  Analyzing air pollution data is challenging as there are various analysis
focuses from different aspects: feature (what), space (where), and time (when).
As in most geospatial analysis problems, besides high-dimensional features, the
temporal and spatial dependencies of air pollution induce the complexity of
performing analysis. Machine learning methods, such as dimensionality
reduction, can extract and summarize important information of the data to lift
the burden of understanding such a complicated environment. In this paper, we
present a methodology that utilizes multiple machine learning methods to
uniformly explore these aspects. With this methodology, we develop a visual
analytic system that supports a flexible analysis workflow, allowing domain
experts to freely explore different aspects based on their analysis needs. We
demonstrate the capability of our system and analysis workflow supporting a
variety of analysis tasks with multiple use cases.
