the contemporary political climate has resulted in widespread distrust and even disdain of police

Criminology & Public Policy. 2020;1–26. © 2020 American Society of Criminology 1wileyonlinelibrary.com/journal/capp

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on the part of public officials, academics, and the news media; in turn, the public has become increasingly “anti-police” and emboldened to question, resist, and violently attack police offi- cers on U.S. streets (Mac Donald, 2016). Despite widespread concern among police administra- tors (Nix, Wolfe, & Campbell, 2018), however, empirical research on the most dire implication of a war on cops—violence against police—finds no significant increases in fatal or nonfatal violence against police in recent years (Maguire, Nix, & Campbell, 2017; Shjarback & Maguire, 2019). Nonetheless, the issue of violence against police remains highly salient to U.S. politics and policy, including the rise of the Blue Lives Matter movement and the growth in laws seeking enhanced penalties for killing police officers (Craven, 2017).

1

Despite the rich history of research on danger in police work, there are several long-standing limitations to this body of scholarship. First, researchers’ operationalization of “danger” tends toward the rarest, most extreme measure of danger in police work: felonious line-of-duty deaths that are driven by firearm assaults (see White, Dario, & Shjarback, 2019, p. 14). This focus on felo- nious deaths underestimates the total scope of the danger police confront by ignoring nonfatal violence against officers (cf. Bierie, 2017; Bierie, Detar, & Craun, 2016), including nonfatal firearm assaults that,eventhoughtheydonotresult inaline-of-dutydeath,representcasesofdeadlyforce directed at police. Second, analyses that attend to all assaults on police officers better capture less- than-lethal violence (e.g., punches and kicks) but do not differentiate such cases from especially lethal threats like firearmassaults (Shjarback& Maguire, 2019; Tiesman, Gwilliam, Konda, Rojek, & Marsh, 2018; cf. Bierie et al., 2016). Third, data sources that rely on voluntary reporting by police (e.g., LEOKA and NIBRS) are limited by a lack of consistent reporting by law enforcement agen- cies and marked lag times in the release of said data, frustrating timely, confident estimates of a pressingpublicsafetyandpolicyissue(Kuhns,Dolliver,Bent,&Maguire,2016,p.6;Nix,Richards, Pinchevsky, & Wright, 2019, p. 6; Shjarback & Maguire, 2019). Because of its inattention to cases in which officers are shot but not killed, existing research

tends to provide either an underestimate of gun violence directed at officers or eschew specificity in favor of an estimate of assault broadly defined. This, in combination with the data quality and timeliness issues that affect data sets commonly used to examine violence against police, pre- vents accurate estimates of total firearm assaults on officers that are of long-standing salience to the issue of officer safety in the United States (Cell, 2019; The President’s Commission on Law Enforcement and Administration of Justice, 1967, p. 239).

2 Given the decided gravity of the prob-

lem at hand, there is a clear and urgent need for researchers to bring new, more timely data to bear. Thisarticleaddressestheseissueswithopen-sourcedataprovidedbytheGunViolenceArchive

(GVA), a nonprofit organization that collects and constantly updates data on firearm assaults of police officers across the United States. Because GVA records both fatal and nonfatal firearm assaults on police, we are able to provide an estimate of firearm assaults on police officers that includes (and differentiates) fatal and nonfatal shootings.

3 We use these data to provide national-

and state-level estimates of fatal and nonfatal firearm assaults against police officers in the United States from 2014 to 2019. We conclude with consideration of future directions for this research as well as the promises and limitations of data like those collected by GVA in research on violence against and by police. We also provide concrete policy recommendations for improving the qual- ity and timeliness of data on violence against police to better support police agencies, researchers, and policy makers.

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1 LITERATURE REVIEW

Social science research on the danger of police work in the United States can trace its roots back more than half a century to foundational ethnographic studies of life on patrol. Early single-site studies (Westley, 1953, 1970) and comparative studies (Banton, 1964) noted officers’ tangible pre- occupation with danger and violence in the line of duty. Decades of subsequent scholarship have confirmed the enduring importance placed by officers, supervisors, and the police organization on the reality of violence in policing (Brown, 1988; Moskos, 2009; Sierra-Arévalo, 2016; Skolnick, 1966), especially when that violence proves deadly (Manning, 1977, pp. 7–8; Sierra-Arévalo, 2019). Such qualitative research began to be complemented by quantitative analyses of line-of-duty

danger beginning in the 1970s. In 1971, a group of law enforcement executives—in response to sharp increases in felonious officer deaths throughout the 1960s—called for an expansion of the FBI’sdatacollectioneffortsonviolencedirectedatpolice(Rabe-Hemp,2017,pp.61–62).Beginning in 1972, the FBI began collecting more detailed information on both officers killed and officers assaulted in the line of duty, eventually combining these data in 1982 into what is now commonly known as LEOKA, or Law Enforcement Officers Killed and Assaulted (FBI, 2019a). Researchers quickly took advantage of this new data source to quantitatively assess the landscape of violence against police. The earliest analyses of LEOKA data concentrated on felonious officer deaths, specifically in

cities, and uncovered a positive relationship with structural factors such as the percentage of the city population that is Black, city crime rate, and the proportion of a city living in poverty (Lester, 1977, 1984). Later city-level analyses examined the relationship between political, city-level factors like Black representation in city council and Black mayorship on felonious police deaths (Jacobs & Carmichael, 2002; Kaminski & Stucky, 2009; Kent, 2010). LEOKA has also been used to exam- ine felonious officer deaths at the national (Swedler, Simmons, Dominici, & Hemenway, 2015), regional (Fridell & Pate, 1995), and county level (Kaminski, 2008). Finally, other scholars have moved beyond LEOKA and turned to the National Violent Death Reporting System (NVDRS; Blair,Fowler,Betz,&Baumgardner,2016), theNationalIncidentBasedReportingSystem(NIBRS; Bierie, 2017; Bierie et al., 2016; Willits, 2014), or data collected by nonprofit organizations like the National Law Enforcement Officers Memorial Fund (NLEOMF) or the Officer Down Memorial Page (Kaminski & Marvell, 2002; Maguire et al., 2017; White et al., 2019) to explore patterns in felonious police deaths. Scholars have noted for some time, however, that analyses focused on felonious line-of-duty

deaths systematically underestimate the full scope of danger that officers face by excluding non- fatalassaults(seeBrandl,1996).Accordingly,otherresearchhasanalyzednonfatalassaults,specif- ically (Shjarback & Maguire, 2019; Tiesman et al., 2018), both fatal and nonfatal assaults (Crifasi, Pollack, & Webster, 2016; Fridell, Faggiani, Taylor, Brito, & Kubu, 2009), or some combination of fatalassaults, nonfatalassaults, andline-of-dutyaccidents(Brandl,1996; White etal., 2019).These related streams of research provide invaluable insight but, of course, also come with important limitations. With regard to studies that focus on nonfatal assaults or which examine both fatal and nonfatal

assaults, theclearestbenefitofsuchresearchis itsabilitytodescribethemostcommontypeofvio- lencedirectedatpolice.EstimatesfromthemostrecentlyavailableLEOKAstatistics illustratethis point: In comparison with the 55 officers feloniously killed in 2018 (51 by firearm), nearly 59,000 were nonfatally assaulted (2,116 by firearm; FBI, 2019b). This practical benefit notwithstanding, special attention to nonfatal assaults often obfuscates the particular phenomenon of assaults that, even if nonfatal, constitute a use of deadly force against police.

