Datafying Disaster: Institutional Framings of Data Production Following Superstorm Sandy

Ryan Burns

Annals of the American Association of Geographers, 2018, vol. 108, issue 2, 569-578

Abstract: In the wake of disasters, communities organize to produce spatial data capturing knowledge about the disaster and to fill gaps left by formal emergency responders. The ways in which communities affect overall response efforts can produce inequalities, disempowerment, or further marginalization. Increasingly, this organizing and knowledge production occurs through digital technologies and, recently, digital humanitarianism has become an important suite of such technologies. Digital humanitarianism includes technologies like the crowd-sourced crisis mapping platform Ushahidi and the community of volunteers Humanitarian OpenStreetMap Team, which focuses on the amateur-generated global base map OpenStreetMap. Digital humanitarianism is shifting how needs and knowledges are captured and represented as data following disasters. These transformations raise important questions for geographers interested in the sociopolitical and institutional processes that frame data production and representation. In this article, I contribute to geographers' efforts to understand the institutional and community-based politics that frame the types of data that are produced in disaster contexts by drawing on an ethnographic project that took place in both Washington, DC, and New York City after Superstorm Sandy in 2012. I show that digital humanitarians produced data in the Rockaway Peninsula of New York in response to perceived gaps on the part of formal emergency responders. In so doing, they represented needs, individuals, and communities in ways that local community advocacy organizations found problematic. These findings shed light on the politics and struggles around why particular data sets were produced and the motives behind capturing particular disaster-related needs and knowledge as data.

Date: 2018
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DOI: 10.1080/24694452.2017.1402673

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