This city is not a bin: User-generated content to map litter

publications 1 Minute

I am pleased to share this new article that just appeared in the Journal of Industrial Ecology. It is a inter-disciplinary study between industrial ecology and geographic data science that I crafted with my esteemed colleagues in Leiden Stefano Cucurachi et al. It is nice to see that the study received some media coverage. The full text is available as open access in the journal.

Abstract: Urban litter, such as cans, packaging, and cigarettes, has significant impacts, and yet little is known about its spatio-temporal distribution, with little available data. In contexts of data scarcity, crowdsourcing provides a low-cost approach to collecting a large amount of geo-referenced data. We consider 1.7 million litter observations in the Netherlands, collected by the crowdmapping project Litterati. First, we analyze the biases of this data at the province and municipality level. Second, in a local case study with high-quality data (the city of Purmerend), we investigate the spatial distribution of urban litter and the points of interest that attract it. This study’s findings can support both the crowdmapping process, steer volunteers’ efforts, and policy-making to tackle litter at the urban level.

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Distribution of litter from Litterati data in the Netherlands. Total litter observations: 1.71 million. The maps are north oriented. (a) Density of litter observations in the Netherlands (2016–2019) with province borders. Observations grouped into five logarithmic bins, over 4405 spatial units (each hexagon covers 10 km2). (b) Hotspot analysis with Getis–Ord Gi* with significance level. Hotspots are labeled with municipality names.
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Litter observations from Litterati per 1000 people in the Netherlands (2016–2019). (a) Province level, with manual bins (provincies, 12 units). (b) Municipality level, with Jenks bins (gemeentes, 388 units). Total litter observations: 1.71 million, total population: 17.2 million. Population data from Dutch Census (2017).

Keywords: crowdmapping, litter, Litterati, spatial analysis, waste

Reference: Ballatore, A., Verhagen, T. J., Li, Z., Cucurachi, S. (2021) This city is not a bin: Crowdmapping the distribution of urban litter, Journal of Industrial Ecology  [web] [data] [pdf]

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