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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.

Keywords

unsupervised machine learning economic development county typology rurality

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

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