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.

Given the neighbourhood focus of much regeneration policy, we need to know more about the functional roles that neighbourhoods play in the way that households move within the housing market and hence about the different functional types of neighbourhood amongst deprived areas. Such knowledge would help both to guide the priorities of policy and to interpret the probability of policy interventions being successful. This exploratory study draws on an evaluation of the British government's National Strategy for Neighbourhood Renewal, part of which entails an interpretation of household mobility data from the 2001 Census. It suggests four categories of neighbourhood—transit, escalator, isolate, and improver areas—based on the relationship between where households move to and move from, focused on the 20% most deprived lower super output areas in England. Evidence on the ground suggests the plausibility of the different functional roles played by the four neighbourhood types. Some continuing conundrums—the robustness of the categorisation, the need to take account of the spatial context of deprived areas, and the difference between movers and stayers—are discussed as a prelude to further continuing research.

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The importance of search behaviour has long been recognised in the study of housing markets but research in this area has frequently been hampered by a lack of data. In many nations, the vast majority of initial housing search queries are now conducted online. The and data this generates could, in theory, provide us with better insights into how housing market search operates spatially, in addition to generating new knowledge on the geography of local housing submarkets. This paper seeks to explore these propositions by discussing existing conceptions of search before developing a framework for understanding housing search in the digital age. A large, user-generated housing market search dataset is then introduced and analysed with respect to area definition, submarket geography and search pressure locations. The results indicate that this kind of 'big data' approach to housing research can could generate important new insights for housing market analysts.

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