Conclusions:
The often complicated and important process of data acquisition, rectification and preparation for implementation into a GIS software system is often underestimated. A common source of digital data, provided by projects like TIGER are long overdue, and could provide \”customers\” and users of this data warehouse with a consistent and reliable single source database. This is not to say a single source database may not be erroneous, but the error is kept constant if all are accessing the same information. The purpose of this paper was to explore the spatial statistics program for the analysis of crime incident locations, CrimeStat. The \”hotspot analysis\” module, which included the K-means clustering and the nearest neighbor hierarchical spatial clustering methods were both successful in processing the inputted data effectively and efficiently. The nearest neighbor hierarchical spatial clustering routine found the overall distribution of data points to be distributed in a completely spatially random pattern. The K-Means analysis was employed to refine the Nnh process by categorizing the data points into five ellipses, based on the standard deviation of the data in both the major and minor axes. Two of these ellipses, ellipse 1 and 2, were found to be statistically significant. The \”hotspot\” aspect of the program did indeed highlight areas of higher concentration and is a useful identification and monitoring tool. The CrimeStat program and programs like it, provide users with the ability to create intelligent maps that allow us to extrapolate information into predictive models. This \”modular approach\” of expanding and developing specific software supplements, empowers the user by providing new applications and dynamic methodologies, to the static and expensive cartographic / GIS software nuclei ( Anselin, L., 1998). The strength of these analyses cannot stand alone and requires an intelligent user to make intelligent choices to provide meaningful results.
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