Crime mapping and spatial analysis have become growing tools used by enforcement and other groups to analyze crime patterns. These tools have helped employ many crime prevention strategies throughout the United States, however they are still developing. Crime mapping, since it is still new, has many technical problems and also ethical issues that should not be overlooked when utilizing these tools. The following section will examine criticisms in the area of spatial analysis and the crime mapping of hotspots in a broad sense. Jerry Ratcliffe (2002)  This article describes potential risks and problems that arise with the use of spatial analysis and crime mapping. Further the impact of poverty, racism, are not included into crime mapping leading to this factor not being considered and individual peace officers bringing their own vices, judgement calls into the process. Crime is not a mythical construct, it has tangible root causes that range from financial poverty, to biological causes (hormonal imbalances, etc) to desperation. One of the first steps to analyzing crime, with the use of crime mapping, is the generation of pinpoint maps using the process of geocoding. This is the process of embedding coordinate information of crime incidents on city maps. Anyone can gain access to create maps on the Internet using the process of geocoding. Geocoding, however, has many errors that can occur in the process because the process is still being developed. This article uses material from the Wikipedia article "Crime_hotspots", which is released under the Creative Commons Attribution-Share-Alike License 3.0.
A study that uses nearest neighbor index (NNI), and STAC Ellipses was completed for the City of Roanoke, Virginia. The study focuses on data reported to police on robberies that occurred between January 1, 2004 and December 31, 2007, with a total of 904 robberies reported (Patten, Mckenlden-Coner & Cox, 2009). The purpose of this study was to determine if there were localized areas of robberies using hotspot analysis. The project first began by geo-coding all data onto a pinpoint map. The records of all robbery data came from the cities records and management system. After receiving satisfying results from geocoding the data, the data was then tested for global and spatial clustering (Patten, Mckenlden-Coner & Cox, 2009). To test for spatial randomness, NNI was employed. For each year, 2004-2007, NNI was calculated and compared to a set of random points. Each year presents a NNI value of less than one (Patten, Mckenlden-Coner & Cox, 2009). A value less than one, according to Eck, Chainey, Cameron, and Wilson (2005), signifies that the clustering in the data set is consistent in its distribution. Patten, Mckenlden-Coner & Cox (2009) concluded that the data set has significant global spatial clustering that applies to the entire study population. Following the testing of random clustering, using NNI hotspot analysis, was employed in the study. The study examined hotspot using many different spatial analysis techniques. The study used nearest neighbor hierarchal clustering (NNH) and other kernel density estimation (KDE). The following will look at the analysis of STAC ellipses in further details for the purpose of this section. Ellipses were developed for each year and then were further examined using different techniques. To create the ellipses, parameter settings were made based on the distance a person can travel on foot in approximately five minutes before looking for another form of transportation. A search radius of a quarter mile was set for the data (Patten, Mckenlden-Coner & Cox, 2009). Ellipses were made for the total amount of robbery incidents, 904. Fifteen offenses per ellipse were used. Offenses were dropped to 7 incidents per ellipse for a single year, and for two year increments 7,10, and 15 incidents were evaluated (Patten, Mckenlden-Coner & Cox, 2005). With all the different techniques employed in this study it was concluded that STAC ellipses had the greatest reliability rate. It was determined that ellipses tend to be less accurate than other methods utilized; but, by far were more consistent. Patten, Mckenlden-Coner & Cox (2009) concluded in this study that all methods utilized converge around the same areas of the city. This indicated there is random spatial clustering and agreement between the different methods employed. Using the hotspot analysis, different areas in the city were identified as “problem areas.” There were areas that were determined to be crime generators and others attractors. Patten, Mckenlden-Coner & Cox (2009) recommend that for areas of attractors increase in guardianship, and better place management should be the area of focus. Areas that contain crime generators would require more strategic approaches by police to make an impact (Patten, Mckenlden-Coner & Cox, 2009, p. 27). This article uses material from the Wikipedia article "Crime_hotspots", which is released under the Creative Commons Attribution-Share-Alike License 3.0.