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Weapons crime in Denver County
Odeh, Malik
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Denver, CO
University of Colorado Denver
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Spatial & Functional Data Analysis (MATH 6384), Joshua French WHERE ARE THE WEAPONS? To locate weapon hotspots, we will treat weapon related crimes as cases and all other crimes as controls. Are there clusters of weapon related crimes in Denver county? Is clustering of weapon crimes more extreme than other types of crime? ADDRESSING CHANGE Denver is growing in population, which requires more attention to public safety. Can we identify weapons crime hot spots in order to educate the public as well as efficiently direct police efforts? POINT PATTERN M E T H O DS The spatial scan method returns significant clusters (at the 0.05 level) of weapon related crimes under the random labeling hypothesis. MANAGING RESOURCES How do we respond to clusters of weapon related crimes? Comparing clustering of cases versus controls can help officials determine where and how to direct their efforts. POLICY PROPOSAL I propose stricter gun laws and better mental health resources to reduce weapon related crimes. This will impact the number of weapon related crimes involving a firearm (illegal possession, illegal sale, etc.) Implementing stricter gun laws is a large challenge. BIBLIOGRAPHY 1. Crime Dataset (Denver Police Department) 2. 2010 Denver Census Tracts ( 3. Neighborhood Map (City and County of Denver) WEAPONS CRIME IN DENVER COUNTY IDENTIFYING CLUSTERS VIA CASE/CONTROL SPATIAL DATA ANALYSIS Malik Odeh CONCLUSION We observe clusters of weapon related crimes that are more extreme than what we expect under the random labeling hypothesis. Locating clusters provides public awareness, while comparing clustering between cases and controls can aid in prioritization and preparation for various crimes. In this case, there is no convincing evidence of a weapon related crime epidemic. Crime locations (of all types since 2015) on census tracts suggests clustering that data available through the Denver Police Department Crime locations on census tracts suggests clustering of weapon related crimes around West and East Colfax, downtown, the Cole/Clayton area, and Montbello . This test does not return a cluster of cases downtown! Monte Carlo p values for clusters 1, 2, and 3 are and cluster 4 has a p value of . REGIONAL COUNT M E T H O DS The Besag Newell method returns the most significant clusters (at the 0.05 level) of weapon related crimes under the random labeling hypothesis. The red cluster has a p value of , the green cluster has , and blue has . Each cluster window expands until they contain 65 cases and are compared to what is expected under the random labeling hypothesis. Above: A contour plot of the log ratio of density functions including 95% tolerance envelopes. There is clustering of cases over controls and vice versa in specific locations. Below: A plot of the difference in K functions between cases and controls. There is no evidence of clustering of cases over controls or vice versa at any spatial scale. There is no convincing evidence that weapon related crimes are more clustered than non weapon related crimes.