How Mobility and Accessibility Affect Crime Rates: Insights from Mobile Device Location Data

This research study will investigate the possible correlations between mobility, accessibility, and crime rate. A rich mobile device location dataset including detailed anonymized location traces of the mobile devices observed in the city of Baltimore will be combined with the police arrest records to study how mobility and accessibility affect neighborhood safety. The research team will first process and analyze the mobile device location dataset to obtain measures of mobility and accessibility. These measures will be different from the traditional measures in that they will be obtained based on the empirically observed location data. The research team will then build statistical and machine learning tools to model crime rates at zone level and corridor level, using the calculated mobility and accessibility measures, land use variables, and economy-related variables as the covariates. Subsequently, the team will focus on the correlation of the crime rates with the mobility and accessibility variables. The study seeks to inform the decision makers about the transportation-related issues contributing to the lack of safety and offer transportation solutions to crime-related problems, specially at the neighborhoods suffering from high crime rates.

Project

• Status: Active
• Funding: $100000
• Contract Numbers:

  69A43551747123

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Tucker-Thomas, Dawn

• Performing Organizations:

  Urban Mobility & Equity Center

  Morgan State University
  Baltimore, MD  United States  21251
• Principal Investigators:

  Zhang, Lei

• Start Date: 20191001
• Expected Completion Date: 20210330
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Accessibility; Crimes; Location; Machine learning; Mobile communication systems; Mobility; Neighborhoods; Police reports; Safety; Statistical analysis
• Geographic Terms: Baltimore (Maryland)
• Subject Areas: Data and Information Technology; Safety and Human Factors; Transportation (General);

Filing Info

• Accession Number: 01723279
• Record Type: Research project
• Source Agency: Urban Mobility & Equity Center
• Contract Numbers: 69A43551747123
• Files: UTC, RiP
• Created Date: Nov 21 2019 9:28AM