SaveOurAir is a project developed with Public Data Lab, during two data sprints, at King’s College in London and at Aalborg University in Copenhagen. Focusing on air quality, SaveOurAir explored three ways to make urban data more "local" and "politically relevant" and developed three experiments in data activation. My team developed a project called the "Hot Potato Machine" tool. I was in charge of the ideation and design of the prototype.
Team members: Jack Close, Matteo Sacchi, Jonathan Gray, Nerea Calvillo Gonzalez, Lucy Kimbell, Tommaso Venturini, Axel Meunier
In this project, we wanted to understand and visually explore different ways of responding to and apportioning responsibility for complex issues such as air pollution. Taking cue from Bruno Latour’s call to “follow the actors” (2007) the Hot Potato Machine was envisaged as a way to follow what different actors say about each other in relation to how to tackle air pollution. Rather than focusing on measurements of pollutants, we were interested in how digital data might tell us about different ways of seeing air pollution as an issue, different imagined solutions, the fabric of relationships around it, and where there might be tensions, differences, knots and spaces for movement.
As a test case, we created a prototype of the Hot Potato Machine starting with statements from actors engaged in the issue of air pollution in the London Borough of Camden. Following these statements soon took us to statements at different scales, including to the level of the Greater London Authority and the UK as a whole. Our prototype focuses on a specific “issue story” revealing different views on who is responsible for reducing air pollution from diesel taxis. In order to further extend this work we have developed a worksheet for collecting this kind of data in relation to other locations and issues, which may be used in the context of teaching, research, advocacy or public engagement activities.
We wanted to map public statements by actors across government, business and civil society. To this end, we started by collating, querying and extracting data from policy documents, consultation responses and position papers. As a result of the UK’s digital and public sector information policies, many policy documents and reports are available online (Owen et al, 2013). Our initial exploratory data collection is available here. Having used this data to identify a specific controversy around diesel taxis, we then focused on gathering a subset of data focused on this specific issue. We have developed a worksheet with further considerations for assembling and coding data for the Hot Potato Machine, which may inform subsequent work in this area.