Regional scientists and geographers have long tried to uncover the keys to regional success through comparative studies. From Chinitz's (1961) comparison of Pittsburgh and New York to Saxenian's (1994) pairing of Silicon Valley and Route 128, these studies have generally adopted an exceptionalist or historicist perspective, in which what is important is not the commonality of processes that produce the spatial world we see, but the unique results that can only be understood by deep observation of specific regions. The contrast between two regions helps to highlight the differences in regional assets (in particular, factor endowments), as well as regional institutions, actors, and cultures, that have led to different outcomes. Of particular interest in these studies is the ability of regions to adapt, to reinvent themselves after a downturn, as Silicon Valley did in the late 1980s, due in large part to the networked structure of its economy (Saxenian, 1994). In contrast, the systematic perspective seeks to analyze the processes of change and the phenomena that they produce on a large (e.g., national) scale. By detecting patterns across a large number of regions, researchers hope to find commonalities that can lead to large-scale policy reform. Examples of large-scale pattern detection studies abound, from city ranking studies (Chapple et al., 2004; Hill, Wolman & Ford, 1995), to a growing literature on metropolitan disparities (Orfield 2002; Rusk 1993). The advantage of these systematic studies is that they are able to identify outliers, or regions that have performed exceptionally well, and suggest common factors behind success that are likely to be replicable across regions. The disadvantages are that they typically look at success at one point in time, rather than success or adaptiveness through time, and attribute success to a set of variables that are by definition universal across regions.