10.5061/DRYAD.9P4R16M
Dannemann, Teodoro
University Austral de Chile
Sotomayor-Gómez, Boris
University Austral de Chile
Samaniego, Horacio
University Austral de Chile
Data from: The time geography of segregation during working hours
Dryad
dataset
2018
segregation
urban computing
time geography
present
community detection
2018-09-21T14:51:04Z
2018-09-21T14:51:04Z
en
https://doi.org/10.1098/rsos.180749
32635484 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Understanding segregation is essential to develop planning tools for
building more inclusive cities. Theoretically, segregation at the work
place has been described as lower compared to residential segregation
given the importance of skill complementarity among other productive
factors shaping the economies of cities. This paper tackles segregation
during working hours from a dynamical perspective by focusing on the
movement of urbanites across the city. In contrast to measuring
residential patterns of segregation, we used mobile phone data to infer
home-work trajectory networks and apply a community detection algorithm to
the example city of Santiago, Chile. We then describe qualitatively and
quantitatively outlined communities, in terms of their socio economic
composition. We then evaluate segregation for each of these communities as
the probability that a person from a specific community will interact with
a co-worker from the same community. Finally, we compare these results
with simulations where a new work location is set for each real user,
following the empirical probability distributions of home-work distances
and angles of direction for each community. Methodologically, this study
shows that segregation during working hours for Santiago is unexpectedly
high for most of the city with the exception of its central and business
district. In fact, the only community that is not statistically segregated
corresponds to the downtown area of Santiago, described as a zone of
encounter and integration across the city.
HW Commuting in Santiago Chile from cellphone CDR dataColumn are as
following. numa_id: user id; tower_w: cellphone tower at work place;
tower_h cellphone tower at home place ;X_w: work location easting ;Y_w:
work location northing;X_h: home location easting;Y_h: home location
northing;Distance: commuting distance ;angle angle of commuting w/ respect
to east direction.HW_20days_dataset.csvCommunities by dayCommunities
detected from HW commuting behaviour in Santiago Chile.
Santiago
Chile