Association between crime, places, and neighborhood characteristics

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Title: The association between crime, places, and neighborhood characteristics.

Mentor: Dr. Aleksandra J. Snowden

Approach: Develop spatially informed models using place- and neighborhood-based measures to predict crime patterns in Milwaukee, Wisconsin. Students will gain experience with spatial data analytics – data cleaning and management, geocoding, mapping, conducting exploratory spatial data analysis, and estimating spatially informed regression models to predict crime patterns. Summary: While crime levels have been declining in the United States, crime has been consistently high in Milwaukee, Wisconsin. Milwaukee’s violent crime rate is over four times higher than the national crime rate or the Wisconsin crime rate, and crime is spatially clustered in some parts of the city. Prior studies have examined neighborhood characteristics that are associated with crime. However, we know less about the role of place- and neighborhood- characteristics that are associated with crime occurring in close proximity to alcohol selling establishments. The goal of this study is to utilize socio-economic, alcohol license, and crime data from Milwaukee, Wisconsin, aggregated to U.S. census block groups and estimate spatially lagged regression models to identify key factors that may be used to predict crime occurring in close proximity to alcohol selling establishments. These findings have the potential to inform theoretical explanations of the alcohol-violence relationship and may be beneficial when considering and designing custom tailored local alcohol policy to reduce alcohol-related harm.

Student Research Activities: The REU fellows will perform the following major tasks:

  • Read theoretical and empirical literature on neighborhoods, places, and crime.
  • Identify appropriate measures for theoretical concepts (i.e., neighborhoods, social disorganization, crime).
  • Develop testable hypotheses.
  • Survey publicly available crime data.
  • Download, pre-process, and manage geospatial data; prepare datasets for analyses.
  • Estimate exploratory spatial data models.
  • Estimate spatial regression models.

Student Background: Students need to have basic computing knowledge and introductory programming skills in Python or R.