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