This thesis is an exploration of my imaginary world through a short narrative with a focus on placemaking in fiction. The narrative follows Dengar, a civil servant estranged from the central government, as he investigates disappearances occurring in the edges of the empire, uncovering secrets related to the empire’s past and the past of the conquered people of Thron. He must navigate a bitter, cold landscape and a dangerous resistance group as he learns more about the real reason behind why he was sent there. Schemes are uncovered and foiled as he makes his way into the core base of the resistance, a towering mountain called Diran. Following the narrative, I explain my inspirations and analyze my narrative from the perspective of placemaking, referring to placemaking scholars such as Basso and Whitridge.

This honors thesis explores using machine learning technology to assist a patient's return to activity following a significant injury, specifically an anterior cruciate ligament (ACL) tear. The goal of the project was to determine if a machine learning model trained with ACL reconstruction (ACLR) applicable injury data would be able to correctly predict which phase of return to sport a patient would be classified in when introduced to a new data set.

Visualizations can be an incredibly powerful tool for communicating data. Data visualizations can summarize large data sets into one view, allow for easy comparisons between variables, and show trends or relationships in data that cannot be seen by looking at the raw data. Empirical information and by extension data visualizations are often seen as objective and honest. Unfortunately, data visualizations are susceptible to errors that may make them misleading. When visualizations are made for public audiences that do not have the statistical training or subject matter expertise to identify misleading or misrepresented data, these errors can have very negative effects. There is a good deal of research on how best to create guidelines for creating or systems for evaluating data visualizations. Many of the existing guidelines have contradicting approaches to designing visuals or they stress that best practices depend on the context. The goal of this work is to define the guidelines for making visualizations in the context of a public audience and show how context-specific guidelines can be used to effectively evaluate and critique visualizations. The guidelines created here are a starting point to show that there is a need for best practices that are specific to public media. Data visualization for the public lies at the intersection of statistics, graphic design, journalism, cognitive science, and rhetoric. Because of this, future conversations to create guidelines should include representatives of all these fields.

The following paper builds upon version one of The Women’s Power and Influence Index (WPI). The WPI Index is a product created by The Difference Engine, a center at ASU, to address gender inequality in the workplace. The WPI Index ranks Fortune 500 companies on various criteria and releases the information to the public in an easy-to-understand manner. Following the first release in 2021, we aim to help the WPI Index continue to grow by researching social movements that can inspire the Index, suggesting additional criteria for version 1.5, and raising awareness through events and social media. Part I of the paper details how social movements have utilized social pressure and social media to create broad change, setting the stage for the WPI Index’s public rankings to incentivize change. Part II provides research on new criteria we propose to be added to the Index for the next release. Lastly, part III covers how we used Tik Tok, events, and partnerships to help the Index gain notoriety. Altogether the paper suggests new directions and provides scientific research to further the goals of the WPI Index.