An Examination of Means of Application and Core Function in Predicting Crime - Assignment Example

Section/# CompStat: An Examination of Means of Application and Core Function in Predicting Crime COMPSTAT, an acronym for Computer Statistics, is the recent fusion of GIS information along with key crime information that provides certain law enforcement entities that employ it with the identification of key hotspots in which crime is more likely to take place based on past experience and records. As a result of computer algorithms and the utilization of mathematicians and statisticians of the data the police departments chronicle on a daily basis as a means of a litany of different police reports, the application of CompStat to “predict” crime has become an indispensible tool that many law enforcement entities use as a way to maximize available resources in a way that can help reduce the overall prevalence of crime in the aforementioned “hotspots” and zones in which certain types of crime are likely to exist. The following brief analysis will seek to define and categorize the ways in which CompStat utilizes the four basic IS functions of input, processing, output, and feedback as a means of building capability in seeking to deter crime within the regions it is employed. Lastly, a SWOT analysis will be conducted as a means of seeking to further quantify the usefulness of the system in deterring crime. Using the example of the New York City Police Department, given in the prompt for this essay, one understands that the key inputs that the police department interfaces with in regards to building the outputs and feedbacks that are generated is made available through a weekly process of data input. In this way, personnel from New York City Police Department’s precincts, service areas, and transit districts compile a statistical summary or overview of the week’s crime data. In this way, crime patterns, arrests, complaints, and other verifiable and measurable data of significance are collated and entered into a database. This process represents the input phase, or data gathering phase that forms the very backbone of the process of CompStat. It is important to note that this process itself is the most important as it is the only stage in the process where human inputs can directly affect and oftentimes skew the results that are reported. For this reason, an individual department has been set up within the Chief of Police’s office; labeled as the CompStat Unit (Willis et al 2012). As such, the unit is directly responsible for winnowing, sorting, and inputting in the correct fashion, any and all pertinent information that could be of use with reference to developing a type of predictive policing to ultimately reduce the level of crime displayed within the city’s regions and precincts. Once the inputs have been entered into the system, the second phase of analysis then takes place; that of processing of the determinant data. This stage is reliant not upon the level of analysis that individual humans would otherwise perform; rather, it is entirely dependent upon the algorithms and likelihood matrixes that CompStat itself can infer from the inputs it has been given. As with the old cliche with regards to a computer program only being as good as the programmers that programmed the code, the same is true with relation to CompStat as it is but a tool and should not be seen as a perfect representation truth or overall infallibility. As has been stated with the input stage that has previously been discussed, the processing of the data and the outputs that it ultimately yield is solely reliant on the quality and voracity of the information that is loaded into the system. In other words, only a quality level of inputs will be processed and related into a quality level of outputs from which a degree of reasonable and actionable inference could foreseeably be drawn. The heart of the CompStat process therefore relies on each step of the process; however, for purposes of evaluation, the third step, or the outputs, is of perhaps the most interest. Within this step, a team of analysts is then responsible for taking the outputs that the computer system has generated and seeking to draw a level of inference from this (Hyunseok 2010). As a means of this level of inference, analysts and police from within each and every precinct seek to utilize the information as a means to relegate their limited resources to the greatest effect. Such a stage invariably leads to the final determinant in the process of CompStat’s utilization of the raw data that has thus far been discussed; that of the feedback that the inputs, processing, and outputs necessarily point towards. Through a combination of human analysis and the outputs that CompStat is able to generate, the final stage is the ultimate goal upon which each preceding stage has been predicated. As a function of the level of prediction upon which CompStat is founded, key crime trends, deviations, anomalies, and actionable information with regards to key geographic information and the means whereby police forces can seek to maximize their limited resources as a means of deterring crime are related. The level to which CompStat has enabled jurisdictions to respond to and allocate law enforcement resources as a means to deter crime in a more efficient manner has been profound. However, before CompStat can even be considered as a viable tool for a given police department, the necessity of having a uniformity in information system that is capable of effortlessly relating the determinant data into CompStat is a must. Similarly, with respect to the Strengths, Weaknesses, Opportunities, and Threats that CompStat necessarily engenders, one can immediately note that the greatest strength is the reduction in crime due to a better and more intelligent allocation of limited police resources. Similarly, the greatest weakness is centric upon the quality of the information that is analyzed within the constraints of the program itself. Likewise, the greatest opportunity with relation to the use of CompStat is for the program itself to become more advanced in time and provide LE entities a better means of targeting troubled areas. Lastly, the greatest threat to the system is that of faulty or skewed inputs equating to faulty or skewed outputs (Eterno 2010). As has been noted earlier in the discussion of CompStat, the computer algorithms and modeling that ultimately yields informational analysis for key shareholders is only as good as those guidelines that helps to determine what data can be included and what data should be discarded. Moreover, the process of categorization of the data at every stage of the process is the single most important facet of determining whether ultimate success or failure will be generated as a result of the analysis. In conclusion, CompStat represents an impressive and promising new tool in predictive modeling of crime and its associated statistics. In a time in which police departments around the entire nation struggle with funding and meeting the needs that their community’s have with relation to maintaining officer presence on the streets, CompStat acts as a type of force multiplier, when utilized effectively, to ensure that as many determinant needs of the community are met by allocating officer presence in a way that seeks to ameliorate total crime in the affected area. Although no tool of policing is perfect or cost-neutral. CompStat allows for the jurisdictions that employ it to approach the issue of resource allocation in a somewhat more rational way than merely relying on the traditional accumulation of reports and officer experience to dictate the manner in which key resources are ultimately allocated. References Eterno, J. A., & Silverman, E. B. (2010). The NYPD's Compstat: compare statistics or compose statistics?. International Journal Of Police Science & Management, 12(3), 426-449. doi:10.1350/ijps.2010.12.3.195 Hyunseok, J., Hoover, L. T., & Hee-Jong, J. (2010). An Evaluation of Compstat’s Effect on Crime: The Fort Worth Experience. Police Quarterly, 13(4), 387-412. doi:10.1177/1098611110384085 Willis, J. J., & Mastrofski, S. D. (2012). Compstat and The New Penology. British Journal Of Criminology, 52(1), 73-92. Read More