Predictive Policing: Law Enforcement and Technology

Since 2009, the idea of predictive policing, or the use of advanced statistical analysis and data to make policing decisions, has become widely used in the United States. Predictive policing is the perfect tool to help Law enforcement agencies become more efficient as budgets continue to be reduced. “With predictive policing, we have the tools to put cops at the right place at the right time or bring other services to impact crime, and we can do so with less,” (Gascon 2009).

On the other hand, predictive policing will hold officers accountable for tackling and decreasing crime and those who fail to do so could have adverse effect on hisher career (Willis, Mastrofski & Weisburd, 2003). COMPSTAT, also short for computer statistics, is a system which implements the four basic information system (IS) functions which are input, processing, output, and feedback. Although the application of information technology has been able to help reduce crime, it is quite different than random patrol tactics used by police departments.

To successfully compare and contrast the use of information technology (IT) to optimize police departments’ performance to reduce crime versus unplanned street patrols, we have to look at exactly what IT is offered to police now. The definition to predictive policing is given as “any policing strategy or tactic that develops and uses information and advanced analysis to inform forward-thinking crime prevention”.

COMPSTAT, as an information system (IS), implements the four basic IS functions as follows: The input function data is gathered and entered into the database. This is the building block of COMPSTAT it contains information compiled from various sources like police incident reports, arrest reports, suspect briefing report, phone calls, and field interview reports. The keying the input data into the Incident Reporting System where it is maintain as a database for future reference.

Crime analysts are responsible for inputting the data and are responsible for their accuracy. Data errors could be discovered as a result of the close collaboration among members of the CAU, who were familiar with the geography and nature of crime in their cities; and District commanders would report discrepancies between what they had read in their officers’ reports and the materials the CAU had prepared for CompStat. (Willis et al. , 2004). The processing function is the same as effective tactics because “after command and staff officers are in

possession of timely and accurate intelligence, they are accountable for the creation, development, and implementation of crime reduction strategies and action plans for the purpose of minimizing the identified crime or risk management problems. ” Data is transformed, converted and analyzed for analysis (Godown, 2012). Additionally by using Geographic Information Systems units depended upon police incident and arrest reports for their crime data, but they also used Computer-Aided Dispatch data to help identify geographic hot spots.

The output function is the same as rapid deployment because “Once an issue has been identified and appropriate resources have been formulated into a tactical plan, command personnel must rapidly deploy the plan to get results before their target moves (Godown, 2012). The feedback function is the same as relentless follow up and assessment because “As Jack Maple stated about the CompStat process, “You can only expect what you inspect. ” A feedback mechanism is put into place to assist with monitoring and controlling operations. (Godown, 2012).

Knowledge from previous errors should never be circumvented. Constantly working at innovation and integration with new technology to keep the system up to date can provide better outcomes. Constant assessment of performance and shortage of accomplishing goals should be reviewed so that corrective action can be taken meet desired results. Predictive Policing SWOT Analysis Predictive Policing strength allows resources to be used more efficiently because they can be deployed to specific locations in which crimes are likely to occur and for specific types of crimes.

In this regard, it is also easier to prevent crime from occurring as opposed to merely responding to it (Goode, 2011). Improving the algorithms and more data collected the predictions will be more accurate. Predictive policing weaknesses are often treated as being solely related to the use of computers and data to the detriment of involving front-line police officers in the decision-making process. This can result in police officers feeling both disrespected and unimportant in performing police work (Willis, Mastrofski & Weisburd, 2003).

The weakness in predictive policing could be – rogue officers. Reports misfiled, misclassifying crimes, officers are not completing reports, and reporting a series of offenses as a single event. Predictive policing opportunities provide for the opportunity for police departments to reduce criminal activity at a lower cost to taxpayers. Police departments can prevent crime from occurring rather than using limited resources to respond to crimes once they have occurred and hoping responses will deter other criminals (Pearsall, 2010).

Predictive policing threats are primary related to some police officers, mainly the, I hate computers, older police officers, are unconvinced of the use of statistics and data in place of human element is not trustworthy. This could result in predictive policing not being as successful as it can possibly be. In conclusion, predictive policing can result in a reduction in crime by predicting where it will happen rather than reacting to it once it has occurred. The predictive policing assurances calculable results, including crime reduction and more effective law enforcement agencies.