The United States is experiencing a decades-long epidemic of gun violence dating back to the 1970s [
1]. In recent years, more than 30,000 people die each year from gun violence, with approximately one-third attributed to homicide and two-thirds to suicide, and nearly 70,000 more are non-fatally injured [
2,
3,
4]. The risk of this violence is disproportionately distributed among different demographic groups and across geographic entities at different levels. For example, firearm homicide is the leading cause of death for African American men aged 15–34 with a homicide rate up to 20 times higher than white males, and is the second leading cause of death for African American women aged 15–24 [
1]. Geographically, the mortality rate incurred by gun violence varies by state from as low as 3.4/100,000 in Massachusetts to 23.3/100,000 in Alaska [
3]. At the local level, scholars have long observed that criminal incidents, especially violent crime, are highly concentrated in disadvantaged inner-city neighborhoods, which are disproportionately resided by minority and low-income residents [
5,
6,
7].
Despite the clear indications of a nationwide crisis of gun violence, research is limited on the subject due in part to limited funding at the federal level since the 1990s [
8]. Even when researchers are able to investigate this problem, incident-level data on gun violence are incomplete as it only reflects cases reported to law enforcement from 911 calls for service. In many areas of the country, gun violence is subject to chronic underreporting, especially in dilapidated urban neighborhoods, to the extent that nearly 90% of incidents are never reported, further hindering the reliability and accuracy of spatial analysis and mapping of crime that relies on police-recorded data on gun violence [
9]. In two major American cities for example, as few as 12% of gunfire incidents are actually reported to police [
9]. In addition to underreporting, 911 calls for service reporting gun violence are subject to the problem of low accuracy because callers oftentimes have limited knowledge about the exact location of a gunshot incident [
10]. New technologies such as gunshot detection technology (GDT), however, provide more comprehensive and accurate locational information on gunshot incidents, providing new insights into the spatiotemporal aspects of gun violence in urban areas. This article seeks to investigate the spatial and temporal patterns of gun violence and the underreporting problem of firearm discharge by comparing newly released data collected from GDT and 911 calls for service in Louisville Metro, Kentucky.
1.1. Gunshot Detection Technology
Gunshot detection technology (GDT) is an application of acoustic detection and triangulation to detect and locate sound generated by the muzzle blast of firearm discharge using a network of acoustic sensors installed over an area [
11]. Since introduced in the early 1990s, a growing proportion of local law enforcement agencies, especially large police departments, across the U.S. have adopted or planned to implement GDT as a strategy to address urban gun violence [
10,
11,
12]. The Louisville Metro Police Department (LMPD) in Louisville, KY is one such agency that implemented a GDT system provided by the California-based company ShotSpotter in June of 2017. The impact of GDT on actual law enforcement effectiveness is not thoroughly understood yet, but some research suggests that GDT can significantly improve police dispatch and response time [
13]. Under a problem-oriented policing framework, GDT presumably helps accurately identify firearm discharge hotspots, thus contributing to the analysis of gunfire problems and enhancing police response efforts [
14].
The accuracy and sensitivity of GDT to detect actual gunfire has been shown to vary spatially and temporally, with better performance at nighttime and with increased density of sensors [
15]. Sensitivity also varies based on the type of firearm discharged, i.e. better performance with larger caliber weapons [
11]. Early field tests for civilian versions of this technology indicated that discharges from shotguns, handguns and rifles were detected 90%, 85% and 63% of the time respectively, and identified the location within an average margin of error of 41 feet [
14]. Similarly, field-testing in an urban environment for one manufacturer revealed 83% and 76% detection for handguns and rifles respectively [
16]. False positives, or detected events that are not actually the result of a firearm discharge, and false negatives, actual firearm discharge not detected, are key considerations when interpreting and utilizing this type of data. Heavily noisy environments, such as real-world urban settings, have been shown to affect GDT effectiveness where up to 9% of actual gunfire is not detected and approximately 25% of non-gunfire events with a similar acoustic signature, i.e. balloon popping and hand clapping, were falsely identified as gunfire [
17]. This evidence supports the observation that GDT systems appear to perform better during the overnight hours when other environmental background noise is lower. Nevertheless, manufacturers are constantly improving their technology and detection algorithms, and GDT has the potential to improve monitoring of urban firearm discharge over traditional methods, thus benefiting law enforcement activity and criminological analysis.