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For example, Shjarback and Maguire’s (2019) time-series analysis of LEOKA data to investigate trends in violence directed at police, although able to provide cautious estimates of national-level trends in nonfatal assaults, did not analytically distinguish an injury caused by a fist or a bullet. Tiesman et al. (2018)) analysis of injurious assaults treated in U.S. emergency rooms and analyses employing NIBRS data had the same limitation (Bierie, 2017; Willits, 2014).

4

Several studies did disaggregate fatal and nonfatal firearm assaults on police across the United States. Bierie and colleagues’ (2016) analysis of gun violence against police included both fatal and nonfatal firearm assault estimates drawn from NIBRS, improving on past research that either focused on fatal assaults alone or conflated firearm assault with assault more generally. Although NIBRS collects data from multiple states and thousands of law enforcement agencies, it is affected bydataissuesnotunlikethosethataffectLEOKA(Kuhnsetal.,2016,p.6). In2010,themostrecent year of NIBRS available to Bierie et al. (2016), approximately 5,400 agencies from 37 states were represented in NIBRS, capturing only 37% of agencies and oversampling on small- and medium- sized agencies (2016, p. 506). In the same vein, even though Crifasi et al. (2016) differentiated fatal from nonfatal firearm assaults in their study of assault lethality, their reliance on LEOKA data raises concerns about the reliability of their firearm assault estimates similar to other studies employing this data set. Besides the lack of representativeness that characterizes LEOKA and NIBRS data, the issue

of significant lag times in the release of these data creates marked challenges in providing timely, accurateanalysesofdeadlyforceagainstpolice.Althoughopen-sourcedatalikethoseprovidedby the National Law Enforcement Officer Memorial Fund (NLEOMF) and the Officer Down Memo- rial Page (ODMP) have provided practically real-time data on officers accidentally and feloniously killed in the line of duty, they have not recorded information on nonfatal assaults. As a result, researchers interested in nonfatal assaults are mainly restricted to data that are anywhere from 18 to 24 months old (Kuhns et al., 2016, p. 6).

5 This is, of course, neither the fault of researchers nor,

to our knowledge, the result of willful tardiness on the part of government—collecting and clean- ing data from thousands of independent law enforcement agencies is a monumental undertaking. Nonetheless, the persistent limitations of existing data create clear need for new, national-level data sources that can enable more timely investigation of firearm violence against police and sup- port the decision-making of law enforcement agencies and policy makers.

2 DATA AND METHOD

2.1 Data source

This analysis uses data collected by the Gun Violence Archive (GVA), an independent, nonprofit organization whose mission is to “provide free online public access to accurate information about gun-related violence in the United States” (GVA, 2020a, para. 2). The GVA’s definition of gun- related violence is expansive and tracks firearm homicides, suicides, and injuries, as well as acci- dentalshootings,defensivefirearmuses,massshootings,officer-involvedshootings,andmore.To gatherthisdata,GVAresearchersmonitorapproximately7,500newsmedia, lawenforcement,and governmental sources from across the United States for cases of firearm violence. Additionally, GVA researchers manually sweep social media accounts (e.g., Facebook and Twitter) and web- sites to gather relevant cases. For each incident, GVA records date, geocoded location, city/county, state, available victim- and perpetrator-level information (e.g., name, age, and sex), incident type

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(e.g., “Shot – Wounded/Injured” and “Shot – Dead”), and URL links to online sources that docu- ment each incident (GVA, 2020b). In addition to data on officer-involved shootings of the public tracked by other open-source

efforts, 6 GVAalsorecordsfirearmviolencedirectedatlawenforcementofficers.Additionally,GVA

includes and differentiates fatal and nonfatal firearm assaults, allowing for more complete and fine-grained estimation of the firearm violence that results in the death and nonfatal injury of police officers.

2.2 Case selection and analytic strategy

All cases in the GVA’s larger data set in which law enforcement officers were shot (fatally and nonfatally) were provided by GVA for the period between January 1, 2014 and December 31, 2019.

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We restrict our analytic sample in several ways. First, we include active, sworn local and state law enforcement officers who are members of

agencies that respond to calls for service; this sample is composed of officers employed by local departments at the city or county level, sheriff’s departments, and state police agencies. Addition- ally, our sample includes special jurisdiction officers such as transit or university police, tribal police, and specialized state agencies like wildlife or park police whose patrol and enforcement activities are reasonably similar to local and state departments. We exclude federal law enforce- ment officers, parole and probation agents, and court officers who, although sworn, do not engage in routine investigatory, patrol, or enforcement activity. Second, our analytic sample is restricted to cases involving (a) on-duty officers, (b) whose per-

son or equipment (excluding vehicles) was shot, 8 (c) with a firearm, (d) by someone who is not a

police officer (including while struggling with a suspect over a firearm). 9 These criteria exclude

off-dutyfirearminjuries; injuriescausedbymeansotherthanapistol,rifle,orshotgun(e.g.,shrap- nel from an explosion, pellet gun); self-inflicted firearm injuries whether accidental (e.g., training accident) or intentional (e.g., suicides or suicide attempts); and “blue on blue” shootings in which one officer accidentally shot another officer.

10 Additionally, these criteria exclude cases in which

a suspect fired at but did not strike an officer, as well as those in which a suspect pointed a firearm at an officer but did not fire. To select this sample from the raw data provided by GVA, the authors (and a research assistant

directly supervised by the first author) independently checked each case (N = 1,962). The case-by- case check was accomplished by following the online sources recorded by GVA for every individ- ual listedinthedataset.BecauseURLsforonlinemediareportsweresometimesinactive,Internet searches using the incident date, incident location, and available officer names were used to find other sources to verify the incident. In the interest of providing a conservative estimate of nonfatal firearm injury, cases for which media sources listed an officer as “wounded,” “injured,” or “hurt” but did not specifically stipulate a gunshot injury from a bullet, shot (e.g., shotgun ammunition), bullet fragments, or shrapnel were excluded. Similarly, cases in which it was unclear whether an officer shot themselves, was shot by a suspect, or was shot by another officer were excluded to err toward a conservative estimate. Cases that coders were uncertain how to code were flagged and reviewed by the authors to arrive at a final coding decision. Our inclusion criteria and coding process produced an analytic sample of 1,467 cases for our descriptive analysis of fatal and non- fatal firearm assaults on police officers to provide estimates at the national and state level (see Table 1).

11

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TABLE 1 Officers fatally and nonfatally shot by a suspect, 2014–2019

Nonfatal Fatal Total Year N % N % N % 2014 152 80.4 37 19.6 189 100.0 2015 202 86.3 32 13.7 234 100.0 2016 229 79.5 59 20.5 288 100.0 2017 211 85.1 37 14.9 248 100.0 2018 195 81.3 45 18.8 240 100.0 2019 229 85.5 39 14.6 268 100.0 Total 1,218 83.0 249 17.0 1,467 100.0

Note: Some rows may not sum to 100% due to rounding.

To calculate national rates of firearm assault per 100,000 officers (or at the state level, per 1,000 officers), we use estimates of the number of sworn local and state officers from the FBI’s Police Employee data (PE), which document the number of sworn officers at the agency level.

