CyberGIS-Vis for Democratizing Access to Scalable Spatiotemporal Geovisual Analytics: A Case Study of COVID-19.

Author(s): Su Yeon Han, Joonseok Kim, Jeon-Young Kang, Jinwoo Park, Chaeyeon Han, Alexander Michels and Shaowen Wang

The COVID-19 pandemic underscored the critical need for effective disease mapping tools, which have long been integral to public health efforts in tracking infectious diseases. In response to the World Health Organization’s (WHO) declaration of COVID-19 as a pandemic in March 2020, numerous technological solutions were rapidly developed to map cases, assess risk factors, and monitor human mobility, attracting significant attention from researchers and policymakers. Despite the widespread adoption of these tools, there was a notable lack of reusable and reproducible, open-source mapping software, which is essential for quick and effective responses to future pandemics. The pandemic also highlighted the growing importance of visualizing spatiotemporal dynamics in disease datasets. However, there remains a scarcity of open-source JavaScript-based tools that support Coordinated and Multiple Views (CMV) within geovisual analytics, a critical feature for enabling comprehensive and dynamic data analysis. Traditional GIS software packages that support CMV, such as GeoViz Toolkit and CommonGIS, are predominantly Java-based and designed for offline environments, creating a gap in their integration with modern, web-based visualization environments that rely on libraries like D3, Plotly.js, and Leaflet. To address these gaps, we developed an innovative open-source JavaScript-based software tool within the CyberGIS-Vis project, designed to support CMV for interactive geospatial visualization. This paper introduces two visualization modules within CyberGIS-Vis, demonstrating their application in visualizing spatiotemporal data through a COVID-19 case study. The CyberGIS-Vis tool integrates advanced cyberGIS and online visualization capabilities with robust analytical methods, empowering knowledge discovery from geospatial data. Features include dynamic choropleth mapping linked with charts, comparative visualization of spatiotemporal patterns, and integration with CyberGIS-Jupyter for reproducible visual analytics, offering multi-language support for Python and JavaScript.

Keywords: COVID-19, D3, Geovisual Analytics, Geovisualization, JavaScript, Leaflet, Mapping, Pandemic, Plotly, Spatial Data Science

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Su Yeon Han

Texas State University

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CyberGIS-Vis for Democratizing Access to Scalable Spatiotemporal Geovisual Analytics: A Case Study of COVID-19.

Su Yeon Han

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