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作者

Nikhil Kaza Katherine Nesse

来源

INTERNATIONAL REGIONAL SCIENCE REVIEW,

语言

英文

关键字

作者单位

1Environment, Ecology and Energy Program, Department of City and Regional Planning, University of North Carolina at Chapel Hill, NC, USA;2Community Development Department, City of Bellevue, WA, USA

摘要

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.