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As mentioned in our discussion of past work, PE data (and the FBI’S UCR data more broadly) have well-documented issues with incomplete reporting/missing data (King, Cihan, & Heinonen, 2011; Lynch & Jarvis, 2008). We also note that 2019 PE data are currently unavailable at the time of this article’s writing, further underscoring our critique of the lag time in the release of governmentally produced policing data. To address these two issues, we follow the suggestions of past research (King et al., 2011, p. 450; Stucky, 2005) and use multiple years of PE data to impute missing estimates of sworn state and local officers. Specifically, we calculate a quadratic regression function for each state’s officer population for 2013–2018, then use the regression coef- ficients for year and year2 to estimate missing state-years. We use this approach to impute a total of 53 values, 51 of which are 2019 imputations (50 states plus Washington, D.C.) and two of which correspond to a single year of missing data for Alaska in 2015 and West Virginia in 2014.

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Despite our use of multiple years of data to impute 2019 values for each state and mitigate the unreliability of any single-year estimate, some states in the PE data show reporting problems across several years. According to FBI UCR records (FBI, 2019c), three states—Mississippi, Indi- ana, and West Virginia—had less than 75% of agencies in metropolitan statistical areas (MSAs), cities outside MSAs, or nonmetropolitan counties report data to the FBI for every year between 2013 and 2018.

14 We denote these three states in all our analyses of state-level trends in firearm

assault on police officers with “*” and discuss the broader implications of such data quality issues in our Discussion.

3 RESULTS

From 2014 to 2019, 249 police officers were fatally shot by suspects and 1,218 were struck or non- fatally wounded by suspect gunfire (see Table 1). The total number of firearm assaults during this period has shifted between a low of 189 in 2014 to a high of 288 in 2016. During the full 6-year period, an average of 245 officers a year were shot by suspects in the line of duty. Of those shot, an average of 42 per year (17%) were killed; only 14% to 21% of firearm assaults on officers each year result in fatalities, underscoring the importance of collecting and analyzing data on nonfatal firearm assaults alongside those on fatal firearm assaults.

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FIGURE 1 Monthly firearm assaults on U.S. police, 2014–2019 [Color figure can be viewed at wileyonlineli- brary.com]

Figure 1 presents monthly frequencies of fatal and nonfatal firearm assault on officers from 2014 to 2019. On average, 20 officers were assaulted with firearms each month. The number of monthly firearm assaults ranged from a low of 10 in February 2014 to a high of 46 in February 2016. Interestingly, there is no clear evidence of seasonality in firearm assaults on officers overall or when looking at nonfatal firearm assault, running counter to seasonal patterns found by some research for violence and crime more generally (McDowall & Curtis, 2015; McDowall, Loftin, & Pate, 2012). Turning to longitudinal trends, although the trend in the monthly frequency of fatal firearm assaults on officers is flat from 2014 to 2019, there does appear to be a slight, upward trend in the monthly frequency of firearm assaults on officers, overall. This overall trend is driven by the parallel trendline in monthly nonfatal firearm assaults. Without accurate estimates of the population of officers in the United States per month, however, it is not possible to calculate a monthly rate. Though, to our knowledge, no such monthly estimates exist, we can aggregate monthly counts of firearm assaults into yearly counts and use yearly estimates of the population of U.S. police officers to calculate annual rates. Figure 2 does exactly this and plots the national rate of firearm assault on police officers (per

100,000 officers) from 2014 to 2019 and disaggregates this overall rate into separate trend lines for fatal and nonfatal firearm assaults. Across the time series, the national rate of firearm assault on police was lowest in 2014 (29.92 per 100,000 officers) and highest in 2016 (44.11 per 100,000 officers). Overall, the national rate shows a slight upward trend between 2014 and 2019 (B = .750). Turning to the disaggregated trendlines for fatal and nonfatal firearm assaults, two notable pat-

ternsemerge.First,wefindthatyear-to-yearchangesinthefatalandnonfatalfirearmassaultrates do not consistently track one another over time. The rates of fatal and nonfatal firearm assault diverge from 2014 to 2015, move in parallel between 2015 and 2017, and diverge again from 2018 to 2019. Note also that 2017 to 2018 is the only period in which the rate of nonfatal firearm assault on officers decreases while the rate of fatal assault increases. Overall, these longitudinal patterns reinforce that trends in the national rate of firearm assault on police are mainly driven by changes in the rate of nonfatal firearm assault.

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FIGURE 2 National rate of firearm assault on police, 2014–2019 [Color figure can be viewed at wileyonlineli- brary.com]

Similar to the frequency trends shown in Figure 1, the trend in the national fatal firearm assault rate is flat from 2014 to 2019 (B = –.025), whereas the fitted linear trends for total firearm assaults and nonfatal firearm assaults shows a slight increase (B = .750, .772). Of course, sober interpre- tation of this increase is merited given that the slope of both of these trend lines is minimal and represents a small yearly increase in the number of officers nonfatally assaulted with firearms. To illustrate this, let us assume a static number of officers drawn from 2018 UCR estimates of the number of full-time, sworn police officers in the United States: 686,665 (FBI, 2019c). Using this as a population baseline, we then look to the slope of the fitted nonfatal firearm assault trend knowing that trends in total firearm assault are driven by changes in nonfatal assault. The slope of the fitted trend for the rate of nonfatal firearm assault suggests that, on average, an additional 5.3 officers were victims of nonfatal firearm assault every year between 2014 and 2019. Although it is certainly informative to study national trends, such analyses are likely to be

affected by aggregation bias wherein heterogeneity across smaller ecological units is masked (Kaminski, 2008; Kaminski & Marvell, 2002; Kent, 2010; Peterson & Bailey, 1988). That is, by combining data from across the U.S. to produce national-level estimates, we risk losing sight of important variation at smaller units of analysis. To address this, we first provide a state-level view of the frequency of firearm assaults on police for 2014 to 2019 (see Figure 3). During this 6-year period, states experienced an average of 28.77 firearm assaults or 4.80 firearm assaults per year. Texas (n = 143) and California (n = 112) had the highest number of firearm assault incidents over this period, averaging 23.83 and 18.67 firearm assaults per year, respectively. In contrast, GVA data indicate Delaware and Montana each experienced only two firearm assaults on officers over this period. Next, we calculate 6-year average firearm assault rates for each state to account for variation

in state-level officer populations (see Figure 4). Our results show substantial variation across the United States. Officers in Mississippi, New Mexico, and Alaska experienced the greatest risk of beingassaultedwithfirearmsduringthelast6years.BothMississippi’sandNewMexico’saverage firearm assault rate from 2014 to 2019 were more than 2.0 standard deviations greater than the

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FIGURE 3 Total firearm assaults on police by state, 2014–2019 [Color figure can be viewed at wileyonlineli- brary.com]

FIGURE 4 6-year average rate of firearm assault on police by state, 2014–2019 [Color figure can be viewed at wileyonlinelibrary.com]

national mean (.47 firearm assaults per 1,000 officers); Alaska’s rate was more than 1.5 standard deviations greater.

