1. Compare and contrast the application of information technology (IT) to optimize police departments’ performance to reduce crime versus random patrols of the streets. In recent years, the idea of predictive policing, or the use of statistics and data to make policing decisions, has become widely popular in the United States. In 1994, the New York City Police Department adopted a law enforcement crime fighting strategy known as COMPSTAT (Computer Statistics). COMPSTAT uses Geographic Information Systems (GIS) to map the locations of where crimes occur, identify “hotspots”, and map problem areas.
COMPSTAT has amassed a wealth of historical crime data. Mathematicians have designed and developed algorithms that run against the historical data to predict future crimes for police departments. The purpose of this paper is to briefly examine predictive policing and how tools such as COMPSTAT allow police departments to respond more efficiently to criminal activity. Using information technology to fight crime by the police officers is becoming increasingly effective in apprehending the crime perpetrators.
Predictive policing, or programs such as COMPSTAT, involves using data from disparate sources, analyzing them and then using the results to anticipate, prevent and respond more effectively to future crimes. “The predictive vision moves law enforcement from focusing on what will happen and how to effectively deploy resources in front of the crime, thereby changing outcomes,” writes Charlie Beck, chief of the Los Angeles Police Department (Predictive Policing: The Future of Law Enforcement, NIJ, 2012). From the early 1800s to the 1980s, patrol and criminal investigation dominated policing.
Uniformed police patrolled the streets to prevent crime, to interrupt crimes in progress, and to apprehend criminals. However, research since the 1960s has shown the limits of both patrol and investigation for controlling crime. Patrol officers did not effectively prevent crime by questioning suspects, victims, and witnesses. In the 1990s, the police adopted predictive policing strategies in which police initiate action instead of waiting for calls. Patrol remains the backbone of police operations. It consumes most of the resources of police agencies.
On patrol, a police officer makes regular circuits or passes through a specific area. Studies of foot patrol indicate that these patrols are costly and do not reduce crime. Because crime is not evenly distributed throughout a community, which means some places need more patrol than others. The tradition of giving each neighborhood an equal amount of patrol wastes police resource, so the tradition of giving each neighborhood an equal amount of patrol just wastes police resources, however, which can make citizens less fearful of crime and improve citizen attitudes toward the police(CliffsNotes. om. ). While predictive police operations focus on the concentration of crime in certain offenders, places, and victims. Predictive operations include using decoys, going undercover, raiding, relying on informants, stopping and frisking suspects, shadowing repeat offenders, policing repeat-complaint locations, and saturating an area with police to maintain order which can be an effective method to prevent crime(CliffsNotes. com. ). 2. Describe how COMPSTAT, as an information system (IS), implements the four (4) basic IS functions: 1.
Input, 2. Processing, 3. Output, 4. Feedback. COMPSTAT is the name given to the New York City Police Department’s accountability process and has since been replicated in many other departments. COMPSTAT is a management philosophy or organizational management tool for police departments, roughly equivalent to Six Sigma or TQM, and is not a computer system or software package. ” (State of CA, 2010). COMPSTAT as an information system implements the four basic IS functions in the following ways: Input
Data gathering process which is the building block of COMPSTAT is comprised of information compiled from variety of sources like police incidents, arrest reports, suspect debriefing, telephone calls, and field interview reports. Pushing the data into the Incident Reporting System will help to maintain a database for future reference (Willis et al. , 2003). Processing The collection of data is presented during every COMPSTAT meeting. This would be entered into a database using a data management program like MS Access. This task is performed by the Crime Analysis Unit (CAU).
These analysts classify, categorize, aggregate and analyze the data in order to gain a detailed report including many details like date, location, day and other information related to the crime. At this stage they would be able to identify a targeted response that may be the source of concern. This report provides useful identification of crime patterns like the point of entry for a burglary or make and model of a stolen vehicle (Willis et al. , 2003, Pg 48-50). Further by using GIS (Geographic Information Systems) data and spatial analysis geographic hot spots are located and mapped to the data.
Output Once the data is processed, it is presented to the department commanders. With available intelligence they should devise a strategy and deploy resources with expertise to tactfully pre-empt a crime situation and follow it up to ensure performance and results were as desired. These are key steps or principles which guide the departments patrol and investigative work: * Accurate and timely intelligence, * Rapid deployment * Effective tactics, and * Relentless follow-up and assessment (William J. Bratton, 1999 pg-15). Feedback The key element of improvement is feedback.
As it includes data, any error related to this aspect should be avoided. Consistent and correct data has to be entered into the incident reporting system and database. Accurate report writing should be followed while recording incidents. Extensive data analysis to identify the root cause of crime becomes mandatory. Regular meetings and brainstorming session should be conducted to improve data collection and teams involved should be evaluated. Learning from past mistakes should never be avoided. Continuous aiming at innovation and integration with latest technology to keep the system up to date can provide better results.
Periodic assessments of performance and gaps in accomplishing set goals and objectives should be reviewed so that corrective measures can be taken to fill the gaps to meet desired results. 3. Determine how information systems have allowed police departments that implement tools such as COMPSTAT to respond to crime faster. With the implementation of information systems real time access to data has become easier. Identification of hot spots related to crime is possible and problem solving has become proactive instead of reactive. Monitoring of multiple locations and futuristic crime and its patterns can be detected.
This allows operations to be carried with fewer resources, and reduced random patrols. Further it provides a platform for administering vast information which enables better decision making and problem solving. By demanding accountability it facilitates team work and police personnel are now working together to accomplish set goals faster thereby reducing crime. 4. Apply the strengths, weaknesses, opportunities, and threats analysis (SWOT analysis) on behalf of police departments that intend to implement predictive policing. SWOT Analysis of Predictive Policing SWOT Analysis of Predictive Policing
Strengths: The strength of predictive policing is that it 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). Weaknesses: Predictive policing is 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). Opportunities: Predictive policing provides 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).
Threats: The primary threat related to predictive policing is that some police officers, particularly older police officers, are skeptical of the use of statistics and data in place of traditional street patrols. This could result in predictive policing efforts not being carried out in a way that will allow them to be as successful as possible. Conclusion The information contained in this brief paper has shown that with the use of information technology and programs such as COMPSTAT, predictive policing can result in a reduction in crime by predicting where it will occur rather than responding to it once it has occurred.
However, predictive policing can have problems if the sole focus is on the use of information technology. Instead, police officers and the general public must be included in the process. Their feedback must be solicited as part of the larger process. Otherwise, they have the ability to derail any reductions in crime and improvements in performance that might be possible from predictive policing. References 1. Goode, E. (2011, August 15). Sending the police before there’s a crime. Retrieved from http://www. nytimes. com/2011/08/us/16police. html 2. Pearsall, B. (2010, May).
Predictive policing: The future of law enforcement? National Institute 3. Willis, J. J. , Mastrofski, S. D. , ; Weisburd, D. (2003). Compstat in practice: An in-depth Analysis of three cities. Police Foundation. Retrieved from http://www. policefoundation. org/pdf/compstatinpractice. pdf 4. CliffsNotes. com. Police Strategies. Retrieved April,12th, 2012, from http://www. cliffsnotes. com/study_guide/topicArticleId-10065,articleId-9953. html 5. Bratton, W. J. ; Malinowski, S. W. (2008). Police performance management in practice: Taking COMPSTAT to the next level. Policing, 2(3), 259-265.
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