The article Crime Mapping and the Crimestat Program begins with an overview of the Crimestate program and its contribution to crime mapping. The Crimestat program provides supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in crime mapping. It specifically highlights version 3.0 and emphasizes its ability for law enforcement personnel to analyze travel patterns over metropolitan areas. It is designed to increase the effectiveness of crime mapping and other GIS applications as well as work with large databases considering most police department work with large files.
Following a brief overview of Crimestat the article provides a detailed definition of crime mapping. It states that crime mapping is the mapping of crime incidents in order to detect general patterns of crime to better allocate resources for enforcement and prevention as well as to identify and apprehend offenders committing crimes. It has multiple uses, examples include, planning efficient deployment strategies to focus on hot spots, tracking the behavior of serial offenders, and mapping motor vehicle crash locations.
The article provides detail into the statistics built into the program to make crime mapping more efficient and easier to use. First, the author describes spatial statistics. He states that a key concept in spatial statistics is spatial autocorrelation, defining it as events that are spatially arranged in a nonrandom manner, either more concentrated or, occasionally, more dispersed than would be expected on the basis of chance. Next, the author provides a description of hot spot analysis, an extreme form of spatial autocorrelation, stating that incidents, mainly criminal activities are concentrated in a limited number of locations. Spatial modeling is the next area of focus. The first method in this category is interpolation, the act of extrapolating a density estimate from individual data points. It involves the placement of a fine-mesh grid over the area under study and the distance from each grid cell to each data point in calculated. This is followed by an estimate of incident density for each grid cell. Journey-to-crime analysis is the next analytical tool discussed and is a criminal justice method for estimating the likely residence location of a serial offender when given the distribution of incidents and a model for travel distance. Space-time analysis precedes journey-to crime-analysis and includes many routines for analyzing clustering in time and space including the Knox and Mantel indices as well as the Correlated Walk Analysis module. The final statistical tool is crime travel demand that models criminal travel behavior over a metropolitan area. Finally, the article is comprised of a section discussing the miscellaneous options in Crimestat. The concluding paragraphs reemphasize the importance of GIS and crime mapping overall into law enforcement. Furthermore, the author reiterates the need for statistical tools to aid in the assessment of the important trends in the data.
CrimeStat, a statistical tool used to increase the usefulness of crime mapping is likely to increase the effectiveness of crime mapping overall but considering the program developer is the writer of the article it has the potential to be biased. It would have been useful to include reviews or opinions by its users in the law enforcement field or other users such as students, professors, researchers or analysts to minimize the possibly of bias.
The article states that CrimeStat is one of the many tools developed to summarize and assess the trends in crime mapping. Examples of other tools used to achieve the same results for comparison reasons would provide the reader with a better means to assess Crimestat's effectiveness over other programs.
Given the nature of the article, to incorporate and explain Crimestat and its use in crime mapping, a more through explanation of the relationship between the different types of crime mapping and Crimestat’s contribution to these methods would be highly useful. For example, Crimestat provides seven hot spot analysis routines including the mode, the fuzzy mode, hierarchical nearest neighbor clustering, risk adjusted nearest neighbor hierarchical clustering, the Spatial and Temporal Analysis of Crime routine, and K-means clustering. An example giving a basic overview of each of these analysis routines would provide a better understanding of Crimestat's functions in relation to crime mapping while allowing the reader to compare and contrast the methods evaluating what method is most effective in a particular situation. Furthermore, Crimestat uses five different mathematical functions to estimate the density and has two different applications including a single-variable density estimation and a dual-variable density estimation routine. Again, the author mentions Crimestat and the five different mathematical functions as well as the two different applications the program is able to use. This is another instance in which more examples would prove to be effective in understanding the functions and their contributions to crime mapping. This would allow for an overall better understanding of the program and crime mapping in general.
The article discusses Version 3.0 of Crimestat stating it has a crime travel demand module of modeling criminal travel behavior over an entire metropolitan area. The author focuses on the concept of aiding law enforcement in metropolitan areas throughout the article. While this is useful, it is also important to emphasize on its effectiveness in smaller law enforcement agencies. In the conclusion, the author stresses the strong need for statistical and other analytical tools that can summarize the trends in the data. Although visuals and graphs are provided for some methods, specific examples for each method and an outright description as to how it relates to crime mapping would be beneficial. This would allow for an overall better understanding of the program and crime mapping in general.
Levine, Ned. (2004). Crime Mapping and the Crimestat Program. Ned Levine & Associates, 41-56. Retrived from http://cs.iupui.edu/~tuceryan/pdf-repository/levine2006crime.pdf