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Accepted Paper:

The boundaries of open data: digitally mapping displacement as an anti-capitalist collective  
Erin McElroy (University of Washington)

Paper short abstract:

Focusing upon the work of the Anti-Eviction Mapping Project - a digital cartography collective documenting gentrification struggles - this paper centers the collective's struggles with data colonialism and open data, questioning what models can be used to maintain relevance, endurance, and autonomy.

Paper long abstract:

This paper focuses upon the work of the Anti-Eviction Mapping Project - a digital cartography and storytelling collective documenting displacement struggles upon gentrifying landscapes (AEMP). While the group attempts to retrieve, analyze, map, and make public various sets of data pertaining to eviction in the San Francisco Bay Area and more recently the cities of Los Angeles, New York, and Cluj (www.antievictionmap.com), several struggles have emerged related to the question of open data. As the AEMP is an unaffiliated activist collective comprised of technologists, cartographers, oral historians, artists, and more working outside the boundaries of academia, the non-profit industrial complex, and for-profit web-mapping worlds, it is often confronted with questions of legitimacy. Often the veracity of its data, the quality of its maps, and the deep collaborative connections it maintains with other housing activist groups foster its endurance and relevance. Yet recently academics and non-profits alike have made attempts to obtain AEMP's data for their own work without compensation or credit, thereby threatening its obsolescence. This paper situates these attempts within emergent conversations on data colonialism and open data, questioning what models autonomous collectives such as the AEMP can create and/or follow to both make its datasets accessible and germane to housing justice movements, and to maintain autonomy and endurance.

Panel B06
Collaboration in/with "open labs": studying the objects of boundary-making and crossing
  Session 1