Dash Sylvereye: A WebGL-powered Library for Dashboard-driven Visualization of Large Street Networks

Abstract

State-of-the-art open network visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. The present paper presents Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps to fill this gap. Dash Sylvereye can render large street graphs in commodity computers by exploiting WebGL for GPU acceleration. Dash Sylvereye also provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by Dash. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer in terms of animation CPU time and frames per second. To further illustrate the features of Dash Sylvereye, we also describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation.