Discussion: Measures of Central Tendency

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Data Analysis and Criminal Justice

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Data analytics helps by combining extensive data in various ways in attempts to display previously unseen patterns. In this case, data analytics applies computer-processing power to correlate and bring together many data points. For instance, a data analyst in the criminal justice system correlates criminal justice data such as recidivism and crime rates with information from other sources such as work history and poverty rates to find patterns necessary to improve the justice system’s efficiency and quality. Data analysis and research are more pertinent to police officers because they are the first contact people make in the criminal justice system. The police collect data about crime reporting, which receives compliments from the judicial system’s data concerning criminals/crimes prosecuted and data from probation or correctional services (Hannah-Moffat, 2019). I would use data analysis as a police officer to prevent crime through timely, accurate, and impartial statistical data. I would analyze and research data to keep citizens safe by knowing areas prone to high crimes, gang formation within the community, and ex-prisoners tactics that facilitate recidivism. In this case, I would use data analysis to input information from crime scenes into databases that would help to find connections between cases. Later, I would create specific criminals’ records and narrow suspects list. For instance, by grouping crime information with data reflecting vandalism incidences, unemployment rates, and truancy rates, I can uncover subtle and significant correlations affecting crime. After geotagging data points, I would narrow the data further to predict where and when certain crimes are likely to occur. The prediction would help in deploying the limited personnel and financial resources necessary to patrol high crime areas. Later, I would make crime data public to increase trust and transparency that would facilitate a good working relationship with community members.

Reference

Hannah-Moffat, K. (2019). Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates. Theoretical Criminology, 23(4), 453-470.