Ghost Bikes

Project Summary

Lauren Fox (Cultural Anthropology) and Elizabeth Ratliff (Statistics, Global Health) spent ten weeks analyzing and mapping pedestrian, bicycle, and motor vehicle data provided by Durham's Department of Transportation. This project was a continuation of a seminar on "ghost bikes" taught by Prof. Harris Solomon.

Themes and Categories
Health, Interdisciplinary Inquiry, Public Policy
Data+
Year
2017
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Project Results: After extensive data cleaning and consultation with Transportation planners, the team used QGIS to create an R Shiny app that allows users to view and interact with crash maps, as well as understanding them against a backdrop of variables such as time-of-day, weather conditions, and sociodemographic factors.

The team was able to make a series of data-driven policy recommendation to their community partners.

Partially sponsored by an NSF CAREER Award and by the Franklin Humanities Institute Health Humanities Lab

Click here for the Executive Summary

Faculty Lead: Harris Solomon

Project Manager: Collin Mueller, Ph.D. candidate in Sociology

Analytics for Safety in Durham:

 

 

Related People

Related News

How Can You Ride Your Bicycle More Safely on Durham Streets? This Website Can Tell You
Tuesday, September 12, 2017
iiD's Ghost Bikes Data+ team created an interactive website that demonstrates how fac ...
Durham Traffic Data Reveal Clues to Safer Streets
Monday, September 11, 2017
This summer, a team of Duke students partnered with Durham’s Department of Transportation to analy ...

Related Projects

Predictive Maintenance of F15s

The Air Force’s F-15E Strike Eagle jets have parts that wear down and break, causing unscheduled maintenance events that take away valuable time in the air for critical missions and training. Our team, Limitless Data, is working with Seymour Johnson Air Force Base to mine manually entered maintenance data to visualize and predict aircraft failures. We created a prototype data visualization product that will enable maintainers on the flight line and help them identify and repair critical failures before they happen, keeping jets ready to fly, fight and win.

 

Faculty Lead: Dr. Emma Rasiel

Client Lead: Lt. Devon Burger

Project Manger:  Vignesh Kumaresan

Neural Network-Based Self-Adjusting Computational Processors

This project aims to improve the computational efficiency of signal operations, e.g., sampling and multiplying signals. We design machine learning-based signal processing modules that use an adaptive sampling strategy and interpolation to generate a good approximation of the exact output. While ensuring a low error level, improvements in computational efficiency can be expected for digital signal processing systems using the implemented self-adjusting modules.

Project Leads: Yi Feng, Vahid Tarokh

 

Click here to view the project team's poster

 

Watch the team's final presentation (on Zoom) here:

 

Computational Approaches to the History of Cartography

Mapping History has focused on the categorizing, labelling, digitization, and 3D reconstruction of 16th & 17th century maps & atlases of London and Lisbon. Over the course of the summer, the Mapping History team has developed its own unique analytical dataset by painstakingly labelling every element contained within these maps, used python to digitize this dataset, and, now in the projects final stage, has begun the process of reconstructing these historical perspectives in a 3D game engine.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood

 

View the team's final poster here

Watch the team's final presentation (on Zoom) below:

See all Research