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.
This article assesses the sources and consequences of public disorder. Based on the videotaping and systematic rating of more than 23,000 street segments in Chicago, highly reliable scales of social and physical disorder for 196 neighborhoods are constructed. Census data, police records, and an independent survey of more than 3,500 residents are then integrated to test a theory of collective efficacy and structural constraints. Defined as cohesion among residents combined with shared expectations for the social control of public space, collective efficacy explains lower rates of crime and observed disorder after controlling neighborhood structural characteristics. Collective efficacy is also linked to lower rates of violent crime after accounting for disorder and the reciprocal effects of violence. Contrary to the “broken windows” theory, however, the relationship between public disorder and crime is spurious except perhaps for robbery.
