Air pollution is the second biggest environmental concern for Europeans after climate change and the major risk to public health. It is imperative to monitor the spatio-temporal patterns of urban air pollution. The TRAFAIR air quality dashboard is an effective web application to empower decision-makers to be aware of the urban air quality conditions, define new policies, and keep monitoring their effects. The architecture copes with the multidimensionality of data and the real-time visualization challenge of big data streams coming from a network of low-cost sensors. Moreover, it handles the visualization and management of predictive air quality maps series that is produced by an air pollution dispersion model. Air quality data are not only visualized at a limited set of locations at different times but in the continuous space-time domain, thanks to interpolated maps that estimate the pollution at un-sampled locations.

Visual analytics for spatio-temporal air quality data / Bachechi, Chiara; Desimoni, Federico; Po, Laura. - (2020). (Intervento presentato al convegno 24 International Conference Information Visualisation (IV2020) tenutosi a Victoria University, Australia & Technische Universität Wien, Austria nel 7-11 September 2020).

Visual analytics for spatio-temporal air quality data

Chiara Bachechi
;
Federico Desimoni
;
Laura Po
2020

Abstract

Air pollution is the second biggest environmental concern for Europeans after climate change and the major risk to public health. It is imperative to monitor the spatio-temporal patterns of urban air pollution. The TRAFAIR air quality dashboard is an effective web application to empower decision-makers to be aware of the urban air quality conditions, define new policies, and keep monitoring their effects. The architecture copes with the multidimensionality of data and the real-time visualization challenge of big data streams coming from a network of low-cost sensors. Moreover, it handles the visualization and management of predictive air quality maps series that is produced by an air pollution dispersion model. Air quality data are not only visualized at a limited set of locations at different times but in the continuous space-time domain, thanks to interpolated maps that estimate the pollution at un-sampled locations.
2020
set-2020
24 International Conference Information Visualisation (IV2020)
Victoria University, Australia & Technische Universität Wien, Austria
7-11 September 2020
Bachechi, Chiara; Desimoni, Federico; Po, Laura
Visual analytics for spatio-temporal air quality data / Bachechi, Chiara; Desimoni, Federico; Po, Laura. - (2020). (Intervento presentato al convegno 24 International Conference Information Visualisation (IV2020) tenutosi a Victoria University, Australia & Technische Universität Wien, Austria nel 7-11 September 2020).
File in questo prodotto:
File Dimensione Formato  
Dashboard_IV2020.pdf

Open Access dal 16/10/2020

Descrizione: Articolo conferenza IV 2020
Tipologia: Versione originale dell'autore proposta per la pubblicazione
Dimensione 5.61 MB
Formato Adobe PDF
5.61 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1208368
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 7
social impact