Comparison between IT and Random Patrols in Police Performance
The use of information technology in fighting crime by the police department is becoming increasingly popular and effective in the apprehension of crime perpetrators. Apart from the police department, Information Technology is also being widely applied in other business settings, to enhance delivery of quality services, such as e-commerce (Shurkin, 2011). The police department has found this tool very crucial in optimizing their operations and performance, especially in efforts aimed at stemming out crime. Predictive policing is an application born out of the innovation and revolution of information technology, and a vivid example of such a program in use today is COMPSTAT, which conglomerates data from varied sources, analyzes them, and uses the result produced to interpret possibilities of occurrence of crimes, thereby enabling them to put in place preventive and corrective measures to sabotage such precedencies (Shurkin, 2011). Predictive policing is stated as a move that transforms the normal workflow of the police department from dealing with crimes on hand, to anticipating them ahead of time and deploying relevant and adequate resources to combat such crimes (Goode, 2011). This has noticeably changed the outcomes of police operations, and substantially reduced both property and violent crimes.
On the other hand, community policing is the traditional form of police operations which encourages foot patrols that enhance the development of relationships between the police and the community, thereby involving the active participation of the civilians in crime intervention and management. Though both forms of policing show the presence of police officers in the community, information technology programs like COMPSTAT only partially manifest this picture (Predictive Policing Symposium, 2010). IT-driven are widely applauded for their ability to optimize police operations, but it is recognizable that use of statistical data and interpretation generally generate little understanding of the entire problem at hand, and may lead officers to apply non-holistic approaches. Such interventions might be destined to failure, thus, in as much as it is effective; the use of IT is limited as compared to police patrols. On the other, community policing offers the police department an golden opportunity to get to the root cause of the problem, understanding the “why”, instead of the usual “how” and “when” that are answered by COMPSTAT (Willis, Mastrofski & Weisburd, 2003). The move from a traditional form of policing to an IT-driven crime intervention and management therefore, requires application of high intelligence, coupled with strong and innovative methods.
COMPSTAT, despite the few drawbacks that every technology faces, remains as a prime innovation in the police department. It combines all major prescriptions embedded in the ideas of conventional organizational development experts, and uses current positioning systems like GIS to provide adequate data for crime prediction. In addition, it applies the use of sophisticated computer-generated crime statistics and maps to facilitate prompt responses to crime alerts. The police department is also re-engineered by this model, as every command staff is held culpable for the occurrence of crimes in their respective beats (Shurkin, 2011). It has been tested and found to effectively reduce the rates of crimes, since it enables officers to act to crimes before their actual occurrence. Traditional methods such as random patrols are devoid of adequate abilities to identify crime, since keeping an eye on all the corners of a community simultaneously is not pragmatic (Predictive Policing Symposium, 2010). The latter demands the deployment of many officers, who may not be present, or even feasible in economic context, and may also require a lot of time to complete the patrols. Since random patrols heavily rely on intelligence from the community members, it becomes hard to plan an organized operation, and issues of reliability arise. Conducting accurate anticipation and sanctioning of a timely targeted operation through predictive policing is a sure way of achieving better goals in the police department.
How COMPSTAT Implements Four Basic IS Functions
As an information system, COMPSTAT implements the four basic IS systems which are input, processing, output, and feedback in no uncertain ways. The first element which refers to the manner, in which data is entered or received by an information technology gadget, comprises the data gathering component of COMPSTAT as a major prerequisite (Predictive Policing Symposium, 2010). This component comprises of raw information that has been sourced from different place such as police incidents, suspect debriefing, arrest reports, field interview reports, and telephone calls among others. This load of data is pushed into the Incident Reporting system, which helps in maintaining the database for future use.
