Introduction
Predictive policing is a model that is based on data analytics through which law enforcement agencies can foretell future crime. The model has revolutionized policing from being reactive and focusing on what transpired to concentrating on what is likely to occur (Meijer & Wessels, 2019). The approach provides critical data and information that offers proper guidance on effectively deploying resources to curb crime, thereby transforming policing outcomes. Predictive policing functions using established processes of data collection and analysis.
Predictive Policing in Texas
Texas has used applied predictive policing to collect data using the recent technology based on artificial intelligence (AI) and algorithms. In particular, risk terrain modeling has been applied in Dallas to collect data on areas that are at a high risk of gun violence (Mugari & Obioha, 2021). The model helped to show that gun violence is likely to occur in regions with car washes, a high concentration of stores dealing in used goods, and apartment complexes, which have facilitated crime reduction.
Positive and Negative Consequences
Predictive policing has been highly regarded as an integrated approach to dealing with crime. The technique rides on and integrates basic components of other policing strategies, such as intelligence-led policing, community policing, problem-oriented policing, and evidence-based policing (Meijer & Wessels, 2019). The greatest benefit of the method lies in the identification of new or formerly unidentified trends and patterns that are critical in preventing and responding more effectively to future crime.
On the contrary, data collection is a highly guarded exercise that, if not handled well, can lead to catastrophic results for the community. People also may not have the trust to provide critical information, especially in regions that have police departments with a history of corruption or racial bias. That is why some people consider the model to threaten civil freedoms and the privacy of the population.
Successes and Failures
Predictive policing has been successfully applied in the Arlington, Texas, Police Department. The exercise involved using data on theft cases in residential homes to specify the most affected zones and compare the spots to regions with code infractions. The approach enabled the police officers to establish that every single increase in physical decay corresponded to a six-fold increase in residential break-ins (Mugari & Obioha, 2021). This led to the conclusion that areas with significant physical decay had a higher expectation of suffering increased residential burglaries. The model helped the police department create a formula to help set apart the traits of the most affected neighborhoods.
However, the approach failed in the Los Angeles Police Department (LAPD). The police department was forced to discontinue its LASER crime prediction program. LASER was designed to use documented crime data to designate areas with a high crime rate. (Mugari & Obioha, 2021) State that the Palantir software was deployed to designate individual criminal risk scores. Detailed scrutiny of the software established that the program was racially prejudiced against Latino and Black people.
Predictive Policing and Police-Community Relations
Law enforcement agencies recognize that the community must be involved in enhancing their safety. In implementing predictive policing, the community should trust the process. The police are expected to nurture good relations with the community by being extremely transparent (Meijer & Wessels, 2019). An engagement based on trust emanates from the fact that the success of predictive policing depends on how well law enforcement uses community data in a proactive approach to preventing and reducing crime.
Conclusion
Predictive policing can overcome the challenge of lacking community trust by handling the data collected correctly. The reason is that it involves data collection and analysis of patterns and trends. For it to succeed, the community should support the approach, and law enforcement agencies should ensure that they observe privacy, are accountable, train personnel to use the information properly, and constantly improve the policy.
References
Meijer, A., & Wessels, M. (2019). Predictive policing: Review of benefits and drawbacks. International Journal of Public Administration, 42(12), 1031–1039. Web.
Mugari, I., & Obioha, E. E. (2021). Predictive policing and crime control in the United States of America and Europe: Trends in a decade of research and the future of predictive policing. Social Sciences, 10(6), 234. Web.