AAG 2014 Paper – A genealogy of data assemblages: tracing the geospatial open access and open data movements in Canada

The following paper has been accepted for presentation at the AAG 2014 Data-based living: peopling and placing ‘big data session organised by Matt Finn (Durham University, UK), in Florida.

A genealogy of data assemblages: tracing the geospatial open access and open data movements in Canada

Authors: Tracey P. Lauriault and Rob Kitchin, NIRSA, NUI Maynooth

The field of geomatics has for decades concerned ‘big data’ about people and places, and the monitoring and managing of population, resources and territory.To better carry out this function global, regional, national and sub-national spatial data infrastructures have been built. SDIs are defined as the institutions, policies, technologies, processes and standards that direct the who, how, what and why geospatial data are collected, stored, manipulated, analyzed, transformed and shared.They are also inter-sectoral, cross-domain, inter-departmental, distributed and interoperable authoritative large biopolitical systems. As part of these projects a loose coalition of highly skilled actors have sought to open such geospatial data from state bodies for wider use.Some of these actors have been joined by a nascent open data movement.To date, however, the complex unfolding of the geospatial open access to/data movement has not been charted.In this paper we provide such a genealogical analysis, tracing the open access/data movement in Canada over the past three decades, unpacking the various overlapping, co-evolving and oppositional data assemblages.We conceive a data assemblage as a complex socio-technical system consisting of a number of inter-related elements — systemsof thought; forms of knowledge; finance; political economy; governmentalities; materialities and infrastructures; practices; organisations and institutions; subjectivities and communities; places; and marketplaces — that work together to frame how data are produced, managed, analyzed, shared and used. We suggest that such a conception and approach has utility in understanding and contextualizing the wider changing data landscape.

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