We are thrilled to announce the publication of our latest research paper, “Dash Sylvereye: A Python Library for Dashboard-Driven Visualization of Large Street Networks,” in the open-access journal IEEE Access (2022 IF 3.9).

Bridging the Visualization Gap

In the realm of urban planning and traffic management, visualizing street networks is crucial for analysis and decision-making. However, the vastness and complexity of these networks pose significant challenges for existing graph visualization tools. Advanced applications such as Gephi, KeyLines, and Cytoscape, while powerful, fall short when tasked with displaying thousands of roads, especially when there’s a need for polylines, navigable maps, GPU-accelerated rendering, interactivity, and multivariate data representation—all simultaneously.

Introducing Dash Sylvereye

In response to this challenge, we have developed Dash Sylvereye, a tailor-made Python library for producing interactive visualizations of primal street networks. Leveraging the Dash framework, Dash Sylvereye facilitates the creation of comprehensive web dashboards that combine temporal and multivariate street data with a wide range of plotting and UI components.

Core Features

• GPU-Accelerated Rendering: Dash Sylvereye uses WebGL to rapidly and efficiently redraw road networks.
• OpenStreetMap Integration: The library includes functions for easily importing street topologies, allowing users to incorporate detailed OpenStreetMap data into their visualizations.
• Performance: Our library exhibits exceptional performance, achieving near 60 FPS panning speeds and CPU times below 20 ms, even for networks with thousands of edges.

Performance Benchmarks

Through rigorous testing, Dash Sylvereye has proven to be competitive with tools like Kepler.gl and city-roads. Our performance assessments demonstrate the library’s ability to handle large street networks on mid-range budget systems.

Real-World Application

To illustrate Dash Sylvereye’s practicality, our paper includes a case study on the library’s application in analyzing a SUMO vehicle traffic simulation. This not only shows the library’s raw performance but also its adaptability in real-world scenarios.

Open Access

Consistent with our commitment to knowledge dissemination, our paper is freely available to all. We believe that open sharing of our findings aligns with the collaborative and progressive spirit that drives the scientific community.

Read our paper on IEEEXplore.

Access the source code of Dash Sylvereye on GitHub and PyPi.

We welcome feedback, discussions, and potential collaborations as we continue to explore and expand the capabilities of Dash Sylvereye.

Engage with Us

Your insights and experiences are invaluable. Whether you’re a researcher, urban planner, or enthusiast, we value your input and encourage you to test Dash Sylvereye in your projects.

For more information, discussions, or inquiries, please feel free to contact me.