AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data Dongyu Liu Kalyan Veeramachaneni Alexander Geiger Victor O.K. Li Huamin Qu CSE LIDS LIDS EEE CSE HKUST MIT MIT HKU HKUST Hong Kong, China Cambridge, MA, USA Cambridge, MA, USA Hong Kong, China Hong Kong, China
[email protected] [email protected] [email protected] [email protected] [email protected] Abstract—Anomaly detection plays a key role in air quality of unexpected air pollutant concentrations at a certain time analysis by enhancing situational awareness and alerting users period and place compared with previous observations from to potential hazards. However, existing anomaly detection ap- that location [1]. Based on experience, air quality events can proaches for air quality analysis have their own limitations regarding parameter selection (e.g., need for extensive domain result from unusual weather conditions or some local sources knowledge), computational expense, general applicability (e.g., such as trash burning. require labeled data), interpretability, and the efficiency of anal- Currently, anomaly detection for air quality data primarily ysis. Furthermore, the poor quality of collected air quality data utilizes statistical and threshold-based [2], density-based [3], (inconsistently formatted and sometimes missing) also increases learning-based (need labeled data) [4], [5], and visualization- the difficulty of analysis substantially. In this paper, we system- atically formulate design requirements for a system that can based approaches [6], [7]. Each of these approaches has solve these limitations and then propose AQEyes, an integrated drawbacks. Statistical and threshold-based methods require visual analytics system for efficiently monitoring, detecting, and extensive human knowledge to specify model parameters and examining anomalies in air quality data.