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We examine how an Irish stigmatised neighbourhood is represented by Google Street View. In spite of Google’s claims that Street View allows for ‘a virtual reflection of the real world to enable armchair exploration’ (McClendon, 2010). We show how it is directly implicated in the politics of representations. We focus on the manner in which Street View has contributed to the stigmatisation of a marginalised neighbourhood. Methodologically, we adopt a rhetorical/structuralist analysis of the images of Moyross present on Street View. While Google has said the omissions were ‘for operational reasons’, we argue that a wider social and ideological context may have influenced Google’s decision to exclude Moyross. We examine the opportunities available for contesting such representations, which have significance for the immediate and long-term future of the estate, given the necessity to attract businesses into Moyross as part of the ongoing economic aspect of the regeneration of this area.

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.

Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across four U.S. cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 percent and 6.8 percent lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.