Trash | Track

Trash | Track explores how pervasive technologies can expose the hidden removal chain. Location-aware tags were attached to different types of trash, revealing the journey of our everyday objects through the waste management system in a series of real time visualizations.

🗑️ Actor Network Diagram

🗑️ Ontological Analysis

🗑️ Contextual Analysis

In 2009, the political climate surrounding U.S. trash and recycling policy was marked by several factors:

🗑️ Representational Analysis

The authors take a simple representational approach, using clear text overlayed over a dark map background to spatially depict the path that trash takes from its desposed origin to its final facility destination. The original papers published by the authors include a series of color coded graphs and charts that visualize the quantity and proportion of disposed objects that end in various facility types by location and object category. It also qualifies object categorydestination. The papers published by the authors include a series of color coded charts that visualize the quantity and proportion of disposed objects that end in various facility types by location and object category. It also qualifies object desposal paths as good, fair, or bad. These graphs are used to effectively communicate their conclusions on the effectiveness of the existing recycling system to a general audience.

Trash Track won the 2010 NSF International Science & Engineering Visualization Challenge.

🗑️ Rhetorical Analysis

Trash Track makes the rhetorical claim that shedding light on the invisible infrastructures of waste systems can improve awareness and change citizens' behavior. The project website quotes Italo Calvino: “Nobody wonders where, each day, they carry their load of refuse. Outside the city, surely; but each year the city expands, and the street cleaners have to fall farther back. The bulk of the outflow increases and the piles rise higher, become stratified, extend over a wider perimeter.” Americans are physically and therefore cognitively disconnected from the waste they dispose of. The researchers attempted to close this gap by exposing the waste removal network through real-time data.

Trash Track fits neatly into the Senseable City Lab's wider practice by extending their ongoing agendas of democratizing urban sensing, turning cities into feedback systems, and deconstructing the binary between digital and physical infrastructure.

Despite its success in making visible the invisible, Trash Track fell short in a few aspects. Most notably, its geographic scope was limited to the U.S. It is no secret that much of the U.S.'s waste is transported with the Global South, perpetuating economic and environmental injustices. In 2008, a large portion—roughly 50%—of U.S. recycled materials went to China. Not only are these exported materials not included in the project's dataset (which can be partially explained by the technical infeasibility of implementing trans-national geotag sensing infrastructure), the data visualizations give the illusion that no trash is exported at all. The map shows only U.S. land, in which there is not a clear distinction between processing facilities and transportation facilities. This can lead viewers to perceptually conflate all facility types as the final destination, when in reality many of them—especially around the ports of Seattle—are only one step in the waste's long journey abroad.

On a smaller scale, the fact that trash was deployed only from Seattle and New York reduces the scope of the project to urban areas. The researchers failed to acknowledge this assumption in their published papers, despite the fact that waste disposed in rural land-locked areas likely follows a very different removal network.

Another shortcoming, which Lee, Offenhuber, Biderman, and Ratti address in a 2014 retrospective analysis, is that the project did not lead to behavioural change. The hypothesis that volunteer participation would prime people for building trust in the project and subsequently change their behavior was proven false. This calls into question the ability of visual data alone to incite behavioral change, and suggests the need for deeper political and social interventions beyond improving awareness.

Finally, the project reduces a complex waste removal network into location data, obscuring the humans and policies that drive it. This decenters many actors in the network, from waste worker labor to national recycling politics. The sleek sensors and clean data visualizations work to distance the viewer from the messy reality of waste processing sites, missing an opportunity for greater emotional impact.

Despite these criticisms, Trash Track was an impressive orchestration across researchers, volunteers, and private and public institutions. The sensor tags designed by researchers were novel at the time, and the technical challenges should not be overlooked. The project captured valuable data on the physical path of waste within a specific spatial and temporal domain. It is one iteration in a long lineage of Senseable City Lab projects working to create realtime data that expose the city's hidden functions, making visible the invisible.

🗑️ Bibliography