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.

长期以来，城市研究人员一直在争论大都市就业是单中心，多中心或分散的程度。在本文中，我使用基于失业保险记录的高分辨率数据来表明，美国大都市地区的就业不是集中的，而是空间集中的。与居民形成一个覆盖整个大都市大部分地区的连续表面的居民不同，工作具有双峰的空间分布，其中大多数街区根本没有任何工作，而少数街区的就业密度却很高。在100个最大的大都市统计区中，约有75％的工作岗位位于人口普查区6.5％的建筑用地上，至少是人们的两倍。这些相对比例在各个城市之间极为一致，即使它们的构造物理密度差异很大。基于这些经验规律，我引入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，该算法比当前方法更易于实现且更灵活，不需要特定于地铁的调整参数，也无需假设城市空间布局。