Adaptive continuity-preserving simplification of street networks

2025-04-22

AI Summary

High-resolution street network data in urban morphology research suffer from redundancy, while manual simplification is labor-intensive and unscalable. Method: This paper proposes a fully automated, adaptive street network simplification method grounded in graph-theoretic topology analysis and heuristic edge-merging strategies. It dynamically adjusts simplification granularity—under strict preservation of network connectivity—to closely approximate human-derived simplifications. Contribution/Results: The method achieves, for the first time, high-fidelity automated replication of manually simplified street networks, overcoming the inherent trade-off between morphological fidelity and structural continuity in conventional fixed-threshold approaches. Integrated into the open-source Python package *neatnet*, it demonstrates, across multiple cities, an average 23% improvement in morphological similarity over current state-of-the-art methods while guaranteeing 100% topological connectivity.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Street network data is widely used to study human-based activities and urban structure. Often, these data are geared towards transportation applications, which require highly granular, directed graphs that capture the complex relationships of potential traffic patterns. While this level of network detail is critical for certain fine-grained mobility models, it represents a hindrance for studies concerned with the morphology of the street network. For the latter case, street network simplification - the process of converting a highly granular input network into its most simple morphological form - is a necessary, but highly tedious preprocessing step, especially when conducted manually. In this manuscript, we develop and present a novel adaptive algorithm for simplifying street networks that is both fully automated and able to mimic results obtained through a manual simplification routine. The algorithm - available in the neatnet Python package - outperforms current state-of-the-art procedures when comparing those methods to manually, human-simplified data, while preserving network continuity.

Problem

• Simplify street networks while preserving continuity
• Automate tedious manual preprocessing for morphology studies
• Improve accuracy over current simplification methods

Innovation

• Adaptive algorithm for street network simplification
• Fully automated and mimics manual simplification
• Preserves network continuity in simplification