5-2 Project one
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Southern New Hampshire University *
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CJ 305
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Information Systems
Date
Dec 6, 2023
Type
docx
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5
Uploaded by LieutenantOwlMaster811
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5-2 Project One
Brandon White
Southern New Hampshire University
CJ 305
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Predictive policing is a technique law enforcement uses to identify potential criminal
activity through mathematical models and other analytical methods. Two types of predictive
policing are in use: place-based and person-based. Place-based predictive policing uses
algorithms to analyze data sets and predict locations where crimes are likely. Person-based
predictive policing generates a list of individuals likely to commit a crime based on analyzed
data sets (Diaz, 2019). The use of crime maps is also helpful in predictive policing as it identifies
times and places where crimes are likely to occur. These maps show the type of crime and its
location, allowing law enforcement to predict the most frequent crimes committed in certain
areas based on the hotspots on the map. It is important to note that crime reports submitted for
data analysis were used to predict future criminal activities.
Law enforcement agencies can access various tools that aid their work: surveillance
footage, facial recognition, automated fingerprint identification systems (AFIS), and DNA
databases. Using real-time footage from surveillance and facial recognition tools, law
enforcement can identify individuals involved in a crime or locate suspects. Additionally, AFIS
and DNA databases allow for comparisons of fingerprints and DNA samples, enabling the
identification of known or unknown suspects and linking them to current or cold cases. These
vital tools are critical in safeguarding communities and bringing perpetrators to justice.
Using data analytics in predictive policing has several advantages, such as enhancing
crime prevention and promoting well-informed decision-making. Law enforcement agencies can
make informed decisions that benefit society by predicting potential threats, identifying
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offenders, exposing vulnerabilities within communities, and recognizing crime patterns.
Nevertheless, predictive policing may have unintended consequences, including discriminatory
practices. Consequently, these systems may perpetuate biased policing practices, resulting in
over-policing of communities of color or manipulated data that misrepresents crime rates. Diaz
(2019) cautions against these issues.
Law enforcement can benefit from body-worn cameras and automated license plate
recognition (ALPR) technologies. Body-worn cameras provide officers with a compact and
reliable tool to systematically and automatically document their field observations and
interactions. This includes gathering video footage during stop-and-frisk procedures, property
searches, car stops, pursuits, witness interviews, summons issuances, and arrests. ALPR is a
system consisting of cameras and supporting software that captures license plate information and
compares it to a real-time database of wanted criminals or individuals of interest. This
technology can track drivers who frequent sensitive locations such as healthcare centers,
immigration clinics, firearm stores, labor union offices, protests, or religious centers.
In June 2020, Santa Cruz, California, made history as the first city in the United States to
ban predictive policing. The city council voted unanimously to outlaw the practice due to
concerns over its contribution to racial inequality (Guariglia, 2020). The primary apprehension
surrounding predictive policing is that it perpetuates the discrimination of minorities, resulting in
their disproportionate targeting and arrest. When predictive policing programs are biased toward
specific regions or demographics, they generate inaccurate predictions based on incomplete
information.
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References
Electronic Frontier Foundation. (2019, September 5).
Automated License Plate Readers
(ALPRs)
. Electronic Frontier Foundation.
https://www.eff.org/pages/automated-license-plate-
readers-alpr
Body-Worn Cameras. (2019, May 29).
Body-Worn Cameras
. Electronic Frontier Foundation.
https://www.eff.org/pages/body-worn-cameras
Angel Diaz. (2019, October 4).
New York City Police Department Surveillance Technology
.
Brennan Center for Justice.
https://www.brennancenter.org/our-work/research-reports/new-york-
city-police-department-surveillance-technology
Guariglia, M. (2020, September 3).
Technology Can’t Predict Crime, It Can Only Weaponize
Proximity to Policing
. Electronic Frontier Foundation.
https://www.eff.org/deeplinks/2020/09/technology-cant-predict-crime-it-can-only-weaponize-
proximity-policing
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