  Barriers of urban mobility may contribute to the segregation of
neighborhoods. However, the empirical knowledge about this issue is limited.
Here, we present a methodological framework to assess the importance of
barriers to urban mobility along their hierarchy, over time and across
neighborhoods. Using GPS mobility data, we construct a network of blocks from
the sequence of individual visits. A community finding algorithm allows us to
evaluate the fit of the neighborhoods detected in this mobility network to
characteristic urban barriers by adjusting the resolution parameter of the
Louvain method to decrease the dominance of mobility hubs. Using the Symmetric
Area Difference index, that quantifies the overlap between the polygons
separated by urban barriers and the network communities, we find that the match
of detected neighborhoods and urban barriers improves as the resolution
parameter increases. However, at high values of the resolution parameter, this
fit gets better for lower rank administrative or road barriers than for their
higher rank pairs. Although the role of urban barriers in hindering mobility
became stronger over the COVID-19 restrictions, their fit with detected
neighborhoods deteriorated for most analyzed barriers. Exceptions are primary
roads, to which the fit of neighborhoods is stable over periods. By increasing
the resolution parameter, we find emergent communities of local mobility flows
that cross primary roads and a consequent optimal value of the resolution
parameter. The Barrier Crossing Ratio, the fraction of barrier crossings that
are network community crossings as well, demonstrates that the barriers' impact
on mobility are significantly different across urban areas. These results
contribute to the ongoing discourse on urban segregation, emphasizing the
importance of barriers to urban mobility in shaping neighborhood interactions
and mixing.
