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

arXiv: https://arxiv.org/abs/2202.05413 · HTML: https://mlsanity.com/paper/2202.05413/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning](https://mlsanity.com/paper/1905.03911/) (2019)
- [A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction](https://mlsanity.com/paper/2008.01645/) (2020)
- [Multivariate Functional Data Visualization and Outlier Detection](https://mlsanity.com/paper/1703.06419/) (2017)
- [AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data](https://mlsanity.com/paper/2103.12910/) (2021)

## Built on by (most cited first, 1 of 1)

- [Stop Misusing t-SNE and UMAP for Visual Analytics](https://mlsanity.com/paper/2506.08725/) (2025)

## Abstract (arXiv)

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.