Benefits of Information Technology in Police Departments. 1
COMPSTAT: Predictive Policing. 2
Benefits of Implementing CompStat. 4
The idea of predictive policing is gaining much prominence among law enforcement agencies. With dwindling budgets and a struggle to fight crime, police departments across the nation have the challenge of deploying police officers in areas or locations where crime is likely to occur. The application of information technology use in optimizing police department’s performance in reducing crime is critical to random patrols of the streets. Therefore, police departments should enhance their efforts to systematically predict where and when crimes are likely to occur depends on how well they deploy their resources
Benefits of Information Technology in Police Departments
The application of information technology is critical to raising the performance levels of police departments. The benefits of incorporating information technology systems in fighting crime are very cost effective. Police department in responding to the immense forces of change, such as globalization, changing demographics, emerging environmental issues, growing threats of stability, and increasing expectations from the public on policing services and results, must apply information technology systems. By increasingly using information technology, increases access to information and transparency, aggregating the populace data across law enforcement agencies, providing holistic perspective of the population behaviour, and integrating predictive analyses (Willis, Mastrofski, & Weisburd, 2007).
Therefore, increased application of information systems in fighting and predicting crime is related to the core business of police department in achieving desired results of crime reduction. Information systems thus helps law enforcement agencies to make appropriate decisions, achieve mission objectives, analyze system results, and reduce operating costs. Application of information systems offers solutions to address the key strategic challenges facing law enforcement agencies, to protect the nation and its citizens, and managing the overall police performance. Information technology provides a combination of deep industry expertise, comprehensive hardware portfolio, software and services together with a proven methodology to develop strategies and solutions with an actionable roadmap (DeLorenzi, Shane, & Amendola, 2006).
Information technology definitely increases the engagement of community members and the police, thus the increasing interaction encourage public participation which is a protective factor against crime. The use of ICT tools such as message boards and listservs enhances collaborative problem solving, and broadening participation. Technology though, is not a substitute to face to face interaction between law enforcement agencies and criminal activities. Too much expectation on information technology as a problem solving tool can immensely inflate expectations for information technology projects. Willis, Mastrofski, & Weisburd, (2007) argue that the information technology installed is not immune from problems. Use of information systems does not solve past occurrences of crime, nor eliminate barriers of participation; they can instead turn out be just a means of transferring data. Information technology tends to view crime like weather, or as some occurrence that just happens instead of a series of human decisions and people’s actions.
COMPSTAT: Predictive Policing
According to Willis, Mastrofski, & Weisburd, (2007), COMPSTAT is a predictive information technology model that uses Geographic Information System (GIS) to map crime locations, identifying crime hotspots, and problem areas. CompStat model is a management process within a performance management platform that analyses crime and disorder data, strategic problem solving, an an accountability structure. It is a system that facilitates timely and accurate crime analysis. And it is used to study crime patterns and various problems. The analyses generate customized responses which are implemented through appropriate and rapid deployment of resources and personnel. Its accountability structure is important to ensuring that the analysis is acted upon, and responses affected appropriately. It also assesses if the responses are effective and efficient in reducing crime. Geographic Information systems (GIS) provides search strategies that employs computerized crime mapping, crime analysis, CompStat, geographic profiling, criminal and offender profiling, hotspots identification, spatial analysis, geocoding, and geospatial analysis (Willis, Mastrofski, & Weisburd, 2007)..
This is a data driven management model called CompStat, it known for decreasing crime and enhancing the quality of life of New Yorkers for almost the last decade. This process, as an information system is guided by the four principles of input, processing, output, and feedback.
DeLorenzi, Shane, & Amendola, (2006) argue that for knowing what is happening by gathering accurate and timely intelligence, the sources of data will be from official sources, such as calls for service, crime, and arrests data. This primary data should be available real-time so as to provide crime maps, trends, and other analysis products. The command personnel use these information products to to know which crime problems need to be addressed.
To have a plan need effective tactics that relies on previous successes, and appropriate resources. The command personnel and other law enforcement officers plan tactics that respond well to the identified crime problems. These tactics may include law enforcement, government, and community partners (DeLorenzi, Shane, & Amendola, 2006).
Rapid deployment implies do it quickly which is contrary to the traditional policing of reactive model. If the tactics are deployed in a timely manner, then CompStat deploys resources to places where there is current crime. This results to heading of the problem before it escalates or continues.
Assessment and follow up involves if it has worked, the do more of it, otherwise do something else. CompStat has platform for checking on the success of the present and past strategies in resolving the identified problems. This feedback strategy is excellent for problem solving focused on crime reduction, and provides intelligence on how to improve present and future planning and deployment of resources (Firman, 2003).
Benefits of Implementing CompStat
The New York Police Department introduced techniques of predictive policing in 1994. CompStat has amassed immense wealth of historical crime data. Mathematicians came with a number of algorithms that run against past historical data to forecast on the nature of future crimes for police departments. Predictive policing has seen a significant drop in rates of crime such as car thefts, burglaries, and many other kinds of crimes in neighbourhoods. The goals of the system are to increase accountability, improve the performance of police departments, and their service outcomes.
The strengths, weaknesses, opportunities and threats of police departments that would wish to implement the predictive policing model. The strengths lay in the traditional policing model expertise which CompStat is supposed to compliment. The opportunities are the vast crime intelligence generated and the need to broaden participation of community. The weaknesses could be difficulties in implementing CompStat, as police departments may implement just as a means to convey data. The threats may be acceptability by the conservative police officers who prefer traditional policing approach (Willis, Mastrofski, & Weisburd, 2007).
Law enforcement agencies including police departments have the mandate to improve public safety by engaging in addressing crime and terrorists activities. In applying information systems helps them to integrate data to provide a wholesome view of the persons, objects, locations, and events. This helps in reducing the time needed to conclude investigations, implementing accountability programs to improve police departments performance, defining and discovering criminal and threat related patterns, considering both historical and future senses.
DeLorenzi, D., Shane, J, & Amendola, K. (2006). The CompStat process: Managing performance on the pathway to leadership. The Police Chief, 73 (9).
Firman, J.R. (2003). Deconstructing CompStat to clarify its intent. Criminology & Public Policy, 2 (3), 457-460.
Willis, J.J., Mastrofski, S.D. & Weisburd, D. (2007). Making sense of CompStat: A theory-based analysis of organizational change in three police departments. Law & Society Review, 41 (1), 147-188.
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