Bumps and Bruises in the Digital Skins of Cities: Unevenly Distributed User-Generated Content Across U.S. Urban Areas
Cartography and Geographic Information Science, Forthcoming
45 Pages Posted: 27 Aug 2015
Date Written: August 25, 2015
As momentum and interest builds to leverage new user-generated forms of digital expression with geographical content, classical issues of data quality remain significant research challenges. In this paper we highlight the uneven textures of one form of user-generated data: geotagged photographs in U.S. urban centers as a case study into representativeness. We use generalized linear modeling to associate photograph distribution with underlying socioeconomic descriptors at the city-scale, and examine intra-city variation in relation to income inequality. We conclude with a detailed analysis of Dallas, Seattle, and New Orleans. Our findings add to the growing volume of evidence outlining uneven representativeness in user-generated data, and our approach contributes to the stock of methods available to investigate geographic variations in representativeness. We show that in addition to city-scale variables relating to distribution of user-generated content, variability remains at localized scales that demand an individual and contextual understanding of their form and nature. The findings demonstrate that careful analysis of representativeness at both macro and micro scales simultaneously can provide important insights into the processes giving rise to user-generated datasets and potentially shed light into their embedded biases and suitability as inputs to analysis.
Keywords: USA, representativeness, modeling, user-generated content, spatial patterns
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