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At the other extreme, some geographically clustered states showed markedly lower rates of firearm assault on officers over this time period. For example, the 6-year average firearm assault rate in New York and New Jersey was between .5 and 1.0 standard deviation below the national mean, and Connecticut was the only state with a 6-year average rate more than 1.0 standard devi- ation below the national mean. Other geographic regions, however, show more apparent vari- ation between states, such as in the southeastern United States where Florida—compared with

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FIGURE 5 6-year average rate of firearm assault on police by state, 2014–2019 [Color figure can be viewed at wileyonlinelibrary.com]

Alabama, Georgia,and South Carolina—seemsto have beensafer forpolice officers. In theSouth- west, New Mexico stands out as considerably more dangerous than its neighboring states as mea- sured by its average firearm assault rate over the past 6 years. Figure 5 displays the 6-year average firearm assault rate for each state and the District of

Columbia and illustrates each state’s relative position to the U.S. mean over the same time period. Here, we note that although 46 states and the District of Columbia fell within 1.0 standard devi- ation of the mean, there is still meaningful variation among these states. Consider Idaho, which had a 6-year average firearm assault rate of .71 per 1,000 officers. Officers working in Idaho were 1.9 times more likely to be assaulted with a firearm than officers in neighboring Wyoming (0.38 per 1,000), and 2.5 times more likely to be assaulted with a firearm than officers just south of them in Utah (0.28 per 1,000). Even in the Northeast, which we have noted is comparatively safer for officers than the rest of

the United States, we find notable variation across directly neighboring states, like New Jersey, New York, and Pennsylvania. Although geographically proximate, New Jersey’s average rate (.11) andNewYork’saveragerate(.10)are3and3.3timessmaller thanPennsylvania’s(.33).Thereseems to be even more variation when considering the states surrounding outliers like New Mexico, a state with an especially high average firearm assault rate between 2014 and 2019. New Mexico’s rate (1.56) is between 2.0 and 5.6 times higher than its directly neighboring states: Oklahoma (.77), Colorado (.74), Arizona (.62), Texas (.54), and Utah (.28).

4 DISCUSSION

Despite the marked declines in line-of-duty deaths among police officers over the past 50 years (White et al., 2019), violence against police remains a problem that affects public perception, police practice, and policy in the United States (Moule, 2019; Nix et al., 2018; Sierra-Arévalo, 2016; Stoughton, 2016). And although scholars have done well to leverage available data to empirically

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assess violence against police, long-standing definitional and data quality issues continue to ham- per the timely and precise estimation of the most lethal threat officers face: firearm assault. With the help of open-source data on fatal and nonfatal firearm assault on police officers gathered daily bytheGVA,ouranalysisprovidesafreshassessmentofthis long-standingpublicsafetyandpublic policy concern. With regard to national trends in firearm assault, our findings highlight the necessity of includ-

ing nonfatal firearm assault in discussions of danger in U.S. policing. As we have shown, the lion’s share of firearm assaults on officers in the United States are nonfatal. Between 79% and 86% of a given year’s firearm assaults on police do not result in a line-of-duty death. By extension, con- clusions stemming from analyses that employ data on fatal firearm assaults alone will be derived from, on average, only 17% of total firearm assault cases. Indeed, given that the difference between a fatal and a nonfatal firearm can be a matter of luck—weapon caliber, wound location, and total number of gunshot wounds all affect firearm assault lethality (Altheimer, Schaible, Klofas, & Comeau, 2019; Braga & Cook, 2018)—it is vital to recognize that sole focus on fatal firearm assault will inevitably and sorely underestimate the incidence of deadly force against police officers. Additionally, our state-level analyses show marked variation across states and raise important

questions about what underlying structural conditions might explain these patterns. We believe that GVA provides the means for researchers to revisit questions about violence against police with a more nuanced operationalization of the firearm assaults that drive patterns in felonious police deaths. For example, GVA data might be used to reexamine the relationship between vio- lence against police and demographic or structural factors that include racial and ethnic composi- tion, crime rates, poverty, or local political arrangements (Batton & Wilson, 2016; Kaminski, 2008; Kaminski & Marvell, 2002; Kaminski & Stucky, 2009; Kent, 2010). Of particular interest is the relationship of state-level firearm ownership and firearm laws to

firearm assault on police. Although past work has indicated that states with stronger firearm laws have lower rates of firearm homicide, overall (Lee et al., 2017), and that lower rates of civilian firearm ownership are associated with lower rates of police homicide, specifically (Swedler et al., 2015), our results suggest heterogeneity in these factors is unlikely to fully explain state-level vari- ation in rates of firearm assault on police. For example, although Arizona, New Mexico, Utah, and Colorado are all “shall issue” concealed carry permit states with above average levels of firearm ownership (CDC, 2019; Siegel et al., 2017), they vary markedly in their rates of firearm assault on police. Further insight might be gleaned by investigating whether firearm laws and firearm sup- ply differentially affect rates of firearm assault on police depending on other features of states’ legal and criminal justice systems. For example, easily accessible firearms might combine with punitive sentencing laws to increase the likelihood of a suspect using deadly force to avoid arrest and incarceration (e.g., Kovandzic, Sloan, & Vieraitis, 2002). GVA might also be used to sharpen and better integrate research on violence by police with that

onviolencedirectedatpolice(seeFridel,Sheppard,&Zimmerman,2019).Forexample,Legewie’s (2016) quasi-experimental analysis of NYPD stop, question, and frisk (SQF) data finds that the murder of a police officer by a Black suspect is related to increased use of force against Black New Yorkers—no such effects were found for Whites or Hispanics. Using a regression-based approach, Bejan, Hickman, Parkin, and Pozo (2018) found that, across the United States, an increase in felo- nious police deaths is related to a same-day increase in police killings of minority individuals and toadecreaseinthekillingofWhiteindividuals;anincreaseinminorityciviliandeathswasrelated to a decrease in police deaths, whereas an increase in White, non-Hispanic deaths was associated with an increase in police deaths. Neither of these studies speaks to nonfatal firearm assaults on police. Future work on the cyclical, potentially retaliatory nature of violence between police and

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public can be improved by incorporating the nonfatal firearm assaults captured in GVA. These data would allow better measurement of deadly force targeting both police and the public and, by providing a greater number of data points across states, might reveal localized variation in police–public violence that can explain the state-level variation in firearm assault we find in our descriptive analyses. Besides state-level differences in the prevalence of firearm assault on police, GVA data might

also be leveraged to investigate if and to what degree the lethality of firearm assaults varies based on technology, training, and policy. Although research shows that ballistic body armor signifi- cantly reduces the likelihood of an officer dying after being shot in the torso (Liu & Taylor, 2017), there is significant variation in the strength of agencies’ body armor policies (e.g., whether the agency has a “mandatory wear” policy; Liu & Taylor, 2017). Even though researchers will have to contend with likely issues of small sample sizes driven by the relative rarity of firearm assault on police, the combination of GVA data with LEMAS data on body armor policies could be used to better understand how agency- and state-level variation in such policies affect the likelihood of officers dying by way of firearm assault. Additional factors to consider include whether agencies distribute tourniquets or other trauma care technology to officers, the amount of training that officers receive on the use of this equipment, and the distance of firearm assault incidents from a trauma care facility (Circo, 2019; Crandall et al., 2013). The insights and promise of these data notwithstanding, care should be taken to not extrap-

olate too strongly from the slight, upward trend found in nonfatal firearm assaults recorded by GVA from 2014 to 2019. In particular, it is prudent to consider that 2014 marked a shift in pub- lic and political attention to policing after a string of highly publicized police killings, including that of Michael Brown in Ferguson, Missouri; 12-year old Tamir Rice in Cleveland, Ohio; and Eric Garner in New York, New York (Cobbina, 2019; Weitzer, 2015). It is possible that the lower rate of firearm assaults on police in 2014 may be an artifact of measurement error born of either (a) dif- ferential attention paid by news media to incidents of police victimization in 2014 relative to later years or (b) a change in the likelihood that police organizations notify news media of incidents in which officers were shot. Bearing in mind that GVA only extends back to 2014, we cannot rule out the possibility that GVA records more incidents of violence against police precisely because of increased attention to policing and police violence. By the same token, police departments and their administrators may be increasingly likely to notify the news media of violence against their officers as a means to manage public perception or build public sympathy in a tense political cli- mate (Chermak & Weiss, 2005; Surette, 2001).

