Topology Density Map for Urban Data Visualization and Analysis
Not on the map. Why?
Abstract (arXiv)
Density map is an effective visualization technique for depicting the scalar field distribution in 2D space. Conventional methods for constructing density maps are mainly based on Euclidean distance, limiting their applicability in urban analysis that shall consider road network and urban traffic. In this work, we propose a new method named Topology Density Map, targeting for accurate and intuitive density maps in the context of urban environment. Based on the various constraints of road connections and traffic conditions, the method first constructs a directed acyclic graph (DAG) that propagates nonlinear scalar fields along 1D road networks. Next, the method extends the scalar fields to a 2D space by identifying key intersecting points in the DAG, dividing the underlying territory into planar regions using a weighted Voronoi diagram, and calculating the scalar fields for every point. Two case studies demonstrate that the Topology Density Map supplies accurate information to users and provides an intuitive visualization for decision making. An interview with domain experts demonstrates the feasibility, usability, and effectiveness of our method.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 3; 2025: 2; 2026: 1.
8 papers.
- 2021Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Predictioncited by 4
- 2024CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent Systemcited by 3
- 2024TrafPS: A Shapley-based Visual Analytics Approach to Interpret Trafficcited by 1
- 2024An Image-based Typology for Visualizationcited by 1
- 2022TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapleycited by 0
- 2025RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolationcited by 0
- 2025Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Featurescited by 0
- 2026DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphscited by 0
- 2026DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs28 Jan 2026
- 2025Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Features7 Sep 2025
- 2025RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation2 Aug 2025
- 2024CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System23 May 2024
- 2024An Image-based Typology for Visualization7 Mar 2024
- 2024TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic7 Mar 2024
- 2022TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley11 Mar 2022
- 2021Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction8 Jan 2021