1.2. Spatial Clustering of Gun Violence
Studies to date of gun violence in urban areas show a high degree of spatial clustering, particularly in micro places over time [
5,
18]. These micro places, such as addresses, facilities, or street segments represent only a small proportion of all places in a specific urban community, but account for most of the criminal activity in urban areas [
7,
19,
20]. Weisburd [
7] describes the uneven geographic distribution of crime as the “law of crime concentration at place.” For example, based on data collected for nearly three decades, Braga, Papachristos and Hureau [
5] observe epidemic levels of spatial concentration and temporal persistence of gun violence in Boston—over 50% of all gunshot incidents occurred in less than 3% of the city’s street segments and blocks. Larsen, Lane, Jennings-Bey, Haygood-El, Brundage and Rubinstein [
6] identify similar levels of spatiotemporal concentration of gun violence in the city of Syracuse, New York and find that the intensity of gun violence is positively correlated with neighborhood sociodemographic factors including segregation and poverty. The clustering nature of gun violence across space and over time provides opportunities for hot spots mapping and prediction of future crime [
21].
When evaluating spatiotemporal clustering in more detail, the notion of near-repeat victimization provides a useful way to refine pattern identification for crime phenomena. While the use of near-repeat analysis is well tried in relation to burglary, its application to gun violence is less common [
22,
23,
24,
25]. The near-repeat theory suggests that when an originating event occurs, like unlawful firearm discharge, the risk of a subsequent event nearby increases for a short period of time thereafter [
26,
27]. For example, studies from two major American cities revealed that the risk of near-repeat incidents of gun violence increase by 33–35% within one city block for 2 weeks following the originating incident [
22,
26]. Other research echoes this finding of increased near-repeat incidents within a city block, and further describes a day/night variance with limited risk of near-repeat during the day and significant increase during nighttime hours [
28]. The latest research identifies evidence of spatial and temporal repeat of gun-related violent crime based on GDT recorded data [
29]. Research has also shown evidence of near-repeat phenomenon for armed robbery—increased risk within three city blocks and 1 week of the initiating event, and it is possible to link known long-term hotspots to patterns of increased near-repeat events [
30].
Social disorganization theory in the literature of environmental criminology offers a valuable theoretical framework for explaining spatially the patterns of crime. This theory suggests that social and environmental factors including socioeconomic deprivation, family disruption, residential mobility, and ethnic heterogeneity all contribute to the geographical concentration of crime in urban areas [
31,
32,
33]. Guided by social disorganization theory, Larsen, Lane, Jennings-Bey, Haygood-El, Brundage and Rubinstein [
6] find gun violence is spatially correlated with higher rates of poverty and segregation in Syracuse, New York. Moreover, the lack of informal social control (or cohesion) and collective efficacy may exacerbate the violent level of poverty-stricken neighborhoods and explain the chronic underreporting issue of gun violence in American cities [
34]. In light of the fact that crime-ridden neighborhoods are often highly segregated and disproportionately dominated by African Americans, research has documented remarkable racial disparities in citizen confidence in police, namely that black residents are half as likely to have a positive view of local police and are about 24% less likely to report crime [
35,
36]. Furthermore, issues of desensitization by frequent exposure may also affect residents’ behavior and the likelihood of reporting crime [
37,
38]. This can be explained by the broken window theory that posits residents’ lack of care and participation in crime prevention endeavors can aggravate criminal behavior and violence [
39].