1. Smell Pittsburgh
- Author
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Randy Sargent, Beatrice Dias, Paul Dille, Illah Nourbakhsh, Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang, Michael Tasota, and Jennifer Cross
- Subjects
FOS: Computer and information sciences ,Pollution ,Computer Science - Artificial Intelligence ,media_common.quotation_subject ,Internet privacy ,Computer Science - Human-Computer Interaction ,Air pollution ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,02 engineering and technology ,medicine.disease_cause ,Human-Computer Interaction (cs.HC) ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Citizen science ,0501 psychology and cognitive sciences ,Air quality index ,050107 human factors ,Cardiopulmonary disease ,media_common ,Social and Information Networks (cs.SI) ,business.industry ,05 social sciences ,Computer Science - Social and Information Networks ,Visualization ,Human-Computer Interaction ,Artificial Intelligence (cs.AI) ,Push technology ,business ,Health department - Abstract
Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape. Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities. We also applied regression analysis to identify statistically significant effects of push notifications on user engagement. Our evaluation of this system demonstrates that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns, and can empower communities to advocate for better air quality. All citizen-contributed smell data are publicly accessible and can be downloaded from https://smellpgh.org., Comment: Accepted by ACM Transactions on Interactive Intelligent Systems on 2020. This is an extended version of the arXiv:1810.11143, which was accepted by the ACM IUI 2019 conference. arXiv admin note: substantial text overlap with arXiv:1810.11143
- Published
- 2020
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