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State-of-the-art open network visualization tools like Gephi, KeyLines, andCytoscape are not suitable for studying street networks with thousands of roadssince they do not support simultaneously polylines for edges, navigable maps,GPU-accelerated rendering, interactivity, and the means for visualizingmultivariate data. To fill this gap, the present paper presents Dash Sylvereye:a new Python library to produce interactive visualizations of primal streetnetworks on top of tiled web maps. Thanks to its integration with the Dashframework, Dash Sylvereye can be used to develop web dashboards around temporaland multivariate street data by coordinating the various elements of a DashSylvereye visualization with other plotting and UI components provided by theDash framework. Additionally, Dash Sylvereye provides convenient functions toeasily import OpenStreetMap street topologies obtained with the OSMnx library.Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering whenredrawing the road network. We conduct experiments to assess the performance ofDash Sylvereye on a commodity computer when exploiting software acceleration interms of frames per second, CPU time, and frame duration. We show that DashSylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below20 ms, for street networks with thousands of edges, and above 24 FPS, and CPUtimes below 40 ms, for networks with dozens of thousands of edges.Additionally, we conduct a performance comparison against two state-of-the-artstreet visualization tools. We found Dash Sylvereye to be competitive whencompared to the state-of-the-art visualization libraries Kepler.gl andcity-roads. Finally, we describe a web dashboard application that exploits DashSylvereye for the analysis of a SUMO vehicle traffic simulation.

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