# Enhancing the communication of radiation exposure data for radiological workers in Korea using data visualization techniques

## TL;DR

This paper explores how data visualization can improve the understanding of radiation exposure data for workers in Korea.

## Contribution

The study introduces novel visualization techniques to make radiation exposure data more accessible and actionable for non-experts.

## Key findings

- Visualization methods like radar charts and box plots effectively highlight disparities in radiation exposure across professions and regions.
- The study shows that visualizing data improves comprehension and decision-making for stakeholders.
- Limitations include reliance on public datasets and the need for primary data collection.

## Abstract

Effective communication of radiation exposure data is essential for improving safety management practices for radiological workers. However, traditional tabular formats used in reporting radiation exposure data often fail to convey critical patterns and trends, making it difficult for non-experts to interpret and act on the information. This study evaluates the application of data visualization techniques, including radar charts, box plots, sparklines, and Chernoff faces, to enhance the accessibility and comprehension of radiation exposure data. Using datasets from the “2022 Annual Report on Individual Exposure Doses of Radiological Workers” published by the KDCA, this study demonstrates how visualization can effectively highlight disparities across professions, demographic groups, and geographic regions. The findings underscore the significant potential of visualization methods in simplifying complex datasets, enabling stakeholders to make more informed decisions. Nonetheless, the study has limitations, including its reliance on pre-existing public datasets and a lack of real-time or granular data. Future research should focus on collecting primary data to explore causal relationships in radiation exposure trends and on applying advanced statistical and machine learning techniques to uncover deeper insights. By integrating robust visualization methods, this study aims to bridge the gap between raw data and actionable knowledge, ultimately contributing to safer occupational environments for radiological workers.