Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Description
This thesis explores a method of how political information could be distributed to the public and asks the question, what is the best way to provide voters with all of the information they need to cast an informed vote? It involved the creation of a website, www.azleglive.info, which republishes state

This thesis explores a method of how political information could be distributed to the public and asks the question, what is the best way to provide voters with all of the information they need to cast an informed vote? It involved the creation of a website, www.azleglive.info, which republishes state legislative data in interactive and visually condensed formats and asked users to compare it to the existing Arizona State Legislature website on the metrics of depth of information, usability, and clarity. It also asked what resources users would utilize in order to cast a vote in the next election. Ultimately, the majority of users determined that the new website added needed usability and clarity to available legislative information, but that both websites would be useful when voting. In conclusion, the responsibility of disseminating useful information to voters is most likely to be effective when distributed among a variety of sources.
ContributorsJosephson, Zachary (Co-author) / Umaretiya, Amy (Co-author) / Jones, Ruth (Thesis director) / Woodall, Gina (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / School of Politics and Global Studies (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description

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

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.

ContributorsGinjpalli, Shashank (Author) / Bryan, Chris (Thesis director) / Hsiao, Sharon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

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.

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.

ContributorsWang, Olivia (Author) / LiKamWa, Robert (Thesis director) / Gold, Lauren (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
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Description
As record heatwaves are being seen across the globe, new tools are needed to support urban planners when considering infrastructure additions. This project focuses on developing an interactive web interface that evaluates the effectiveness of various shade structures based on certain parameters. The interface requests user input for location, date,

As record heatwaves are being seen across the globe, new tools are needed to support urban planners when considering infrastructure additions. This project focuses on developing an interactive web interface that evaluates the effectiveness of various shade structures based on certain parameters. The interface requests user input for location, date, and shade type, then returns information on sun position, weather data, and hourly mean radiant temperature (MRT). This tool will allow urban city planners to create more efficient and effective shade structures to meet the public’s needs.
ContributorsMuir, Maya (Author) / Maciejewski, Ross (Thesis director) / Middel, Ariane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
Description
In this work, we explore the potential for realistic and accurate generation of hourly traffic volume with machine learning (ML), using the ground-truth data of Manhattan road segments collected by the New York State Department of Transportation (NYSDOT). Specifically, we address the following question– can we develop a ML algorithm

In this work, we explore the potential for realistic and accurate generation of hourly traffic volume with machine learning (ML), using the ground-truth data of Manhattan road segments collected by the New York State Department of Transportation (NYSDOT). Specifically, we address the following question– can we develop a ML algorithm that generalizes the existing NYSDOT data to all road segments in Manhattan?– by introducing a supervised learning task of multi-output regression, where ML algorithms use road segment attributes to predict hourly traffic volume. We consider four ML algorithms– K-Nearest Neighbors, Decision Tree, Random Forest, and Neural Network– and hyperparameter tune by evaluating the performances of each algorithm with 10-fold cross validation. Ultimately, we conclude that neural networks are the best-performing models and require the least amount of testing time. Lastly, we provide insight into the quantification of “trustworthiness” in a model, followed by brief discussions on interpreting model performance, suggesting potential project improvements, and identifying the biggest takeaways. Overall, we hope our work can serve as an effective baseline for realistic traffic volume generation, and open new directions in the processes of supervised dataset generation and ML algorithm design.
ContributorsOtstot, Kyle (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description

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

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.

ContributorsChung, Michael (Author) / Meuth, Ryan (Thesis director) / Samara, Marko (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
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Description
Simulations can be used to help formulate and solve complex problems. Toward this goal, the Arizona Center for Integrative Modeling and Simulation (ACIMS) is a research laboratory at Arizona State University that creates powerful tools for simulating complex systems. Their flagship simulator, DEVS-Suite, allows users to create models that can

Simulations can be used to help formulate and solve complex problems. Toward this goal, the Arizona Center for Integrative Modeling and Simulation (ACIMS) is a research laboratory at Arizona State University that creates powerful tools for simulating complex systems. Their flagship simulator, DEVS-Suite, allows users to create models that can be simulated. The latest version of this simulator supports storing data in Postgres, a relational database that is well suited for storing millions of data points. However, though DEVS-Suite supports real-time visualizations, the simulator does not support the manipulation and visualization of the data stored in the database. As simulations become more complex, users benefit from visualizing time-based trajectories. User-defined data visualization can help gain new insight into generated simulated data.
ContributorsSchaffer, Albert (Author) / Sarjoughian, Hessam (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05