Topology Density Map for Urban Data Visualization and Analysis

TL;DR

This paper introduces the Topology Density Map, a novel urban data visualization method that models scalar fields along road networks, providing more accurate and intuitive density maps for urban analysis compared to traditional Euclidean-based methods.

Contribution

The paper presents a new approach for urban density mapping that incorporates road network topology and traffic constraints, extending scalar fields from DAGs to 2D space with Voronoi diagrams.

Findings

Provides accurate urban density visualization

Enhances decision-making with intuitive maps

Validated through case studies and expert interviews

Abstract

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.