Visual analytics for spatio-temporal air quality data

Bachechi, Chiara; Desimoni, Federico; Po, Laura; Casas, David Martinez

doi:10.1109/iv51561.2020.00080

Cited by 9 publications

(18 citation statements)

References 8 publications

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“…This work extends a previous work [2], where we mainly refer to the visualizations available in the city of Modena. In this version, the dashboard has been extended https://trafair.eu to Zaragoza and Santiago de Compostela adapting some views and demonstrating the versatility of the proposed solution.…”

Section: Introductionmentioning

confidence: 52%

“…One station is a background station located inside a green area, the other is a traffic station located near a highly congested road. In Zaragoza, there are eight air quality legal stations belonging to three different types: moderate traffic (3), intense traffic (3), background (2). In Santiago de Compostela, two ISO standard air quality stations are available, a background station and a traffic station, both managed by the local meteorological agency Meteogalicia.…”

Section: The Sensor Networkmentioning

confidence: 99%

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Real-Time Visual Analytics for Air Quality

Bachechi

Desimoni

2022

Studies in Computational Intelligence

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Section: Introductionmentioning

confidence: 52%

Section: The Sensor Networkmentioning

confidence: 99%

Real-Time Visual Analytics for Air Quality

Bachechi

Desimoni

2022

Studies in Computational Intelligence

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“…All the data produced are collected and stored in a database. Moreover, raw and calibrated data are available as open data on the Emilia Romagna regional data portal ( (accessed on 30 December 2022)) and also on the National and European data portals; the hourly data of the legal stations are available on the ARPAE data portal ( (accessed on 30 December 2022)); the datasets discussed in Section 6 are available as open data ( (accessed on 30 December 2022)) and displayed in a dashboard [ 55 , 56 ].…”

Section: Airsense Frameworkmentioning

confidence: 99%

Anomaly Detection and Repairing for Improving Air Quality Monitoring

Rollo

Bachechi

2023

Sensors

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Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants’ concentrations can support the identification of urban areas with poor air quality and raise citizens’ awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms’ performances.

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“…García et al [3] developed CrimAnalyzer, a tool to understand crime patterns over time and detect spatial crime hotspots. Bachechi et al [30] developed TRAFAIR to visualize and manage predictive air quality maps in a continuous space-time domain, where the risk is associated with predefined pollutant thresholds. Chae et al [4] developed a visual tool to support the detection of abnormal events for a given geolocation, where some of these events could be classified as disastrous, by using geo-located time-stamped social media data.…”

Section: Spatiotemporal Approaches For Risk Visualizationmentioning

confidence: 99%

A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID‐19 Pandemic

Silva

Ferreira

Filho

2023

Mathematical Problems in Engineering

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Visual analytics tools for spatiotemporal analysis can be used to manage and monitor the propagation of an epidemic. The problem is that dashboards encountered in the literature do not take into consideration how the geolocation characteristics, such as socioeconomic indicators, influence the infection risk or other epidemic variables. This analysis can support health officials in managing the outbreak to consider information about indicators in compartment models for propagation prediction and intervention simulation. The objective of this work was to bring widgets that offer a more profound exploration and analysis of the impact of the pandemic on socioeconomic indicators. In our approach, the association of epidemic variables and indicators can be explored with commonly adopted visualization plots. Also, we propose a way to gather the specialist’s risk perception, and as a result, a risk heatmap is produced, allowing the reduction of time series data cluttering. Finally, it is possible to use the risk heatmap information to compare neighbourhoods and socioeconomic indicators by ranking them according to a severity score. Some use cases were performed to demonstrate the use and capability of the proposed widgets.

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Visual analytics for spatio-temporal air quality data

Cited by 9 publications

References 8 publications

Real-Time Visual Analytics for Air Quality

Real-Time Visual Analytics for Air Quality

Anomaly Detection and Repairing for Improving Air Quality Monitoring

A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID‐19 Pandemic

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