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The spatial structure of US metropolitan employment: New insights from administrative data

机译:美国大都会就业的空间结构:行政数据的新见解

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Urban researchers have long debated the extent to which metropolitan employment is monocentric, polycentric, or diffuse. In this paper I use high-resolution data based on unemployment insurance records to show that employment in US metropolitan areas is not centralized but is spatially concentrated. Unlike residents, who form a continuous surface covering most parts of each metropolitan area, jobs have a bimodal spatial distribution, with most blocks containing no jobs whatsoever and a small number having extremely high employment densities. Across the 100 largest Metropolitan Statistical Areas, about 75% of jobs are located on the 6.5% of built land in Census blocks with at least twice as many jobs as people. These relative proportions are extremely consistent across cities, even though they vary greatly in the physical density at which they are constructed. Motivated by these empirical regularities, I introduce an algorithm to identify contiguous business districts and classify them into four major types. Based solely on the relative densities of employment and population, this algorithm is both simpler to implement and more flexible than current approaches, requiring no metro-specific tuning parameters and no assumptions about urban spatial layout.
机译:城市研究人员长期以来争论大都会就业的程度,单眼,多中心或弥漫。在本文中,我使用基于失业保险记录的高分辨率数据,以表明美国大都市地区的就业不集中,但在空间集中。与形成连续表面的居民不同,覆盖每个都市区域的大多数部分,就业机会具有双峰空间分布,大多数块不包含任何具有极高的就业密度的少数工作。在100个最大的大都市统计领域,大约75%的工作位于人口普查块中的6.5%的建造土地上,至少有两倍的工作。这些相对比例跨城市非常一致,即使它们在构造的物理密度方面变化很大。通过这些经验规则的动机,我介绍了一种算法来识别连续的商业区,并将它们分为四种主要类型。仅基于就业和人口的相对密度,该算法既简单又比当前方法更简单,更灵活,要求没有特定于地铁的调整参数,没有关于城市空间布局的假设。

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