Communities share challenges with the neighborhoods to which former residents move and the neighborhoods from which new residents arrive. A lack of migration data for small geographic areas, however, makes it difficult to identify places that share populations over time. This article uses longitudinal consumer credit data to evaluate the spatial connectivity of neighborhoods. The paper develops a methodology for the construction of Small-Area Moving Ratios (SMvRs), motivating the approach with two applications: (1) the visualization of residential mobility ties across Massachusetts neighborhoods and (2) the application of a community detection algorithm to identify communities of strongly interconnected places. The research produces novel evidence showing that the connectivity between neighborhoods differs for socioeconomically advantaged versus for disadvantaged movers. This work shows how longitudinal, geolocated business administrative datasets can be repurposed to produce planning-relevant insights.
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