Categorizing places based on their network connections to other places in the region reveals not only population concentration but also economic dynamics that are missed in other typologies. The US Office of Management and Budget categorization of counties into metropolitan/micropolitan and central/outlying is widely seen as insufficient for many analytic purposes. In this article, we use a coreness index from network analysis to identify labor market centrality of a county. We use county-to-county commute flows, including internal commuting, to identify regional hierarchies. Indicators broken down by this typology reveal counterintuitive results in many cases. Not all strong core counties have large populations or high levels of urbanization. Employment in these strong core counties grew faster in the postrecession (2008–2015) than in other types of counties. This economic dimension is missed by other typologies, suggesting that our categorization may be useful for regional analysis and policy.

根据与区域中其他地方的网络连接来对地方进行分类，不仅揭示了人口集中程度，还揭示了其他类型中所遗漏的经济动态。人们普遍认为，美国行政管理和预算局将县分类为大城市/小城市和中央/偏远地区，不足以满足许多分析目的。在本文中，我们使用网络分析中的核心指数来确定一个县的劳动力市场中心度。我们使用县到县的通勤流程（包括内部通勤）来识别区域层次结构。按这种类型划分的指标在许多情况下显示出违反直觉的结果。并非所有的核心县都人口众多或城市化水平很高。经济衰退后（2008-2015年），这些实力雄厚的核心县的就业增长快于其他类型的县。其他类型方法忽略了这种经济因素，这表明我们的分类可能对区域分析和政策有用。