Urban road networks are reflections of several environmental, social, and economic factors evolving with different speeds at different times. Sometimes these factors are linearly related and traceable, in other cases street patterns remain formal manifestations. This study represents an attempt of analyzing the geometric properties of street networks, through graph machine learning. Through unsupervised and supervised models, two ways of encoding them are tested: representing streets as graphs with the primal and dual approach. It concludes with some advantages and disadvantages of each encoding method and further opens a discussion on the prospects of using graph machine learning methods when analyzing or generating street patterns.
The publicly available road network data from OpenStreetMap (https://www.openstreetmap.org/) via the OSMnx Python package (https://github.com/gboeing/osmnx) was used for retrieving the street network samples.
Analyzing street networks through graph machine learning is a project of IAAC, Institute for Advanced Architecture of Catalonia developed in the Master in Advanced Computation for Architecture & Design 2021/22 by student: Erida Bendo and faculty: David Andres Leon.