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Despite these potential measurement issues, GVA provides decided benefits for studying firearm assault on police relative to data sources like LEOKA and NIBRS. Perhaps the clearest of these is the speed with which GVA is released to the public. In contrast to the years-long lags between the collection and dissemination of LEOKA and NIBRS data, GVA is updated on a daily basis. Additionally, each case is uploaded with source URLs that allow for independent verifica- tion of each case and that can be mined for other information such as the time of day a shoot- ing took place, the type of call to which officers were responding, what kind of weapon(s) were used, whether bystanders were also wounded or killed, and so on. Finally, GVA gathers data from across the United States and uses sources from independent news organizations instead of police- generated reports, sidestepping some of the reporting issues long known to affect LEOKA and NIBRS. These notable benefits aside, we also emphasize that open-source data like GVA is not a cure-

all for the data quality issues of policing data writ large, and there are considerations when using GVA that merit careful attention. Data sources on violence against police (or any police-related

SIERRA-ARÉVALO and NIX 13

data)cannot inandofthemselvesguaranteebetterestimatesofsocialphenomena.This isbecause no matter how good our estimates of the frequency of a given phenomenon, any estimate of the rate of that phenomenon is dependent on the accuracy of the denominator—in the case of firearm assault on police, this means the number of officers working in a particular geographic area (see Tregle, Nix, & Alpert, 2019, pp. 19–20). Case in point, Mississippi’s firearm assault rate (2.29 per 1000 officers) must be considered alongside the fact that officer population estimates for Mis- sissippi are highly unreliable. In 2018, for example, scarcely more than 75% of the agencies in Mississippi’s metropolitan statistical areas reported data to the FBI; the percentage of agencies reporting drops to 37.9% when considering cities in nonmetropolitan areas and bottoms out at 20.7% in nonmetropolitan counties. Similar but less grave concerns exist for Indiana and West Virginia (see Appendix C). All told, no measure of firearm assault on police officers, regardless of its precision and accuracy, can make up for unreliability in officer population estimates when trying to calculate a state- or national-level rate. TurningtoGVAitself, twoissuesstandout.First,althoughthemedia-basedreportscollectedby

GVA circumvent some of the reporting shortcomings of existing police-generated data, it remains an open question as to the completeness of these reports and whether there is significant variation across states in reporting of violence against police. Relatedly, just as it is unclear how many cases present in other data sets are not present in GVA, it is unclear how many cases recorded in GVA are not accounted for in existing data sets—a problem compounded by GVA’s relative recency and the lag time associated with the release of LEOKA and NIBRS data. Future research would do well to assess the differences between GVA and existing data with an eye toward how much of thesedifferencescanbeexplainedbyoperationaldefinitionsofviolenceagainstpoliceorreporting error. Second, it is imperative for researchers to recognize the irregularity of these data and to care-

fully and clearly operationalize the phenomena they hope to measure when cleaning and cod- ing them. Our own data cleaning and coding process resulted in 510 cases—nearly 26% of the total data set provided by GVA—being excluded from our analytic sample based on our inclu- sion criteria. Additionally, the scope of GVA’s definition of gun violence also demands specificity in researchers’ operational definitions. Our definition of fatal and nonfatal firearm assaults on police officers allows for precise exclusion of a variety of other instances in which officers were shot, including on-duty firearm suicides, cases of officers accidentally shooting themselves, and “blue-on-blue” cases in which an officer was accidentally shot by another officer. Importantly, our operational definition of firearm assault was informed by existing research on the dangers that officers emphasize on patrol and which drive felonious line-of-duty deaths, as well as the authors’ domain-specific expertise on the untidy, often unclear circumstances in which police work takes place. Even with our efforts to craft a precise set of inclusion criteria, some cases forced imperfect

choices. For example, Sergeant Ron Helus of the Ventura County Sheriff’s Department was shot six times while responding to an active shooter situation. Although five of those shots were fired by the suspect, a coroner’s report found that the sixth gunshot wound was caused by a rifle round fired by another officer. This round struck Helus in the heart and was ruled to be the cause of the deputy’s death (Berman, 2018). We elected to retain this case as an example of a nonfatal shooting by a suspect because, even though we could confirm the deputy was shot by a suspect, we could not conclusively determine whether the deputy would have died had another officer not shot him through the heart. Other researchers coding the same case might well have made a different coding decision. We present this example not to argue that our definitional choices are perfect but

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to lay bare and emphasize the ambiguities intrinsic to reducing complex social realities into even seemingly clear-cut variables like fatal/nonfatal or suspect-/officer-inflicted. Such considerations are especially important in light of the growing use of media- or crowd-

sourced data in research on police. At the end of 2019, two high-profile articles—one employing data from Fatal Encounters, the other from Mapping Police Violence—came under scrutiny for codingerrorsanddebatablecodingdecisions.Besidesclearerrorswhereinasuspectshotbypolice who was coded as “unarmed” was verifiably armed, cases in which a suspect was in possession of atoy/replicafirearmorinwhichasuspectcrashedanddiedwhilefleeingfromofficerswerecoded the same as cases in which officers shot a suspect armed with a real firearm. Once coding errors were amended or cases like those involving toy guns or nontraditional weapons (e.g., screwdriver) were coded as “armed,” the reported effect of exposure to police shooting unarmed Black suspects on the birthweight of Black infants or on the mental health of Black Americans was reduced to statistical nonsignificance (AAAS, 2019; Lozada & Nix, 2019; Nix & Lozada, 2019). We chose to manually check each case in the GVA data set to avoid such issues, and in our opinion, such steps are necessary when using any media- or crowd-sourced data. Thankfully, the richness of the GVA data and the inclusion of online sources with each case allows for researchers to verify cases independently and, perhaps more importantly, to finely tune and apply their inclusion criteria to enhance the precision of their measurements. Even with careful verification of cases, of course, any analysis built on GVA will be limited

to estimates of gun violence. We maintain that studying gun violence against police is critically important given its lethality and its centrality to police training, culture, and operations (Carl- son, 2019; Sierra-Arévalo, 2016, 2019). It bears repeating, however, that studies of gun assaults cannot speak to the far more common cases of simple assault on police (FBI, 2019b). Even stud- ies that capture the larger universe of assaults on police do not accurately measure the more general “resistance” to police examined in prior research (Terrill, 2003). We suggest that these phenomena can both be considered examples of what might be more broadly conceptualized as police–public conflict. Although there are significant challenges to integrating data on violent assaultwiththatondisrespectornonviolentresistance(especially formultiple agencies), it would behooveresearcherstoconsiderhowtointegratemeasuresofpolice–publicconflictthatcanrange from lethal violence to low-level disrespect. Such integration would provide valuable insight into not only how lower level police–public conflict predicts serious violence against police but also how the range of police–public conflict shapes officers’ perceptions of the public and their use of coercive force (Nix et al., 2018, 2020). With these benefits, limitations, and cautions regarding GVA in mind, we conclude with some

