In this Barrett Honors Thesis, I developed a model to quantify the complexity of Sankey diagrams, which are a type of visualization technique that shows flow between groups. To do this, I created a carefully controlled dataset of synthetic Sankey diagrams of varying sizes as study stimuli. Then, a pair of online crowdsourced user studies were conducted and analyzed. User performance for Sankey diagrams of varying size and features (number of groups, number of timesteps, and number of flow crossings) were algorithmically modeled as a formula to quantify the complexity of these diagrams. Model accuracy was measured based on the performance of users in the second crowdsourced study. The results of my experiment conclusively demonstrates that the algorithmic complexity formula I created closely models the visual complexity of the Sankey Diagrams in the dataset.

Java Mission-planning and Analysis for Remote Sensing (JMARS) is a geospatial software that provides mission planning and data-analysis tools with access to orbital data for planetary bodies like Mars and Venus. Using JMARS, terrain scenes can be prepared with an assortment of data layers along with any additional data sets. These scenes can then be exported into the JMARS extended reality platform, which includes both augmented reality and virtual reality experiences. JMARS VR Viewer is a virtual reality experience that allows users to view three-dimensional terrain data in a fully immersive and interactive way. This tool also provides a collaborative environment for users to host a terrain scene where people can analyze the data together. The purpose of the project is to design a set of interactions in virtual reality to try and address these questions: (1) how do we make sense of larger complex geospatial datasets, (2) how can we design interactions that assist users in understanding layered data in both an individual and collaborative work environment, and (3) what are the effects on the user’s cognitive overload while using these interfaces.

This project tackles a real-world example of a classroom with college students to discover what factors affect a student’s outcome in the class as well as investigate when and why a student who started well in the semester may end poorly later on. First, this project performs a statistical analysis to ensure that the total score of a student is truly based on the factors given in the dataset instead of due to random chance. Next, factors that are the most significant in affecting the outcome of scores in zyBook assignments are discovered. Thirdly, visualization of how students perform over time is displayed for the student body as a whole and students who started well at the beginning of the semester but trailed off towards the end. Lastly, the project also gives insight into the failure metrics for good starter students who unfortunately did not perform as well later in the course.

For our thesis, we analyzed a set of data from the on-going longitudinal study, “Aging In the Time of COVID-19” (Guest et al., ongoing) from the Center for Innovation in Healthy and Resilient Aging at Arizona State University. This study researched how COVID-19 and the resulting physical/social distancing impacted aging individuals' health, wellbeing, and quality-of-life. The survey collected data regarding over 1400 participants’ social connections, health, and experiences during COVID-19. This study gathered information about participants’ comorbid conditions, age, sex, location, etc. We presented this work in the form of a website including the traditional elements of an Honors Thesis as well as a visual essay with the data analysis portion coded with the JavaScript library D3 and a list of resources for our target audience, older adults who are experiencing social isolation and/or loneliness.