Citation

Sadler RC, Melde C, Zeoli A, Wolfe S, O'Brien M. Appl. Spat. Anal. Policy 2022; 15(1): 117-142.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12061-021-09391-6

PMID

unavailable

Abstract

Geographic information systems enable the study of the spatial distribution of gun violence in American cities. While sociodemographic characteristics and disinvestment patterns have been correlated to their presence, little work has examined spatio-temporal patterns among them. Understanding where shootings are more likely to be fatal may be of great value to criminologists, urban planners, and emergency department researchers. In this paper, we leveraged a crime database for Milwaukee spanning 2006 to 2015, geocoding every crime to its reported location. We ran kernel density analysis on each dataset to derive the relative density of gun homicides and non-fatal shootings. We then created space-time cubes for each crime type. These cubes were used with ESRI's emerging hot spot analysis tool, highlighting different types of hot and cold spots. We appended these results to individual parcels to study the relationship between shooting hot or cold spots and sociodemographic characteristics. Lastly, we identified 'islands' with relatively fewer lethal shootings inside gun homicide hot spots, exploring reasons for their differentiation. While hot spots occurred primarily in African American neighborhoods, the worst areas were in more transitional neighborhoods, perhaps where disinvestment had not yet fully taken hold. Neighborhoods with the highest rate of homicides to total shootings included the most heavily Hispanic neighborhoods in Milwaukee, while lower rates were found in predominately African American neighborhoods. This work introduces a nuance into the discussion on policing gun violence, giving researchers and practitioners a finer lens in which to examine where and why gun violence occurs.

Language: en

Keywords

Crime; Emerging hot spot analysis; GIS; Gun violence