Recent Advances in CyberGIS-Viz for Democratizing Access to Scalable Geovisualization

Keywords: CyberGIS, Geovisualization, Coordinated Multiple Views, Visualization, Data Science
Session Type: Virtual Paper Abstract
Day: Sunday
Session Start / End Time: 2/27/2022 05:20 PM (Eastern Time (US & Canada)) - 2/27/2022 06:40 PM (Eastern Time (US & Canada))
Room: Virtual 63

Abstract

Despite the proliferation of Web-based online data visualization tools in recent years, there has been a lack of JavaScript-based open-source tools supporting coordinated and multiple views (CMV). CMV is a dynamic visual interface where multiple data representations such as maps and charts are interactively linked to each other- i.e. changes in one data representation are broadcasted immediately to other available data representation through the user’s operations such as cross-filtering, brushing, highlighting, and selection. CMV is the core function of geovisualization since the process allows users to explore the data from various perspectives and enable thinking in complex decision-making tasks. Various GIS software packages traditionally have CMV, which are written in Java mostly for offline desktop environments. However, in recent decades, open-source Web-based and distributed GIS have become prevalent. There exists a gap where traditionally developed CMV cannot be integrated or reproduced in the state-of-the-art mainstream visualization environments based on Javascript-based open-source libraries such as D3, and Plotly.js. To address this, we have developed CyberGIS-Viz which is an open-source and JavaScript-based software tool for interactive geospatial visualization supporting CMV. CyberGIS-Viz integrates cutting-edge cyberGIS and online visualization capabilities into a suite of software modules for visualization and visual analytical approaches for knowledge discovery based on geospatial data. Key features of the current CyberGIS-Viz implementation include (1) comparative visualization of spatiotemporal patterns through choropleth maps; (2) dynamic cartographic mapping linked with charts to explore high-dimensional data; (3) reproducible visual analytics through integration with CyberGIS-Jupyter; and (4) multi-language support including both Python and Javascript.