  A common method for delineating urban and suburban boundaries is to identify
clusters of spatial units that are highly interconnected in a network of
commuting flows, each cluster signaling a cohesive economic submarket. It is
critical that the clustering methods employed for this task are principled and
free of unnecessary tunable parameters to avoid unwanted inductive biases while
remaining scalable for high resolution mobility networks. Here we
systematically assess the benefits and limitations of a wide array of
Stochastic Block Models (SBMs)$\unicode{x2014}$a family of principled,
nonparametric models for identifying clusters in networks$\unicode{x2014}$for
delineating urban spatial boundaries with commuting data. We find that the data
compression capability and relative performance of different SBM variants
heavily depends on the spatial extent of the commuting network, its aggregation
scale, and the method used for weighting network edges. We also construct a new
measure to assess the degree to which community detection algorithms find
spatially contiguous partitions, finding that traditional SBMs may produce
substantial spatial discontiguities that make them challenging to use in
general for urban boundary delineation. We propose a fast nonparametric
regionalization algorithm that can alleviate this issue, achieving data
compression close to that of unconstrained SBM models while ensuring spatial
contiguity, benefiting from a deterministic optimization procedure, and being
generalizable to a wide range of community detection objective functions.
