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.

虽然农村的概念在学术和专业文献中已经争论了几十年，但对可以指导本地经济发展战略的实用类型学的研究却很少。本文通过将无监督机器学习 (ML) 应用于美国现有的大量县级数据，增加了越来越多的文献，以寻找更细致入微的农村定义。作者说明了这种方法如何导致一种新的县类型学，以经济发展战略命名，说明资源、机遇和挑战的特质。这项研究是朝着易于处理的异构分类迈出的实际步骤，可以为联邦、州和地方政策制定者、经济发展从业者和许多其他人的工作提供信息。