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Step-by-step explanation:
The emergence in the United States of large-scale “megaregions” centered on major metropolitan areas is a phenomenon often taken for granted in both scholarly studies and popular accounts of contemporary economic geography. This paper uses a data set of more than 4,000,000 commuter flows as the basis for an empirical approach to the identification of such megaregions. We compare a method which uses a visual heuristic for understanding areal aggregation to a method which uses a computational partitioning algorithm, and we reflect upon the strengths and limitations of both. We discuss how choices about input parameters and scale of analysis can lead to different results, and stress the importance of comparing computational results with “common sense” interpretations of geographic coherence. The results provide a new perspective on the functional economic geography of the United States from a megaregion perspective, and shed light on the old geographic problem of the division of space into areal units.
For numerous reasons, ranging from the widening scale of labor markets to the integration of capital flows, many observers have suggested that the economic geography of the United States is now best understood in terms of “megaregions.” These are assumed to be large regional areas, often cutting across state lines, that are normally centered on major metropolitan hubs and include an orbit of smaller sub-centers. To divide the country into a mosaic of such megaregions, analysts typically rely on a loosely interpretive method which takes into account physical proximity, morphological integration, and cultural similarity, in order to group major and minor cities together with rural areas into coherent regional entities; e.g. Hagler [1] or Kotkin and Schill [2].
However, the empirical problem of dividing space into discrete, bounded, internally-homogenous regions has long been a vexed problem for geographers, with attempts at providing an objective method for regionalization stretching back more than a century [3–6]. While more recent scholarly work has emphasized concepts of connectivity and relationality as theoretical lines of inquiry which lead beyond the confines of bounded space [7–10], the goal of partitioning the United States into functional megaregions shows that the old problem of regional delineation remains very much alive—and unsolved. A geography based solely on what Khanna calls “connectography” [11] fails to offer a practical framework within which to define the bounded geographic areas which continue to mark out spheres of legal jurisdiction, planning authority, transportation, and political representation.
In this paper, our contribution is to offer an empirical approach to detecting and defining megaregions which takes the insights of a relational, flowing concept of geography and puts them to use in service of delineating coherent, bounded regions. We employ a data set of more than 4,000,000 commutes as a proxy for patterns of economic interconnection, given the importance of commutes in structuring the geography of labor markets [12,13]. The volume and resolution of this data set allows us to depict the interconnected nature of these labor markets at a national scale and, we hope, allows us to make a substantive methodological contribution to the study of megaregions in the United States.
We use these flows in order to provide a rigorous and evidence-based assessment of whether “megaregions” exist and, if so, what spatial forms they take. We do so through an exploration of two different approaches: one requiring visual interpretation, and the other relying strictly on algorithmic computation. In the visual interpretation method, we show how flow mapping can be used in order to show the spatial clustering of urban 'megaregions' by employing cartographic techniques that augment a visual recognition of interrelation. In the algorithmic method, we employ network partitioning software developed at the MIT Senseable City Lab [14] in order to assess the utility and reliability of a purely statistical analysis in determining the geographical break points between communities. Such a method hints at a possibility long promised by spatial scientists: a regionalization scheme which relies entirely on spatial laws, rather than contestable human interpretation. However, we caution against the idea that regional units can be incontrovertibly determined by raw mathematical analysis alone, and show how “big data” methods are dependent not only on the reliability of input sources, but also on choices about parameters, and “common sense” checks on results.