author = { Chan-Tack, Anjanette M. },

title = { The case for spatially-sensitive data: how data structures affect spatial measurement and substantive theory },

publisher = {},

keywords = { Nachbarschaft , Datengewinnung , regionale Faktoren , Raum , Stadtsoziologie , Stadtforschung , Einzelhandel , Forschungsansatz , Statistik , Analyse , spatial regression , spatially-sensitive data , spatial measurement , ecological validity , Modifiable Areal Unit Problem (MAUP) , retail red-lining , supermarket access , neighborhood effects },

year = {2014},

abstract = {Veröffentlichungsversion},

abstract = {begutachtet (peer reviewed)},

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.},

address = { },

url = { http://slubdd.de/katalog?TN_libero_mab2 }