concrete suggestions as to how current data collection policies and practices might be amended to improve data used to assess the danger of police work. The long-standing calls for better data on force used by police and the decades of insufficient attempts to remedy the lack of a national database on police use of force are instructive in this regard. First, efforts to understand and address issues of violence by and against police are characterized by a common problem: data that prevent accurate measurement of the phenomenon at hand (Shane, 2018, pp. 128–129). As we have already described, and as discussed at length by others (Hickman & Poore, 2016; Klinger, Rosenfeld, Isom, & Deckard, 2016; Nix, Campbell, Byers, & Alpert, 2017), existing data are insuffi- cient for making precise, reliable estimates of police use of force in the United States. Data on violence against police are drawn from the same or similarly flawed data sources (e.g., UCR and NIBRS), and officer employment data required to quantify the scope of violence by and against police are often drawn from these same sources. Even officer population statistics drawn from the Census of State and Local Law Enforcement Agencies (CSLLEA) or Law Enforcement

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Management and Administrative Statistics (LEMAS) data are of limited utility for timely analyses given that the most recent CSLLEA is over a decade old and LEMAS, which is administered every 3 years, takes between 2 and 3 years to be released once data are collected (Reaves, 2011; see also Banks, Hendrix, Hickman, & Kyckelhahn, 2016).

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Webelievethattheparallelproblemsthatplaguedataonpoliceuseofforceandviolenceagainst police can be addressed through common mechanisms, leveraging (and expanding) efforts to improve shortcomings in the former to also improve the latter. Recognizing that the federal gov- ernment has historically struggled to compile timely and reliable national-level criminal justice data (Alpert, 2016; Alpert, 1948; Zimring, 2017), we see more promise in efforts to compile such data at the state level. These state-level data could then be aggregated to generate regional and national estimates. To date, six states—California, Colorado, Connecticut, North Carolina, Ore- gon, and Texas—have begun collecting and publishing data on police use of force (National Con- ference of State Legislatures, 2018; Shjarback, 2019). Suchefforts,althoughdiscussedprimarilyasameanstogatherdataonviolencebypolice,could

and should be used to gather higher quality data on violence directed at police. California stands apartasastatethatisalreadymovingitsdatacollectionefforts inthisdirection.Currently, theCal- ifornia Attorney General compiles state data on use-of-force incidents resulting in serious injury or death, including fatal and nonfatal firearm assault as well as noninjurious firearm discharges for its annual URSUS report. Importantly, these data capture firearm violence that includes inci- dents wherein officers shoot or shoot at civilians and incidents in which an officer is shot or shot at by a civilian. Thus, California’s data on deadly force captures not only fatal shootings but also nonfatal shootings and noninjurious shootings, both of which far outpace fatalities and are nec- essary to provide holistic understanding of the danger associated with policing in the state (Fyfe, 1988; Nix et al., 2017). In 2018, URSUS recorded 630 incidents, detailing not only demographic information on offi-

cers and members of the public involved but also the circumstances that preceded each incident, number of officers and agencies present, level of civilian resistance, indicators of civilian mental status, and more. That the most populous state in the United States can compile such rich data on hundredsofcaseseveryyearsuggeststhatsimilareffortscanandshouldbereplicatedbyallstates. And although the rollout of new data systems will always come with implementation challenges, the advent of open-source, cloud-based options of the kind that undergird the URSUS system can drastically reduce development costs and sidestep the need for individual agencies to build out their own data collection infrastructure (Williams, 2016). The more states that take the necessary steps to improve their policing data, the better positioned police departments, researchers, policy makers, and the public will be to measure, understand, and address the deeply intertwined issues of violence by and against police.

CONFLICT OF INTEREST STATEMENT The authors confirm that they have no conflict of interest to declare.

ORCID MichaelSierra-Arévalo https://orcid.org/0000-0002-4398-9950

ENDNOTES 1 An analysis of emergency room data by Tiesman et al., (2018) departs from other studies of officer victimization and finds a short-term increase in nonfatal assaults. This trend, however, was found only for 2007 to 2011; the trend then decreased through 2014 to pre-2007 levels.

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2 The threat of being shot on duty looms large in the United States where the supply of civilian-owned firearms was recently estimated at approximately 265 million (Azrael, Hepburn, Hemenway, & Miller, 2017). At least in part because of the large supply of firearms, officers in the United States are victimized by firearms at rates far greater than in European nations like Germany or England (Zimring, 2017, pp. 79–80, 86).

3 We recognize that there are other forms of deadly (i.e. lethal) force that seriously injure or kill police in the line of duty, such as assaults with a knife. Nonetheless, firearms stand are far and away the weapons used most often in fatal attacks on officers. For example, the most recently recorded fatal stabbing in the Officer Down Memorial Page’s (ODMP’s) data occurred in 2017. By comparison, there were 144 fatal firearm assaults between 2017 and 2019 (ODMP, 2020).

4 Other studies, although able to differentiate firearm assaults from simple assaults, were unable to speak to national-level trends because their data were limited to a single city (Brandl, 1996; Brandl & Stroshine, 2003, 2012; Gibbs, Lee, Moloney, & Olson, 2018).

5 As Kuhns et al., (2016) discussed, although a LEOKA summary report on a given year is released nearly 12 months after that year’s conclusion (e.g., the 2013 LEOKA summary report was released at the end of November 2014), the detailed data necessary for more than summary statistics are released 16–18 months after year’s end. As a result, the most recent LEOKA data available for Kuhn et al.’s report, written in 2015 and published in 2016, were for 2012.

6 Other open-source data sources that draw on media reports, submissions from the public, and public records requests include The Counted (Swaine, Laughland, Lartey, & McCarthy, 2016), Mapping Police Violence (Sinyangwe, Mckesson, & Paccknett Cunningham, 2020), and Fatal Encounters (Burghart, 2017).

7 The latter half of November 2019 and all of December 2019 were provided to the authors by GVA after the begin- ning of 2020. An additional data pull from the GVA database was performed on January 10, 2020 to check for additional cases from November and December 2019 that had not yet been found. No additional cases in those months were identified by GVA in this final data pull.

8 We include cases in which officers were grazed by a bullet, shot in their ballistic vest or other protective equip- ment (e.g., ballistic shield or ballistic helmet), and cases in which officers’ radios, duty belts, boots, secondary weapons, etc., were hit by bullets, bullet fragments, or shrapnel (e.g., glass or metal shards). We include such cases because they are the outcome of suspects firing rounds at officers where the difference between minimal and significant injury is exceedingly slim.

9 In cases of a struggle over a firearm, we included cases of officers being shot in a struggle over a firearm even if it was unclear whether it was the suspect or the officer who pulled the trigger of the firearm. In cases where the trigger-puller was unclear, we then considered whether other officers opened fire. If other officers opened fire and it was not explicitly stated the officer was hit by a round fired from the firearm over which a strug- gle occurred, we excluded the case on the grounds that we could not rule out a “blue-on-blue”/“friendly fire” incident.

10 We focus on on-duty officers to more accurately estimate the prevalence of nonfatal firearm injury to officers in the course of normal policing activities as opposed to cases in which off-duty officers happen to be victims (e.g., victim of a robbery) or those in which they intervene in situations outside their official duties.

