Shapiro, A. (2017). Street-level: Google Street View’s abstraction by datafication. New Media & Society, 20(3), 1201–1219. https://doi.org/10.1177/1461444816687293
While aerial photography is associated with vertical objectivity and spatial abstractions, street-level imagery appears less political in its orientation to the particularities of place. I contest this assumption, showing how the aggregation of street-level imagery into “big datasets” allows for the algorithmic sorting of places by their street-level visual qualities. This occurs through an abstraction by “datafication,” inscribing new power geometries onto urban places through algorithmic linkages between visual environmental qualities, geographic information, and valuations of social worth and risk. Though largely missing from media studies of Google Street View, similar issues have been raised in critiques of criminological theories that use place as a proxy for risk. Comparing the Broken Windows theory of criminogenesis with big data applications of street-level imagery informs a critical media studies approach to Google Street View. The final section of this article suggests alternative theoretical orientations for algorithm design that avoid the pitfalls of essentialist equations of place with social character.
Street-level imagery (e.g., available via Google Street View and other platforms) is often presented as a non-political representation of locations, which is a position the author of this study confronts via the study of the way two street-level imagery software platforms are programmed. The author argues such platforms: (a) include a flawed “sociological assumption” (p. 13) of linking place with social character, (b) ascribe an “aura of objectivity” (p. 13) onto such geovisual relationships between place and social character, and (c) involve a “circumscription of authority” about who can speak for the qualities of places interpreted. As such, the author recommends “critical examination” (p. 14) of platforms that analyze street-level imagery, particularly with a focus on a “Broken world approach” which would frame underlying questions of such programs around identifying needs for resources, understanding for the source of disparities, and participatory design to address local needs.
Description of method used in the article
Case studies of two street-level imagery based algorithmic programs designed to create associations between images and social factors: (a) PlacePulse, developed at the MediaLab at MIT uses crowdsourcing surveys to inform an algorithm that can align street-level images with perceptions (safety, lively, boring, wealthy, depressing, etc.), and (b) City Forensics, developed at University of California, Berkeley, which creates correlations between visual elements and non-visual social issues (e.g., crime, real estate values). The analysis focuses on the degree to which these programs perpetuate problematic aspects of Broken Windows theory.
Of some practical use if combined with other research