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. To fill this gap, the present paper presents Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. 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 the Dash framework. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries Kepler.gl and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation.

翻译：现有顶级开源网络可视化工具（如Gephi、KeyLines和Cytoscape）因无法同时支持边折线呈现、可导航地图、GPU加速渲染、交互功能及多变量数据可视化手段，故不适用于研究包含数千条道路的街道网络。为填补这一空白，本文提出Dash Sylvereye：一种基于平铺网络地图实现原始街道网络交互式可视化的新型Python库。通过与Dash框架的集成，Dash Sylvereye可协调可视化元素与Dash框架提供的其他绘图及UI组件，从而围绕时态与多变量街道数据开发网页仪表盘。此外，Dash Sylvereye提供了便捷函数，可轻松导入通过OSMnx库获取的OpenStreetMap街道拓扑结构。更重要的是，该库在重绘道路网络时利用WebGL实现GPU加速渲染。我们通过实验评估了Dash Sylvereye在普通计算机上启用软件加速时的性能表现，指标包括帧率、CPU用时及帧时长。实验表明，对于包含数千条边的街道网络，Dash Sylvereye可实现接近60 FPS的快速平移速度及低于20 ms的CPU用时；对于包含数万条边的网络，则可达到24 FPS以上帧率及低于40 ms的CPU用时。同时，我们将其与两款主流街道可视化工具进行性能对比，发现Dash Sylvereye在性能上与顶级可视化库Kepler.gl及city-roads具备竞争力。最后，我们展示了一个利用Dash Sylvereye分析SUMO车辆交通仿真的网页仪表盘应用案例。