Version control systems are integral to software development, with GitHub emerging as a popular online platform due to its comprehensive project management tools, including issue tracking and pull requests. However, GitHub lacks a direct link between issues and commits, making it difficult for developers to understand how specific issues are resolved. Although GitHub’s Insights page provides some visualization for repository data, the representation of issues and commits related data in a textual format hampers quick evaluation of issue management. This paper presents a prototype web application that generates visualizations to offer insights into issue timelines and reveals different factors related to issues. It focuses on the lifecycle of issues and depicts vital information to enhance users’ understanding of development patterns in their projects. We demonstrate the effectiveness of our approach through case studies involving three open-source GitHub repositories. Furthermore, we conducted a user evaluation to validate the efficacy of our prototype in conveying crucial repository information more efficiently and rapidly. 

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As cities continue to grow globally, air pollution is increasing at an alarming rate, causing a significant negative impact on public health. One way to affect the negative impact is to regulate the producers of such pollution through policy implementation and enforcement. CleanAirNowKC (CAN-KC) is an environmental justice organization based in Kansas City (KC), Kansas. As part of their organizational objectives, they have to date deployed nine PurpleAir air quality sensors in different locations about which the community has expressed concern. In this paper, we have implemented an interactive map that can help the community members to monitor air quality efficiently. The system also allows for reporting and tracking industrial emissions or toxic releases, which will further help identify major contributors to pollution. These resources can serve an important role as evidence that will assist in advocating for community-driven just policies to improve the air quality regulation in Kansas City.

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This project is a collaboration with USF Health. Asthma Time is an app to assist in the self-management of asthma. It is designed for adolescents to track their asthma symptoms better and control them. Features include weather, pollution, and pollen tracking, tracking of symptoms, reminders for medications, and more. 

Check out the app at the Google Play Store or Apple App Store!

Visualizing crowded temporal data in line charts presents challenges for trend identification and predicting the future. To address this, we explored three alternatives: aggregated, trellis, and spiral charts, comparing them to the standard line chart. Our human subject study assessed the impact of each one of these visualizations on trend identification, prediction accuracy, and decision-making. Our findings showed that aggregated charts performed comparably to standard ones, excelling in trend recognition and prediction, while trellis and spiral charts lagged significantly. To understand the impact on decision-making, subjects played a trust game, and the results showed similar trust in standard and aggregated charts, varied trust in spiral charts, and a leaning toward distrust in trellis charts. These results offer valuable insights for practitioners visualizing temporal data by providing guidelines for selecting appropriate visualization strategies to improve the effectiveness of data analysis and interpretation, which can ultimately enhance decision-making processes.

 Line charts can surface many relevant features in time series data, from trends to periodicity to peaks and valleys. However, not every potentially important feature in the data may correspond to a visual feature that readers can detect, attend to, or value. In this work, we perform a mixed-methods study, where participants engage in a visual stenography task in which they re-draw line charts, to solicit information about the visual features that participants believe to be important in line charts and how faithfully and accurately they recreate them. We identified three predominant strategies, whose use correlated with the noise present in the stimuli: the replicators attempted to retain all major features of the line chart; the trend keepers faithfully retained trends but no other features; and the overwhelmed only represented the noise. Further, we found that participants tended to faithfully retain trends and peaks and valleys when these features were present, while periodicity and noise were represented in more qualitative or gestural ways: semantically rather than accurately. These results suggest a need to consider more flexible and human-centric ways of presenting, summarizing, pre-processing, or clustering time series data.