Author: paul.rosen

In this paper, we have implemented an interactive map that can help CleanAirNowKC 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.

We discuss the construction and application of peer review in two visualization courses from different colleges at the University of South Florida. We then analyze student projects and peer review text via sentiment analysis to infer insights for visualization educators, including the focus of course content, engagement across student groups, student mastery of concepts, course trends over time, and expert intervention effectiveness.

In this report, we provide a systematic and comprehensive review of research studies on perception related to visualization. This survey reviews perception-focused visualization studies since 1980 and summarizes their research developments focusing on low-level tasks, further breaking techniques down by visual encoding and visualization type.

In this article we use the PageRank function along with persistent homology to obtain a scalable graph descriptor and utilize it to compare the similarities between graphs.

In this paper, we study the parallel analysis of the construction of Mapper. We give a provably correct parallel algorithm to execute Mapper on a multiple processors. Our algorithm relies on a divide and conquer strategy for the codomain cover which gets pulled back to the domain cover.

We propose a parallel augmented Reeb graph algorithm on triangulated meshes with and without a boundary. That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure.

We present a multi-stage user study focusing on 4 factors—distribution size of clusters, number of points, size ofpoints, and opacity of points—that influence cluster identification in scatterplots. From these parameters, we have constructed 2 models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes.