Toward a New Rural Typology: Mapping Resources, Opportunities, and Challenges

Abstract

While the concept of rurality has been debated in academic and professional literature for decades, less research has been done on a practical typology that can guide localized economic development strategies. This paper adds to the growing body of literature in search of a more nuanced definition of rural by applying unsupervised machine learning (ML) to the abundance of existing county-level data in the United States. The authors illustrate how this method can lead to a new county typology, named after economic development strategies, that accounts for idiosyncrasies in resources, opportunities, and challenges. This research serves as a practical step toward tractable, heterogeneous classifications that can inform the work of federal, state, and local policy makers, economic development practitioners, and many others.