Superseded by Working Paper 12-22> The authors document the spatial concentration of more than 1,000 research and development (R&D) labs located in the Northeast corridor of the U.S. using point pattern methods. These methods allow systematic examination of clustering at different spatial scales. In particular, Monte Carlo tests based on Ripley's (1976) K-functions are used to identify clusters of labs — at varying spatial scales — that represent statistically significant departures from random locations reflecting the underlying distribution of economic activity (employment). Using global K-functions, they first identify significant clustering of R&D labs at two different spatial scales. This clustering is by far most significant at very small spatial scales (a quarter of a mile), with significance attenuating rapidly during the first half mile. The authors also observe statistically significant clustering at distances of about 40 miles. This corresponds roughly to the size of the four major R&D clusters identified in the second stage of their analysis — one each in Boston, New York-Northern New Jersey, Philadelphia-Wilmington, and Virginia (including the District of Columbia). In this second stage of the analysis, explicit clusters are identified by a new procedure based on local K-functions, which they designate as the multiscale core-cluster approach. This new approach yields a natural nesting of clusters at different scales. The authors' global finding of clustering at two spatial scales suggests the possibility of two distinct forms of spillovers. First, the rapid attenuation of significant clustering at small spatial scales is consistent with the view that knowledge spillovers are highly localized. Second, the scale at which larger clusters are found is roughly comparable to that of local labor markets, suggesting that such markets may be the source of additional spillovers (e.g., input sharing or labor market matching externalities).