Utilize este identificador para referenciar este registo: http://hdl.handle.net/20.500.11960/2858
Título: A visual analytics approach for effective radon risk perception in the IoT era
Autores: Lopes, Sérgio Ivan
Bogers, Sanne
Moreira, Pedro Miguel
Curado, António
Palavras-chave: IoT
Visual analytics
Radon risk
Data: 2020
Editora: Springer
Citação: Lopes, S. I., Bogers, S., Moreira, P. M. & curado, A. (2020). A visual analytics approach for effective radon risk perception in the IoT era. In H. Santos, G. V. Pereira, M. Budde, S. I. Lopes & P. Nikolic (Eds.), Science and technologies for smart cities: 5th EAI International Summit, SmartCity360, Braga, Portugal, December 4-6, 2019, Proceedings (pp. 90-101). Springer. https://doi.org/10.1007/978-3-030-51005-3_10
Resumo: Radon gas is one of the most relevant indoor pollutants in areas of slaty and granitic soils, and is considered by the World Health Organization (WHO) as the second-largest risk factor associated with lung cancer. In the IoT era, active radon detectors are becoming affordable and ubiquitous, and in the near future, data gathered by these IoT devices will be streamed and analyzed by cloud-based systems in order to perform the so-called mitigation actions. However, a poor radon risk communication, independently of the technologies and the data analytics adopted, can lead to a misperception of radon risk, and therefore, fail to produce the wanted risk reduction among the population. In this work we propose a visual analytics approach that can be used for effective radon risk perception in the IoT era. The proposed approach takes advantage of specific space-time clustering of time-series data and uses a simple color-based scale for radon risk assessment, specifically designed to aggregate, not only the legislation in force but also the WHO reference level, by means of a visual analytics approach. The proposed methodology is evaluated using real time-series radon data obtained during a long-term period of 7 months.
URI: http://hdl.handle.net/20.500.11960/2858
ISBN: 978-3-030-51005-3
ISSN: 1867-8211
Aparece nas colecções:ESTG - Artigos indexados à WoS/Scopus

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