KeywordsNeighborhood -- Arizona -- Tucson.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Collection InformationThis item is part of the MS-GIST Master's Reports collection. For more information about items in this collection, please contact the UA Campus Repository at firstname.lastname@example.org.
AbstractCrime throughout the Tucson city area reaches six figures every year. Over ten percent of these crimes are considered to be violent: murder, aggravated assault, rape, and robbery. It is a widely accepted belief that violent crime is a factor of vulnerability in a neighborhood and can be found in conjunction with certain socioeconomic factors. In 2020, a study conducted by the University of Arizona and the City of Tucson determined that five major socioeconomic factors determine the vulnerability of a neighborhood. These factors did not include crime, but the percentage of residents identifying as anything other than “non-Hispanic white alone”, percent of households who rent, rather than own, their homes, percent of residents aged 25 and over who lack a four-year bachelor’s degree or higher, percent of households with incomes below 80% of the Area Median Income (as determined by HUD), and the share of children that live in households below the official poverty line. This Master’s Project analyzes the five major socioeconomic factors along with violent crime statistics to determine whether vulnerable neighborhoods are also victims of violent crime. The analysis consists of City of Tucson crime reports between 2019 and 2021, spanning the time before and after the study was done to show that neighborhood vulnerability factors and violent crime are statistically significant to each other. Using spatial autocorrelation and regression analysis and ESRI’s ArcGIS Pro, violent crime can be associated with almost all factors of what is considered a vulnerable neighborhood. Analyses conducted include Kernel Density, Average Nearest Neighbor, Global Moran’s I, Geographically Weighted Regression, and Exploratory Regression. The results will be able to aid the City of Tucson in furthering its efforts to prevent violent crime throughout the city and aid the neighborhoods that need the most help.