By U.S. Department of Justice
Mapping Crime: knowing sizzling Spots 2005. seventy nine pages. desk of Contents approximately This document bankruptcy 1. Crime sizzling Spots: What they're, Why we now have Them, and the way to Map Them 1
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Extra info for Crime Scene Investigation Mapping: Understanding Hot Spots
250-m quadrat thematic map Greater than 15 10 to 15 5 to 10 1 to 5 No crime Vehicle crimes by 250-m quadrats 25 SPECIAL REPORT / AUG. 05 with a number of the hot spots identified from the point map. In addition, notice that the census block A identified as a hot spot in the geographic boundary thematic map (exhibit 9) is not revealed as a hot spot using this method. The crimes that contributed to the high count in this large census block are generally spread across the whole geographic region. The quadrat method, therefore, appears to offer a more accurate method for identifying hot spots of crime, particularly where applica tions for crime prevention targeting are required.
For example, the number of hot spots on a map showing the distribution of crime as a kernel densi ty surface depends on the ranges selected by the map designer to show spatial con centrations of these point events. Careful selection of range settings is therefore required, but this opens the opportunity for crime analysts to create different ker nel density estimation hot spot maps of crime from the same data. vehicle crime. The method is visually appealing and structures the thematic thresholds to clearly identify areas of high est crime concentration.
LISA statistics assess the local association between data by comparing local averages to global averages. For this reason they are useful in adding definition to crime hot spots and placing a spatial limit on those areas of highest crime event concentration (Ratcliffe and McCullagh, 1998). One of the more applied LISA sta tistics on crime point events is the Gi* sta tistic (see Ratcliffe and McCullagh, 1998, for more details). The Gi* statistic is applied to a grid cell output, such as a quartic kernel density estimation map, from which local associa tions are compared against the global average.