The near repeats of gun violence using acoustic triangulation data

Mazeika, David; Uriarte, Lorena

doi:10.1057/s41284-018-0154-1

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…If using these more robust error metrics, the use of GDT appears more reasonable, although we cannot say what is an acceptable amount of error for the typical types of crime mapping applications to take place within a police department. While we did not conduct analysis of how such error resulted in loss in efficiency in typical analyst products (Mazeika & Uriarte, 2018;Zandbergen et al, 2012), it seems not an unreasonable amount of error to live with for crime analysts. Given it is popular currently to aggregate crime incidents to street segments to conduct analysis (Weisburd et al, 2004;Wheeler et al, 2016), such aggregation will likely make such error immaterial.…”

Section: Discussionmentioning

confidence: 98%

“…For other examples of analyses using GDT data, Loeffler & Flaxman (2018) and Wheeler (2017) evaluate spatial predictive models using the same D.C. GDT data as others. Mazeika & Uriarte (2018) used GDT data in Trenton, New Jersey, to examine near repeat patterns for gun violence, and found that the use of GDT data led to more near repeat patterns identified relative to calls for service.…”

Section: Shotspotter and Gunshot Detection Technologymentioning

confidence: 99%

“…In the present analysis we test the accuracy of address-based geocoding for shooting incidents relative to the location recorded by acoustic gun-shot detection technology (GDT), ShotSpotter, in Wilmington, North Carolina. Prior analyses have focused on the ability of GDT to record shooting incidents that are not reported to the police, and thus potentially provide more accurate estimates of the incidence of shootings (Carr & Doleac, 2016;Carr & Doleac 2018;Irvin-Erickson et al, 2017;Mazeika & Uriarte 2018). Although it is controversial whether those non-reported incidents represent actual shootings, or whether they are the result of false positives detected by the technology (Drange, 2016;Ratcliffe et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Testing the Spatial Accuracy of Address-Based Geocoding for Gunshot Locations

Wheeler

Gerell

Yoo

2020

The Professional Geographer

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We assess the positional accuracy of address based geocoding of shooting incidents relative to the location recorded via acoustic gunshot detection technology. This provides a test of the accuracy of typical address based geocoding methods used in crime analysis, as well as provides evidence for how much accuracy one gains when using sensors. Examining over 1,000 shooting incidents in Wilmington, North Carolina, we find that the majority of address-based incidents are quite accurate, on average within 60 feet of the actual location (using a street centerline geocoder), or within 90 feet (using Google rooftop geocoding). However, based on the incident narrative we identify a subset of transcription errors in over 10% of the cases that increases the distance between the true shooting location and that geocoded using address data. This suggests mechanisms to prevent human errors may be more frugal than those relying on sensors in geocoding shooting incidents.

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Section: Discussionmentioning

confidence: 98%

Section: Shotspotter and Gunshot Detection Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Testing the Spatial Accuracy of Address-Based Geocoding for Gunshot Locations

Wheeler

Gerell

Yoo

2020

The Professional Geographer

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“…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].…”

Section: Spatial Clustering Of Gun Violencementioning

confidence: 90%

“…Near repeat analysis enables us to identify spatial and temporal repentance of criminal incidents. To date, near repeat analysis of gun violence has largely relied on the use of police-reported data, and this article is one of the first known attempts to apply it to GDT data [29]. Further, analyzing GDT data relative to calls for service should highlight areas where gun violence is chronically underreported, a widespread problem (especially in low-income neighborhoods) plaguing crime analysis [41].…”

mentioning

confidence: 99%

Comparative Analysis of Firearm Discharge Recorded by Gunshot Detection Technology and Calls for Service in Louisville, Kentucky

Renda

Zhang

2019

IJGI

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Gunshot detection technology (GDT) has been increasingly adopted by law enforcement agencies to tackle the problem of underreporting of crime via 911 calls for service, which undoubtedly affects the quality of crime mapping and spatial analysis. This article investigates the spatial and temporal patterns of gun violence by comparing data collected from GDT and 911 calls in Louisville, Kentucky. We applied hot spot mapping, near repeat diagnosis, and spatial regression approaches to the analysis of gunshot incidents and their associated neighborhood characteristics. We observed significant discrepancies between GDT data and 911 calls for service, which indicate possible underreporting of firearm discharge in 911 call data. The near repeat analysis suggests an increased risk of gunshots in nearby locations following an initial event. Results of spatial regression models validate the hypothesis of spatial dependence in frequencies of gunshot incidents and crime underreporting across neighborhoods in the study area, both of which are positively associated with proportions of African American residents, who are less likely to report a gunshot. This article adds to a growing body of research on GDT and its benefits for law enforcement activity. Findings from this research not only provide new insights into the spatiotemporal aspects of gun violence in urban areas but also shed light on the issue of underreporting of gun violence.

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