Law enforcement agencies (LEA) have long attempted to determine crime patterns, determinants of crime, and ways to improve security. For several years, LEAs have utilized geographic information systems (GISs) to better understand the spatial nature of crimes and possible spatiotemporal patterns. While previous analyses have yielded spatial clusters of high crime areas (i.e., hot spots), these analyses have lacked specificity at the street level. This research builds on the concept of a hot spot area and creates linear statistically significant spatial crime clusters at the street level, namely hot streets. The identification of hot streets has the potential to assist LEAs in reducing crime through increased patrolling and refocusing efforts at the street level. This study created a GIS-based geoprocessing model to analyze different crime types at varying temporal scales and in different cities (Atlanta, Georgia and Houston, Texas), classifying streets based on four crime cluster confidence levels. The research found that the geoprocessing model successfully streamlined the creation of crime hot streets in the two study areas, led to the development of multi-scale hot street maps, and can be used and edited by anyone who wants to perform linear cluster analysis in other topics.
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