Title: Dancing with Data: Geospatial Analytics for Tracing Innovation in Urban Dance Culture

Advisors: Dr. Aaron Hipp, faculty fellow and professor in Parks, Recreation and Tourism Management & Dr. Laura Tateosian, faculty fellow and associate teaching professor in the Center for Geospatial Analytics

Abstract: Culturally innovative dance forms convey identity through movement, but the rapid spread of new styles on social media obstructs the recognition and intellectual property claims of original creators. Geospatial analytics and data mining tools offer a tracing framework by analyzing social media conversations, networks, and videos. However, applying quantitative tools to subjective art forms introduces significant methodological and ethical challenges. This dissertation engages with these problems through three studies: analyzing the spatiotemporal diffusion of the dance known as Flexn through using the Diffusion of Innovation theory, constructing a dance-based African American Vernacular corpus to refine NLP models, and quantifying core performance elements in breakdance competitions. This dissertation showcases a critical framework that balances computing with ethical rigor to protect dance innovators.