iBinned.it
A rubbish idea for generating datasets with mapping
Overview
This idea was conceived when speaking to friends about Manchester’s litter problem, something that the city is not unique for but is constantly plagued by. A friend and colleague had the idea for a “where’s the nearest bin?” application, that would direct individuals to the the nearest appropriate bin, according to their GPS location and what variety of litter they needed to discard.
Beyond this basic functionality, we thought it might be a more engaging experience if users could actively track variety of rubbish tey were discarding while using the app, as well as tracking the variety of litter they were finding, and what state the bins were in when they reached them.
The rubbish data
As it turned out, the existing datasets for these were less than helpful. Some enthusiastic individuals had made some effort to add bins to Open Street Map, but the dataset was largely incomplete and inconsistent. After approaching the local council for data on their bins, I was provided with a spreadsheet that provided a shockingly low amount of detail about each bin’s location, type, etc.
In fact, some of the bin labelling in the spreadsheet was so poor, that some of the lines read things like “opposite shop X” where “shop X” might have closed or changed name in the time since the sheet was compiled.
A new direction
With no dataset to pick up and begin consuming, I took a new approach and considered that users of the app could generate the dataset themselves. Following an invitation by the Manchester School of Art to teach some of their students about the important role data plays in our lives, and how data can be used in creative projects, I brought a functioning version on of the application to the workshop as an activity for us all to participate in.
As Covid restrictions were still in place, students joined from home. During the section of the workshop on dataset generation, I invited each participant to go for a short 15-20 minute walk to begin logging what bins and litter they found of their travels. In total around 50 bins were logged and even more litter. One student even managed to map every bin in her small home village of Feeny, Northern Ireland.
Beyond this, the dataset was something we could use in the workshop to generate more images and sound, using parameters such as litter density and proximity to bins to alter synthesiser parameters such as pitch, amplitude, and modulation.