Identification of city motifs: a method based on modularity and similarity between hierarchical features of urban networks

Several natural and theoretical networks can be broken down into smaller portions, henceforth called neighborhoods. The more frequent of these can then be understood as motifs of the network, being therefore important for better characterizing and understanding of its overall structure. Several developments in network science have relied on this interesting concept, with ample applications in areas including systems biology, computational neuroscience, economy and ecology. The present work aims at reporting a methodology capable of automatically identifying motifs respective to streets networks, i.e. graphs obtained from city plans by considering street junctions and terminations as nodes while the links are defined by the streets. Interesting results are described, including the identification of nine characteristic motifs, which have been obtained by three important considerations: (i) adoption of five hierarchical measurements to locally characterize the neighborhoods of nodes in the streets networks; (ii) adoption of an effective coincidence similarity methodology for translating datasets into networks; and (iii) definition of the motifs in statistical terms by using community finding methodology. The nine identified motifs are characterized and discussed from several perspectives, including their mutual similarity, visualization, histograms of measurements, and geographical adjacency in the original cities. Also presented is the analysis of the effect of the adopted features on the obtained networks as well as a simple supervised learning method capable of assigning reference motifs to cities.

Automated summary

This study presents a methodology for automatically identifying motifs in street networks, obtained from city plans. The identified motifs are characterized and discussed from various perspectives, and the impact of the adopted features on the networks is analyzed. Additionally, a simple supervised learning method is introduced for assigning reference motifs to cities.