  Several natural and theoretical networks can be broken down into smaller
portions, or subgraphs corresponding to neighborhoods. The more frequent of
these neighborhoods can then be understood as motifs of the network, being
therefore important for better characterizing and understanding of the 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 an unsupervised methodology capable of identifying motifs
respective to streets networks, the latter corresponding to graphs obtained
from city plans by considering street junctions and terminations as nodes while
the links are defined by the streets. Remarkable results are described,
including the identification of nine stable and informative motifs, which have
been allowed by three critically important factors: (i) adoption of five
hierarchical measurements to locally characterize the neighborhoods of nodes in
the streets networks; (ii) adoption of an effective coincidence 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 perspective, 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.
