Road traffic crashes involving child passengers, child pedestrians, and child bicyclists are the leading cause of death for people aged 5 to 15 years in the USA. A total of 10,344 children died on US roads in the decade from 2010-2019; a further 4.2 million were hospitalized. Urban design—meaning the overall physical form of cities—is a modifiable environmental feature that can be changed to reduce the immense burden due to child road traffic injuries. Altering the overall configuration of a city’s transportation network affects the way children and other road users routinely travel through urban space, thereby altering children’s risks for being injured or killed in a road traffic crash.

This data science internship will apply graph theory to transportation network data to develop novel measures the urban design of US cities. Specifically, the project will involve using OpenStreetMap vector data to characterize cities’ bicycle networks, pedestrian networks, highway networks, and public transit networks. Whereas most studies of urban design and child mobility and crash risks characterize transportation networks using geographic network measures (e.g., network density measured as miles per square mile), we will consider the networks’ full topological structure (e.g., including spatial relationships between edges [road segments] and nodes [intersections]). We will then relate urban design to child mobility and risks for road traffic crash injury and fatality.

This internship is within the Geospatial and Applied Prevention Science laboratory, led by Dr. Christopher Morrison in the Department of Epidemiology. Experience working with geographic information systems (including the ArcPy package) is highly desirable. Experience working with epidemiologic study designs and demonstrated interest in public health research is advantageous.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Christopher Morrison
  • Center/Lab: Department of Epidemiology
  • The Geospatial and Applied Prevention Science laboratory, led by Assistant Professor Dr. Christopher Morrison, uses spatial data and epidemiologic methods to develop environmental strategies to reduce the health burden due to injury.

Project Timeline

  • Earliest starting date: 3/1/2022
  • End date: 8/31/2022
  • Number of hours per week of research expected during Spring/Summer 2022: ~10
  • Number of hours per week of research expected during Summer 2022: ~40

Candidate requirements

  • Skill sets: Fluency with Python required, prior experience with a geographic information system (e.g. ArcGIS) is advantageous
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: NOT eligible
  • Academic Credit Possible: No