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%T The case for spatially-sensitive data: how data structures affect spatial measurement and substantive theory %A Chan-Tack, Anjanette M. %J Historical Social Research %N 2 %P 315-346 %V 39 %D 2014 %K spatial regression; spatially-sensitive data; spatial measurement; ecological validity; Modifiable Areal Unit Problem (MAUP); retail red-lining; supermarket access; neighborhood effects %@ 0172-6404 %~ GESIS %> https://nbn-resolving.org/urn:nbn:de:0168-ssoar-384875 %X Innovations in GIS and spatial statistics offer exciting opportunities to examine novel questions and to revisit established theory. Realizing this promise requires investment in spatially-sensitive data. Though convenient, widely-used administrative datasets are often spatially insensitive. They limit our ability to conceptualize and measure spatial relationships, leading to problems with ecological validity and the MAUP – with profound implications for substantive theory. I dramatize the stakes using the case of supermarket red-lining in 1970 Chicago. I compare the analytical value of a popular, spatially insensitive administrative dataset with that of a custom-built, spatially sensitive alternative. I show how the former constrains analysis to a single count measure and aspatial regression, while the latter’s point data support multiple measures and spatially-sensitive regression procedures; leading to starkly divergent results. In establishing the powerful impact that spatial measures can exert on our theoretical conclusions, I highlight the perils of relying on convenient, but insensitive datasets. Concomitantly, I demonstrate why investing in spatially sensitive data is essential for advancing sound knowledge of a broad array of historical and contemporary spatial phenomena. %C DEU %G en %9 journal article %W GESIS - http://www.gesis.org %~ SSOAR - http://www.ssoar.info