Smoking and Activity Space

Project Summary

Computer Science major Yumin Zhang and IIT student Akhil Kumar Pabbathi spent ten weeks working closely with Dr. Joe McClernon from Psychiatry and Behavioral Sciences to understand smoking and tobacco purchase behavior through activity space analysis.

Themes and Categories
Year
2016

Project Results

The team developed a robust algorithm to extract meaningful features from GPS tracking and subject-indicated smoking and tobacco purchase data, and verified purchases with locations of tobacco retail outlets in Durham County. Yumin and Akhil used clustering techniques and network analysis to compare activity space and length-of-stay behaviors between smokers and non-smokers. Their computational framework has brought valuable insights to the clinical study team and will play an integral role in future studies carried out by Dr. McClernon and colleagues.

Download the Executive Summary (PDF)

Faculty Sponsor

  • Joe McClernon, Associate Professor, Psychiatry and Behavioral Sciences

"Our DATA+ team did wonderful work. I’d wanted a software tool for quantifying human mobility data using network analysis for 5 years—the team got it done in 8 weeks! Paul and Ashlee run a fantastic program that requires minimal oversight on the part of faculty but with maximum returns. Sign me up for next year!" Joe McClernon, Associate Professor, Psychiaty and Behavioral Sciences

Project Manager

  • Tianyuan Liu, UNC-Chapel Hill Transportation Planning and Health Behavior

Participants

  • Yumin Zhang, Duke University Computer Science
  • Pabbathi Akhil Kumar, Duke University Electrical and Computer Engineering

Disciplines Involved

  • Economics
  • Sociology
  • Public Health
  • Psychology
  • All quantitative STEM

Related People

Related Projects

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

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:

 

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: