Learning on Health Fairness and Environmental Justice via Interactive Visualization

TL;DR

This paper presents an interactive visualization tool combined with machine learning consensus analysis to explore how atmospheric and socioeconomic factors influence COVID-19 severity, integrating satellite data and multi-dimensional analysis for health and environmental justice insights.

Contribution

It introduces a novel interactive visualization interface with a consensus machine learning model that incorporates satellite, environmental, and socioeconomic data for health and climate justice research.

Findings

Effective exploration of COVID-19 severity factors across multiple dimensions.

Demonstrated the utility of satellite data in climate justice analysis.

Case studies validate the approach's scientific value.

Abstract

This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machine learning model to include knowledge from county-level records and investigate the complex interrelationships between global infectious disease, environment, and social justice. Additionally, we make use of unique NASA satellite-based observations which are not broadly used in the context of climate justice applications. Our current interactive interface focus on three US states (California, Pennsylvania, and Texas) to demonstrate its scientific value and presented three case studies to make qualitative evaluations.