The definition of community is often fuzzy at best. However, communities can have both political impacts (such as determining zones for resource allocation) and social ones (such as influencing how information and culture spread). Communities may be defined by existing political subdivisions (such as counties or municipalities), but more nuanced definitions are often harder to access. While community detection is a widespread interdisciplinary problem, this project is specifically motivated by the concept of communities of interest in electoral redistricting.
Recent approaches to identifying communities of interest have varied. Some rely on community-driven input, such as participatory mapping efforts and narrative-based testimony, to capture the lived experiences and priorities of residents. Others take a more geographical or data-driven approach, leveraging demographic patterns derived from census data or analyzing physical boundaries like municipal lines. In this project, we plan to (i) expand our investigation of community to include metrics that capture ease of exchange and transportation across regions (e.g., road connectivity and type) and (ii) explore novel methods for conceptualizing communities in a hierarchical framework that begins with tightly-knit neighborhoods and expands to clusters of neighborhoods sharing demographic or geographic similarities.
Project Lead: Dr. Ranthony Clark, Mathematics