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<p>This paper introduces an interactive web-based application for visualizing spatiotemporal public health data (STPH-Viz) at the county level in the United States, with a focus on COVID-19 cases. Developed using Streamlit, an open-source Python framework, STPH-Viz leverages various visualization techniques, including choropleth maps, bubble maps, bar charts, line charts, heatmaps, and scatter plots, to offer insights into both spatial and temporal patterns of the virus's spread. By providing these visualizations, the tool aids in understanding how the pandemic has unfolded across different regions and timeframes. This paper discusses the advantages of using Streamlit for developing spatiotemporal public health visualizations, compares map-based versus tabular data representations, and outlines future recommendations for enhancing the tool's capabilities.</p>

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STPH-Viz: a spatiotemporal visualization tool for county-level public health analysis of COVID-19

  • 期刊论文

  • 发表日期：

    2025年2月26日

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  • Mohammad Shaito,
  • Leonidas Fegaras,
  • David Levine

摘要

This paper introduces an interactive web-based application for visualizing spatiotemporal public health data (STPH-Viz) at the county level in the United States, with a focus on COVID-19 cases. Developed using Streamlit, an open-source Python framework, STPH-Viz leverages various visualization techniques, including choropleth maps, bubble maps, bar charts, line charts, heatmaps, and scatter plots, to offer insights into both spatial and temporal patterns of the virus's spread. By providing these visualizations, the tool aids in understanding how the pandemic has unfolded across different regions and timeframes. This paper discusses the advantages of using Streamlit for developing spatiotemporal public health visualizations, compares map-based versus tabular data representations, and outlines future recommendations for enhancing the tool's capabilities.