Police Technology

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What is Police Technology?

What is the Evidence on Police Technology?

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Seattle Police car image courtesy of Flickr user andrewasmith and used under a Creative Commons license.

What is Police Technology?

Police technology can cover a number of different innovations and advances in policing in recent decades. Some of these technologies, such as computerized crime mapping, have been important in advancing effective strategies such as hot spots policing. Additionally, advances in DNA technology have been important for improving the ability of police to solve violent and property crimes. Not all police technologies have been well-evaluated. As Koper and colleagues (2009: 5) conclude “there is a need for more evaluation research to provide police with better evidence on which technologies are most valuable and cost-effective for law enforcement uses.” We review the available evidence below on four types of police technologies that are currently of interest to many law enforcement agencies: body-worn cameras, ShotSpotter, police drones, and license plate readers (LPR).


What is the Evidence on Police Technology?

Police technology is listed under “What do we need to know more about?” on our Review of the Research Evidence because for the technologies listed below, research is in its infancy, particularly research related to fairness and crime control effectiveness.

Body-Worn Cameras

Body worn cameras have quickly become a prominent part of discussions about police reform.  Research on the effects of body-worn cameras on police use of force, complaints, citizen behavior is beginning to accumulate and studies in progress will provide more data on the effects of cameras on citizen perceptions of police legitimacy, officer behavior, and crime.  As Lum et al. (2015) caution in a recent report, it is premature to reach strong conclusions on the effects of body worn cameras.  The Center for Evidence-Based Crime Policy body-worn camera page will continue to be updated as new research is added to the evidence base.  There is suggestive evidence to date that officers wearing cameras may be less likely to receive complaints.  The evidence on police use of force is less conclusive, as is the data on whether citizens act differently when officers are wearing cameras.

ShotSpotter

ShotSpotter or other Acoustic Gunshot Locations Systems are designed to quickly locate the location of a gunshot after shots are fired and then alert police about the gunfire. The idea is that police could more quickly respond to gunfire incidents to make arrests and the system could potentially act as a deterrent to gunfire as the risk of detection increases. Mazerolle and colleagues (1999) evaluated the systems in use in Redwood City, CA (ShotSpotter) and Dallas, TX (SECURES) and found that the systems could fairly accurately identify the location of gunfire. The systems could increase officer workload because many gunshots were previously unreported to police. The systems also did not tend to help officers make more arrests, as shooters tended to quickly leave the scene. Mazerolle et al. suggest the technology could be useful as a problem solving tool, as it could aid police efforts to analyze high gunfire locations.

More recently, Mares and Blackburn (2012) evaluated the effectiveness of a gunfire location system in St. Louis. They found the system may be related to a decrease in gun-crime related calls for service, but not in reported gun incidents. The authors conclude that the decrease in calls for service is not necessarily a positive development, as it may suggest that residents are less likely to call in incidents because they think the system will take care of it for them.  Choi, Librett, and Collins (2014) found that a ShotSpotter system in southeastern Massachusetts was associated with decreased police response time for gunshot incidents, but there was no improvement in gun-related case resolution (e.g., making arrests or prosecuting suspects).

Unmanned Aerial Vehicles (drones)

While the police use of drones has become increasingly popular (and controversial), there is no evaluation research on the effectiveness or cost-effectiveness of the use of drones. While the technology may enhance the ability of police to do surveillance work versus helicopters or other more traditional approaches, it is not clear whether any benefits that do exist will outweigh the high cost of the devices, both in terms of monetary cost and potential lowered citizen perceptions of legitimacy as a result of concerns about civil liberties.

License Plate Reader(LPR)

Lum and colleagues (2010) tested the effectiveness of license plate readers (LPR) in deterring crime and automobile crime in a two-jurisdiction randomized experiment. License plate readers take images of vehicle license plates and compare them to a database of information on vehicles associated with particular crimes and offenders. The hot spot approach to LPR use was not associated with a significant crime decline. Koper and colleagues (2013) found that LPR in Mesa, AZ led to an increase in the number of plates scanned (compared to checking plates manually) and led to increases in the number of hits for stolen vehicles, arrests for stolen vehicles, and recoveries of stolen vehicles (see Taylor et al., 2012). There was some evidence of residual deterrence effects on drug crime in the locations where LPR was used.

Evidence-Based Policing Matrix

 

Police Technology Studies from the Evidence-Based Policing Matrix:

Author
Intervention
Officers using technology in hot spots patrols did not have a statistically significant overall effect on crime, but higher dosage locations saw crime reductions
grey-circle
VR

MP

G

P
Koper et al. (2013) Use of license plate readers has significant impact on drug crimes, other crimes (including auto crimes) had more mixed or nonsignificant results
grey-circle
VR
MP
F
P
Lum et al. (2010) Use of license plate readers mounted on patrol cars in autotheft hot spot areas not associated with declines in auto crime or crime generally in two jurisdictions
empty
VR
MP
G
P

Resultfull-circle =successful intervention; grey-circle = mixed results; empty = nonsignificant finding; backfire = harmful intervention

Rigor: M = moderately rigorous; R = rigorous; VR = very rigorous

X-axis: I = individual; G = group; MP = micro place; N = neighborhood/community; J = jurisdiction

Y-axis: F = focused; G= general

Z-axis: R = reactive, P = proactive, HP = highly proactive