A web-based visual analytics system for air quality monitoring data

Liao, Zhifang; Peng, Yanni; Li, Yong; Liang, Xin-Gang; Zhao, Ying

doi:10.1109/geoinformatics.2014.6950834

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…A correlation detection view is proposed to visualize the change of air quality. Liao et al [6] used the networked visualized analysis system to monitor air quality data in Beijing, with Geographical Information System (GIS), parallel coordinate, and splattering. Li et al [7] analyzed the air pollution data in Beijing and used a two-dimensional diagram for the pollution levels of areas.…”

Section: Visualized Analysis Of Air Quality Datamentioning

confidence: 99%

Visualization of real-time monitoring datagraphic of urban environmental quality

Chen

2019

J Image Video Proc.

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Quality of urban environment directly affects people health, and it is important to understand the real-time status of urban air quality. Air quality monitoring, data analysis, and visualization can grasp the concentration data of air pollutants in cities. In view of the current air quality monitoring using digital displays, it is difficult for users to intuitively determine the air pollution level with unsatisfied interaction mode of the data query. Using the real-time monitoring data of 23 observation points in Beijing, the work based on Google Earth applied Keyhole Markup Language (KML) for the visualization of air monitoring data. The interactive query makes it easier for users to query air quality, and gradually varied color can visually highlight the air quality level. Visualization of data has stronger expression (more images and more intuitive) than the original data table, which is beneficial for further analysis of data.

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Section: Visualized Analysis Of Air Quality Datamentioning

confidence: 99%

Visualization of real-time monitoring datagraphic of urban environmental quality

Chen

2019

J Image Video Proc.

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“…European Environment Agency proposes European Air Quality Index which is based on five key pollutants that harm people's health and the environment such as particulate matter (PM2.5 and PM10), ground-level ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2) [27]. [14], [17] and [28] cover majority of the pollutants mentioned in the above standards, while [29] consider 12 pollutants. However, pollutant types are not specified in several studies such as [16], [18] and [30].…”

Section: Air Pollutantsmentioning

confidence: 99%

“…Most papers consider basic context information such as current location, time and pollutant type. [28] and [31] provide only locationbased information, while [23] considers time. [14], [15] and [16] consider more context information such as environment and user's personal health features, however, all three researches oriented on indoor air quality.…”

Section: Context Awarenessmentioning

confidence: 99%

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CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms

Nurgazy

2019

EasyChair Preprints

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Recently air pollution became a severe issue in many big cities due to population growth and rapid development of economy and industry. This led to proliferating need to monitor urban air quality in order to avoid personal exposure as well as to make savvy decisions on managing the environment. In the last decades Internet of Things (IoT) is increasingly being applied to environmental challenges, including air quality monitoring and visualization. In this paper we present CAVisAP, a context-aware system for outdoor air pollution visualization with IoT platforms. The system aims to provide context-aware visualization of three air pollutants such as nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5) in the city of Melbourne, Australia. In addition to primary context as location and time, CAVisAP takes into account users’ pollutant sensitivity levels and color vision impairments to provide personalized pollution maps. Experiments are conducted to validate the system and results are discussed.

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“…The results of the analysis provide support for the pollution abatement of specific pollutants and areas. For example, AirVis [4] is a web-based visual analytic system that supports a collaborative analysis of spatiotemporal and multivariate features. However, it is implemented using only eight air quality monitoring stations in Beijing and is incapable of managing big data.…”

Section: Introductionmentioning

confidence: 99%

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

et al. 2019

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Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data.

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A web-based visual analytics system for air quality monitoring data

Cited by 12 publications

References 12 publications

Visualization of real-time monitoring datagraphic of urban environmental quality

Visualization of real-time monitoring datagraphic of urban environmental quality

CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

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