AI summaryⓘ
The authors created a new way to describe the shape and structure of streets in a city by looking at small neighborhoods around each street and analyzing patterns using math tools called graph Laplacians. They introduce two new measurements, the Mesh Index and Connectivity Resilience Index, which capture details about street patterns that older methods missed. Tested on the city of Poznan, Poland, their method reliably distinguishes different street types and relates to the variety of activities happening nearby. The authors highlight that their approach explains what kinds of activities streets support, not economic factors like price. Their work adds a new, machine-learning-friendly tool for studying urban layouts.
space syntaxgraph Laplacianspectral fingerprintmesh indexconnectivity resilience indexego subgraphalgebraic connectivitystreet network topologycentralityurban morphology
Abstract
Decades of space-syntax research have established that the topology of the street network conditions movement, co-presence and urban activity. The standard vocabulary for this -- integration, choice and connectivity -- summarises each street's position as a scalar centrality, yet two streets with identical centrality can sit in radically different morphological fabric. We introduce a compact, comparable, machine-learning-ready encoding of that local fabric: the spectral fingerprint, a fixed-dimensional kernel-density representation of the graph-Laplacian eigenvalue distribution of each node's k-hop ego subgraph, computed on the COINS dual graph of the street network. From it we derive two interpretable scalar readouts: the Mesh Index (MI), a normalised spectral entropy, and the Connectivity Resilience Index (CRI), the algebraic connectivity (Fiedler value). Both are corrected for an ego-subgraph-size confound that dominates raw spectral statistics. Applied to the full street network of Poznan, Poland (1,908 continuity-based strokes), the descriptor is robust to its encoding hyperparameters (spectral resolution and kernel bandwidth; Spearman rho >= 0.98) while remaining scale-dependent in its neighbourhood radius. The size adjustment leaves the Mesh Index near-orthogonal to integration (r = 0.06), carrying information classical centrality does not. The fingerprint separates morphological tissue types without supervision, and the Mesh Index is associated with the functional diversity of street-adjacent activity at the neighbourhood scale (r ~ 0.19, on open OpenStreetMap data). We delineate the method's scope honestly: it characterises what kind of activity a street's position affords, not the price that activity commands. It offers an information-theoretic morphological descriptor that complements space syntax and is directly consumable by modern graph-learning pipelines.