The case for spatially-sensitive data: how data structures affect spatial measurement and substantive theory

Abstract

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.