11 These 1,467 cases represent individual officers assaulted by gunfire during 1,185 incidents (1.24 officers per inci- dent): 510 cases were removed from our analytic sample based on manual verification and our inclusion criteria; 15 cases of an officer being shot that were not present in GVA data were found during independent verification of cases (11 of which were retained); 6 cases were coded as “unverifiable” and excluded from our sample when no online sources could be found to verify GVA-listed information; and 8 cases were coded as “unclear” and excluded from our sample because the available news sources did not provide sufficient information to defini- tively discern who shot an officer. One such case described an officer who was nonfatally shot in the hand while making entry into a residence to serve a search warrant. News sources indicate a suspect pointed a firearm at officers and was shot by officers but do not stipulate the suspect ever fired their weapon. Another case involved an officer who was shot in the foot while struggling with a suspect in a bar. A second officer present at the scene fired multiple rounds and the suspect’s firearm was discharged once. News sources covering the incident did not state whether the officer was struck by a round from the suspect’s weapon or that of the other officer. See Appendix A for a frequency table of reasons for case exclusion. Note that these frequencies do not sum to 510 because cases could be excluded for multiple reasons (e.g., a federal law enforcement officer who accidentally shot themselves or an off-duty deputy who committed suicide).

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12 Law enforcement officers are defined by the FBI as “individuals who ordinarily carry a firearm and a badge, have full arrest powers, and are paid from governmental funds set aside specifically for sworn law enforcement representatives” (FBI, 2019c, para. 1). Our estimates exclude agencies that report they are “covered by” another law enforcement agency to avoid double-counting officers. Our chosen denominator—population of local and state officers—has strengths and limitations. To the former, this denominator is well suited to estimate the aver- age risk of firearm assault faced by sworn state and local police officers. Because it does not require assumptions about the type of interaction (if any) that precedes a firearm assault, we are able to include the greatest possible number of firearm assaults in our analysis. Conversely, our denominator does not differentiate officers based on their assignment (e.g., administrative vs. patrol vs. SWAT), obfuscating variation in officers’ exposure to the risk of firearm assault and preventing estimation of assignment-specific risk profiles. Other denominators (all with their own assumptions and limitations) could be used, including the rate of firearm assault vis-à-vis violent crime, firearm crime, or arrest. An arrest-based denominator might, for instance, be preferred for estimation of the risk faced by patrol officers since they are responsible for most arrests. On the other hand, appropriate use of an arrest-based denominator would necessitate excluding firearm assaults that did not occur during an arrest— a problematic choice since, between 2014 and 2018, only 5% of felonious officer deaths occurred during an arrest (FBI, 2019d).

13 We selected a quadratic imputation function after benchmarking it against two other imputation models. The first,a laggedvalueimputation,assumesnoyear-to-yearvariationinthestate’sofficerpopulationandduplicates the 2019 value for the prior year of data (e.g., state-year population2019 = state-year population2018). The second, a linear regressionimputation, calculates a predicted state-year population value for everystate-year by regressing state officer population on year. The quadratic imputation is the same as the linear imputation save for the addition of a year2 term that allows for variation in the slope of the regression function over time. For each of these imputation methods, we then compared the predicted population estimates to the original values present inthePEdataandcalculatedtheroot-mean-squarederror(RMSE)foreachmodeltoassessthemagnitudeofthe error between estimated values and the PB population estimates. Of our three tested imputation approaches, the quadratic regression imputation model had the smallest RMSE values. See Appendix B for more information.

14 See Appendix C for an illustration of this data quality issue using 2018 UCR data. 15 As we have noted, Mississippi’s rate was deemed unreliable and its rate should be interpreted with this in mind. Appendix D shows new estimates for Mississippi and two other states using more reliable (but dated) estimates from the Census of State and Local Law Enforcement Agencies (CSSLEA; Reaves, 2011). Even using a larger, more reliable denominator for Mississippi only caused its ranking to fall from first to third in terms of its average rate of firearm assault on police. As such, we can be reasonably certain that Mississippi is one of the more dangerous states in which officers work.

16 Potential measurement bias for 2014 should also be considered alongside the fact that estimated trends for a limited time frame are sensitive to the inclusion or exclusion of individual years. If, for example, 2014 is dropped and a new linear trend line is imposed on estimates for nonfatal firearm assaults from 2015 to 2019, the trend is negative (B = –.573), indicating that, on average, 3.9 fewer officers per year were nonfatally assaulted with a firearm during this period (see Appendix E).

17 Garner, Hickman, Malega, and Maxwell (2018) is one exception that used LEMAS and UCR data to provide a national estimate of police force for 2012. Because their analytic sample was composed of only 1,646 agencies, however, their national estimate required significant imputation that could not provide reliable agency- or state- level estimates of force. Furthermore, such techniques do not address the fact that timely estimates of police use of force with these data are impossible given that LEMAS is released every 3 years.

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AUTH OR BIOGRAPH IES

Michael Sierra-Arévalo is an assistant professor in the Department of Sociology at the Uni- versity of Texas at Austin. His research employs both qualitative and quantitative methods to explore police culture, legitimacy, and behavior. Examples of this research have been pub- lished in the Proceedings of the National Academy of Sciences, Criminology, and Law & Society Review.

Justin Nix is an associate professor in the School of Criminology and Criminal Justice at the University of Nebraska Omaha, where his research focuses on police legitimacy, procedural justice, and officer-involved shootings. He earned his PhD in Criminology and Criminal Jus- ticefromtheUniversityofSouthCarolinain2015.HisrecentworkappearsinJusticeQuarterly, Journal of Research in Crime and Delinquency, and Journal of Experimental Criminology.

How to cite this article: Sierra-Arévalo M, Nix J. Gun victimization in the line of duty: Fatal and nonfatal firearm assaults on police officers in the United States, 2014–2019. Criminol Public Policy. 2020;1–26. https://doi.org/10.1111/1745-9133.12507

APPENDIX A: FREQUENCY TABLE OF REASONS FOR CASE EXCLUSION

Reason for Case Exclusion Cases Not Active-Duty State or Local Law Enforcement 129 Federal Employees 60 Not a Law Enforcement Officer 24 Retired or Off-Duty 102 Self-Inflicted 188 Suicide 45 Blue on Blue 101 Officer Lied or Committed Crime 7 Duplicate Case 18 Unverifiable Case 6 Unclear Circumstances 8

Notes. Case total does not sum to number of excluded cases (n = 510) because cases could be excluded for multiple reasons. For example, 22 of the 60 federal employees would have been excluded for at least one of the other reasons listed in the main article.

