Use of Predictive Policing in Managing Crimes in Major Cities in the United States

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Use of predictive policing in Managing Crimes in Major Cities in the United States

Abstract

This purpose of this research paper was to determine the effectiveness of using predictive policing in managing crimes in major cities in the United States of America. It entailed the analysis of peer-reviewed secondary sources to determine major cities across the world that have employed the predictive policing algorithm to address crime issues and their impact. After evaluating several peer-reviewed sources, the search indicated that the use of predictive policing to manage crime is effective way of addressing crime issues in major cities. Police officers use predictive policing to determine the potential criminal and victims of prospective crime and take precautions to prevent the individual from committing the projected crimes. Predictive policing algorithms use advanced technology to gather information about individual crime records and behavior that can be used to predict their possibility of summiting crime in the future. It also provided comprehensive information about a crime rate of a given locality to help the police force enhance the safety of such places. Also, police departments use such individual crime records and crime rates of a region to make informed decisions about the adequate resources required to maintain the area's safety and prevent suspected individuals from committing a crime. Challenges such as privacy concerns, discrimination, and lack of accuracy predictive policing undermine its effectiveness. However, embracing equality in law enforcement, adequate personal training, authorization in data collection, and objectivity in data recording can significantly improve the effectiveness of predictive policing in managing crime in major cities in the United States of America. The first cities in the United States of America to embrace the technology also became the first ones to ban its use in managing crime, arguing that a high level of discrimination in police departments is a significant challenge for the algorithm implementation.

Keywords: predictive policing, police department, discrimination, privacy concern and crime

Introduction

It is unfair to use stereotypes to associate people with crime, but predictive policing is more than stereotyping person and profiling them as probable criminals. Policing departments in some large cities in the United States have been experimenting with predictive policing to predict the likelihood of criminal activity. Predictive policing utilizes advanced information technology systems to analyze vast arrays of data like historical crime records, to assist in deciding where to allocate police officers or identify persons with a likelihood of committing a crime in the future. Place-based predictive policing is the most used method. It utilizes preexisting crime information to point out regions and times with a high crime threat (Ferguson, 2016). People-based predictive policing is another approach used to manage crime in major cities. It helps identify people or groups with high probability of breaking laws or become a victim of a crime.

The rapid rise it outlines among the minority group necessitates an investigation of predictive law enforcement to establish equality and fairness in the community. People of color are more vulnerable to incarceration than whites, although their numbers make up a tiny portion of the United States of America. According to the data aired in 2018 by the FBI's Uniform Crime Reporting (UCR), people of color were overrepresented among the people arrested for nonfatal violent felonies (33%) and serial nonfatal felonies (36%) relative to their representation in the US population (Jefferson, B. J. (2018). White Americans represent 60% of the population in the country, but around 46% of people were arrested for assaults such as robbery and rape, and 39% of all arrests for nonfatal violent offenses excluding other assaults.

Predictive policing was first deployed in the 1990s by the Chicago School of Sociology on parole recidivism. Ferguson, (2016) the approach involved sociologist Ernest Burges in research purposed to create the statistical policy. It purposes to identify and assess particular aspects linked with future lawbreaking forecasting and later spread into the diverse legal systems. Santa Cruz, California, was the first country to test and use predictive policing to control crime in 2011. Also, in mid-2020, the city became the first one in the US to prohibit the use of policy following an alarming increase in arrest and incarceration of people of color in the city and USA at large.

Predictive policing has depicted a significant improvement in predicting criminal activities in the United States of America. However, the policy has failed due to discrimination, privacy concerns, and lack of accuracy.

Benefits of Predictive Policing

Law enforcement officers use predictive policing to identify potential crimes and take the precaution to prevent occurrence of such crimes. Algorithms used in predictive policing can point out parties or criminal groups that depict escalated risk of a crime occurring between them, such as gang shooting (Jefferson, 2018). As a result, a person with the potential of becoming an offender in the future is identified. People showing a behavior of becoming criminals are monitored or targeted before committing the actual offenses. Security officers use people's demographic characteristics and past crime...