A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data

TL;DR

This paper introduces a machine-learning-assisted visual analysis workflow designed to explore complex air pollution data across feature, space, and time dimensions, aiding domain experts in comprehensive environmental analysis.

Contribution

It presents a novel methodology combining multiple machine learning techniques with a visual analytic system for flexible, multi-aspect air pollution data exploration.

Findings

System effectively supports diverse analysis tasks.

Enables flexible exploration of spatial, temporal, and feature data.

Demonstrated through multiple real-world use cases.

Abstract

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.