The processing component is also perfectly utilized and exemplified by a COMPSTAT program. Usually, data collected is presented in every COMPSTAT meeting, which is often entered in a database through the application of a data management program like MS Access (Godown, 2012). The Crime Analysis Unit is the one department tasked with the responsibility of building such a database. The CAU arrange, organize, categorize, and aggregate data collected in order to come up with meaningful and consistent reports of occurrences, including location of crime, date, or any other relevant information (Predictive Policing Symposium, 2010). At this stage of data processing, the department is at a position to identify a specific and applicable response to a crime. Moreover, it provides a clear trend of criminal practices, such as models of vehicles often targeted, and points of entry of burglars. Geographic hotspots are also successfully located and mapped into the database through the use of spatial analysis and geographical information systems.
Once the collected data is input and processes, the result is presented to department commanders, who deliberate on possible intervention methods using the intelligence provided by the report. This is made possible by the output component of COMPSTAT. The output can be made in soft copy, usually reflected in computer screens, or can be presented in hard copy by printing. This is then acted upon by the police expertise, which deploy resources to combat a likely occurrence of crime in an identified area (Godown, 2012). The patrol and investigation work of the police department is guided by the following key principles: timely and accurate intelligence, rapid deployment, effective tactics, and consistent assessment of efforts.
The COMPSTAT system utilizes the element of feedback as a key channel of making improvements. Any errors that arise from the input, processing, and output of data are corrected through the feedback component. For the success of the system, accurate entries of data into the incident reporting system should be consistently ensured, as well as extensive analysis of principal cause of crimes. When implementing the COMPSTAT program, regular meetings are conducted to offer opportunities for brainstorming and improvement of data collection and evaluation of teams involved in the process. The program emphasizes learning from past mistakes, in addition to up-to-date integration of the program with the newest technology. Lastly, as stated by its creator, Jack Maple, one can only create what one can inspect (Willis, Mastrofski & Weisburd, 2003). The program is equipped with a feedback mechanism which works perfectly to assist in efforts of monitoring and control of operations.
How IS has Enabled Police Departments to Respond to Crime Faster
The implementation of information systems by the police department allows real time access to relevant data, which is crucial in carrying out an investigation. Consequently, the identification of hotspots has become relatively easier and faster, and approaches to crime resolutions have adopted a proactive stance, rather than reactive strategy. With this model, monitoring of multiple locations as well as futuristic crime predictions is possible, thus reducing the use of vast resources in random patrols (Godown, 2012). The availability of an enormous base of information enables brainstorming and decision-making processes easier and faster. Lastly, the system demands and promotes accountability, thereby facilitating teamwork for a more efficient and bolstered crime response.
SWOT Analysis of Predictive Policing
A SWOT analysis of predictive policing identifies the system as a monitoring process, which involves such elements as the use of integrated technology, proper planning, and communal organizational values. This program has the ability to forecast crimes, which is an important tool in developing proactive measures to combat criminal activities (Pearsall, 2010). The system is, however, prone to data error, may be faced with the challenges of inadequate staff training, and adopting a poor implementation roadmap. As technology and information systems continue to revolutionize, opportunities are availed to improve the COMPSTAT system to a new level, which would perfectly optimize police operations such as accurate crime locations, data collection procedure, and customization. It can also be safeguarded from unauthorized entry and from terrors of cybercrimes, thereby eliminating any possible threats to the system.
Godown, J. (2012). The Police Chief, The Professional Voice of Law Enforcement: The CompStat Process: Four Principles for Managing Crime Reduction, http://www.policechiefmagazine.org/magazine/index.cfm?fuseaction=display_arch&article_id=1859&issue_id=82009.
Goode, E. (2011). Sending the police before there’s a crime. Retrieved from http://www.nytimes.com/2011/08/us/16police.html
Pearsall, B. (2010). Predictive policing: The future of law enforcement? National Institute Of Justice Journal, 266.Retrieved from http://www.nij.gov/nij/journals266/predictive.htm
Predictive Policing Symposium (2010). Technical Breakout http://www.nij.gov/topics/law-enforcement/strategies/predictive-policing/symposium/technical-breakout.htm
Shurkin, J. N. (2011). Santa Cruz cops experiment with ‘predictive policing’.
Willis, J. J., Mastrofski, S. D., & Weisburd, D. (2003). Compstat in practice: An in-depth analysis of three cities. Police Foundation.
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