A Data-Driven Approach for Tracking Human Litter in Modern Cities

Abstract

In the recent years, human litter, such as food waste, diapers, construction materials, used motor oil, hypodermic needles, etc, is causing growing problems for the environment and quality of life in modern cities. Data about this waste has a significant importance in the field of environmental sciences due to its important use cases that span saving marine life, reducing the risk from natural hazards, community cleaning efforts, etc. In addition, such litter spreads several diseases in urban areas with high populations such as undeveloped neighborhoods in large modern cities. In this paper, we introduce a data-driven approach that enables environmental scientists and organizations to track, manage, and model human litter data at a large scale through smart technologies. We make a major on-going effort to collect and maintain this data worldwide from different sources through a community of environmental scientists and partner organizations. With the increasing volume of collected datasets, existing software packages, such as GIS software, do not scale to process, query, and visualize such data. To overcome this, we provide a scalable data management and visualization framework that digests datasets from different sources, with different formats, in a scalable backend that cleans, integrates, and unifies them in a structured form. On top of this backend, frontend applications are built to visualize litter data at multiple spatial levels, from continents and oceans to street level, to enable new opportunities for both environmental scientists and organizations to track, model, and clean up litter data. The framework is currently managing thirty real datasets and provide different interfaces for different kinds of users.