Topological Graph Simplification Solutions to the Street Intersection Miscount Problem

TL;DR

This paper introduces algorithms for simplifying urban street network graphs to improve the accuracy of intersection counts and densities, addressing overcounting issues in transport geography analysis.

Contribution

It presents novel algorithms for automatic graph simplification that enhance accuracy and efficiency in street network analysis.

Findings

Overestimation of intersection counts by 14% without simplification

Algorithms reduce memory and runtime for graph analytics

Bias in intersection counts varies significantly across regions

Abstract

Street intersection counts and densities are ubiquitous measures in transport geography and planning. However, typical street network data and typical street network analysis tools can substantially overcount them. This article explains the three main reasons why this happens and presents solutions to each. It contributes algorithms to automatically simplify spatial graphs of urban street networks -- via edge simplification and node consolidation -- resulting in faster parsimonious models and more accurate network measures like intersection counts and densities, street segment lengths, and node degrees. These algorithms' information compression improves downstream graph analytics' memory and runtime efficiency, boosting analytical tractability without loss of model fidelity. Finally, this article validates these algorithms and empirically assesses intersection count biases worldwide to demonstrate the problem's widespread prevalence. Without consolidation, traditional methods would overestimate the median urban area intersection count by 14\%. However, this bias varies drastically across regions, underscoring these algorithms' importance for consistent comparative empirical analyses.