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Urban researchers have long debated the extent to which urban 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 MSA, 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 MSAs about 75% of jobs are located on the 10% of built land in Census blocks with at least twice as many jobs as people. Further, most of these jobs are in clustered business districts of more than 5 contiguous employment blocks. These relative proportions are extremely consistent across MSAs, even though cities 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 form. As one output, it provides an inductive, data-driven method of identifying city centers for the purposes of urban economic analysis.