SIERRA-ARÉVALO and NIX 23

APPENDIX B: POLICE EMPLOYEE DATA, 2013–2018, AND TWO IMPUTED ESTIMATES FOR 2019

State 2013 2014 2015 2016 2017 2018a 2019b 2019c

Alabama 9492 7698 8801 6978 5235 10661 11926.8 7807.5 Alaska 1312 1258 1227.4* 1219 1247 1286 1357.7 1243.8 Arizona 11955 12663 12807 12474 12862 12756 12497.4 13013.1 Arkansas 5893 5917 5860 5943 5976 6743 7185.5 6506.3 California 76912 77190 77402 77849 78740 79141 80051.7 79496.5 Colorado 11981 10478 11835 11972 11402 12512 13145.9 12253.1 Connecticut 8517 8634 7918 7944 7849 7792 7782.6 7513.6 Delaware 2339 2194 2239 2283 2325 2315 2424.7 2314.2 District of Columbia

4579 4535 4264 4174 4228 4393 4558.9 4168.1

Florida 42346 36054 36287 34436 44593 42880 52508.1 42076.3 Georgia 21498 19881 19690 20223 24044 27250 32969.7 26275.9 Hawaii 2886 3006 2939 2995 2951 2844 2731.1 2904.9 Idaho 2707 2725 2766 2770 2851 2894 2964.7 2917.2 Illinois 9420 24358 13295 23897 25215 26744 27547.3 30467.5 Indiana 7054 8763 8169 5539 7794 9349 10142.8 8371.8 Iowa 5035 5003 5183 5241 5252 4716 4460.0 4992.7 Kansas 6174 6804 5864 6559 6874 6575 6828.5 6766.0 Kentucky 7092 7101 6321 3709 7659 7386 9516.2 6597.9 Louisiana 8228 10037 9118 15274 15418 13960 15087.9 17101.7 Maine 2243 2285 2280 2311 2322 2349 2363.7 2365.5 Maryland 17312 15893 17379 17158 15535 15410 14373.0 15367.3 Massachusetts 16293 16644 17033 16686 16511 16717 16287.9 16784.7 Michigan 16909 17028 16996 17142 17081 17229 17259.5 17254.7 Minnesota 8743 8879 9211 9413 9689 9927 10213.2 10165.5 Mississippi 2903 2524 2415 2506 2884 2700 3158.1 2670.9 Missouri 14400 14458 14347 14572 14688 14428 14474.0 14587.7 Montana 1684 1435 1853 1612 1917 1839 1988.5 1921.3 Nebraska 3497 3644 3722 3523 3345 3797 3711.9 3628.4 Nevada 5359 5501 6299 6341 6789 6273 6159.6 6941.3 New Hampshire

2612 2583 2632 2699 2764 2838 2958.5 2862.0

New Jersey 24460 29668 30272 30811 31341 31817 29969.8 33962.5 New Mexico 353 3341 977 3176 2933 2710 2262.5 3524.3 New York 58528 58370 60821 62484 62433 62327 62487.2 64111.9 North Carolina 23258 23980 23775 24006 24070 24004 23794.6 24271.9 North Dakota 1471 1593 1698 1761 1749 1776 1723.1 1880.3 Ohio 13147 9616 13256 12765 13460 14976 17131.1 14888.6

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State 2013 2014 2015 2016 2017 2018a 2019b 2019c

Oklahoma 7841 6897 7517 8561 8828 8893 9904.7 9299.2 Oregon 5959 6356 6538 6237 6767 6553 6514.7 6791.9 Pennsylvania 25278 24611 25525 25191 25839 25505 25873.5 25773.3 Rhode Island 2434 2497 2441 2469 2504 2494 2507.9 2508.1 South Carolina 11521 9552 11470 11792 8380 11553 11139.6 10407.9 South Dakota 1529 1669 1501 1659 1780 1771 1890.1 1821.6 Tennessee 16595 16774 16613 16766 16736 17363 17656.4 17195.7 Texas 46059 38512 45451 46521 43699 44857 45992.1 45245.3 Utah 3236 4879 4865 4902 4988 5106 4429.9 5634.1 Vermont 1158 1194 1149 1155 1196 1392 1516.2 1325.5 Virginia 18756 18858 18769 18953 19036 19258 19475.8 19261.1 Washington 10341 10311 10341 10507 10718 10820 11115.2 10884.5 West Virginia 3534 3671.6* 3806 3679 3669 3592 3399.0 3677.8 Wisconsin 12504 12776 12772 12846 12870 12704 12534.6 12880.9 Wyoming 1605 1322 1294 1253 1243 1490 1736.2 1282.5

aWe also tested the fit of a model that assumed no year-over year change between 2018 and 2019 (RMSE = 2015.18). Although not displayed here, its imputed values for 2019 equal those listed in the “2018″ column for each state. bImputed values calculated using the coefficients for year and year2 from a quadratic regression model used to estimate officer population at the state level (RMSE = 948.78). The values in this column are the values selected as our final imputation values for 2019. cImputed values calculated using the coefficient for year from a linear regression model used to estimate officer population at the state level (RMSE = 1105.73). *Entries for WV (2014) and AK (2015) reflect imputed estimates derived from a quadratic regression equation regressing officer population on year and year2.

APPENDIX C: PERCENTAGE OF AGENCIES IN EACH STATE THAT SUBMITTED UCR DATA IN 2018

State

Metropolitan statistical areas

Cities outside metropolitan areas

Nonmetropolitan counties

Alabama 94.3 93.1 91.9 Alaska 100.0 97.1 100.0 Arizona 99.3 96.3 100.0 Arkansas 99.7 91.1 84.9 California 99.9 100.0 100.0 Colorado 95.1 94.9 91.5 Connecticut 100.0 100.0 100.0 Delaware 100.0 n/a n/a District of Columbia 100.0 n/a n/a Florida 99.9 95.0 100.0 Georgia 96.6 88.7 93.5 Hawaii 100.0 n/a 100.0

SIERRA-ARÉVALO and NIX 25

State

Metropolitan statistical areas

Cities outside metropolitan areas

Nonmetropolitan counties

Idaho 100.0 95.7 98.3 Illinois 92.1 83.2 89.7 Indiana 84.4 59.0 51.4 Iowa 90.7 100.0 97.0 Kansas 83.8 95.4 96.6 Kentucky 99.7 97.5 100.0 Louisiana 97.2 80.8 96.7 Maine 100.0 100.0 100.0 Maryland 100.0 100.0 100.0 Massachusetts 97.5 100.0 98.8 Michigan 98.8 98.0 99.2 Minnesota 98.6 99.7 100.0 Mississippi 75.9 37.9 20.7 Missouri 99.8 98.5 100.0 Montana 100.0 100.0 99.3 Nebraska 99.8 92.9 84.6 Nevada 100.0 100.0 100.0 New Hampshire 99.2 93.6 93.4 New Jersey 100.0 n/a n/a New Mexico 92.5 96.0 97.4 New York 99.7 97.4 100.0 North Carolina 89.1 86.2 95.3 North Dakota 100.0 99.8 100.0 Ohio 90.1 80.9 86.0 Oklahoma 99.9 99.3 97.9 Oregon 99.3 95.0 84.3 Pennsylvania 99.7 98.9 100.0 Rhode Island 100.0 n/a n/a South Carolina 98.8 97.0 96.3 South Dakota 99.1 96.0 82.8 Tennessee 99.9 100.0 100.0 Texas 98.2 93.4 98.4 Utah 99.4 89.6 90.9 Vermont 100.0 100.0 100.0 Virginia 99.9 97.8 100.0 Washington 98.6 92.8 100.0 West Virginia 81.9 54.3 86.7 Wisconsin 98.2 99.5 100.0 Wyoming 81.3 91.9 89.1

26 SIERRA-ARÉVALO and NIX

APPENDIX D

6-year average rate of firearm assault on police by state, 2014–2019 [Color figure can be viewed at wileyon- linelibrary.com]

APPENDIX E

National rate of firearm assault on police, 2015–2019 [Color figure can be viewed at wileyonlinelibrary.com]

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