Abstract
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.
Highlights
With the rapid development of the social economy and the improvement in public life conditions, urban air pollution has become a hot topic and has attracted progressively more attention [1]
Considering that there are still some noteworthy anomalies hidden in regular patterns that reflect significant changes among similar timestamps or adjacent cities, we further introduce two indicators called time diversity (TD) and geographical surprise (GS) to quantize the data anomaly strength in these two cases
We have presented our design of an innovative visual analysis system, AirInsight, to address this problem
Summary
With the rapid development of the social economy and the improvement in public life conditions, urban air pollution has become a hot topic and has attracted progressively more attention [1]. An increasing number of works have been devoted to the analysis of air quality data, but most of them have been limited to analyzing the patterns of only one major pollutant in a specific city or monitoring station because of the complexity, diversity and large volumes of data [2,3]. AirVis [4] is a web-based visual analytic system that supports a collaborative analysis of spatiotemporal and multivariate features It is implemented using only eight air quality monitoring stations in Beijing and is incapable of managing big data. It is imperative to establish a comprehensive visual analysis platform that can analyze the regular patterns of air quality and potential anomalies Such a technique helps the departments involved in environmental protection formulate effective policies to improve air quality; it even enables non-professional users to understand the patterns of air pollution
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