Discovering Important Nodes Through Graph Entropy Encoded in Urban Space Syntax

TL;DR

This paper introduces a method using graph entropy to identify influential nodes in urban spatial networks, revealing insights into city connectivity and global structure through entropy participation ratios.

Contribution

It proposes a novel approach combining local and global entropy measures to analyze urban networks, enhancing understanding of city connectivity and intelligibility.

Findings

Connectivity entropy increases with city size

Centrality entropy decreases as cities grow

Local and global properties of nodes are positively correlated

Abstract

Potentially influential spaces in the spatial networks of cities can be detected by means of the entropy participation ratios. Local (connectivity) and global (centrality) entropies are considered. While the connectivity entropy has a tendency to increase with the city size, the centrality entropy is decreasing that reflects the global connectedness of cities. In urban networks, the local and global properties of nodes are positively correlated that indicates the intelligibility of cities. Correlations between entropy participation ratios can be used in purpose of intelligibility measurements and city networks comparisons.