  The spatial homogeneity of an urban road network (URN) measures whether each
distinct component is analogous to the whole network and can serve as a
quantitative manner bridging network structure and dynamics. However, given the
complexity of cities, it is challenging to quantify spatial homogeneity simply
based on conventional network statistics. In this work, we use Graph Neural
Networks to model the 11,790 URN samples across 30 cities worldwide and use its
predictability to define the spatial homogeneity. The proposed measurement can
be viewed as a non-linear integration of multiple geometric properties, such as
degree, betweenness, road network type, and a strong indicator of mixed
socio-economic events, such as GDP and population growth. City clusters derived
from transferring spatial homogeneity can be interpreted well by continental
urbanization histories. We expect this novel metric supports various subsequent
tasks in transportation, urban planning, and geography.
