  Mexico City, the largest city in Mexico, is also one of the largest cities in
the world. It has over 9 million inhabitants and concentrates the vast majority
of government and business centers. In this work we describe algorithms that
use anonymized location data from mobile devices to construct Mexico City's
contact and mobility networks aiming to help the analysis of the city's
complexity by understanding movement and physical interaction patterns between
its inhabitants. We show the effectiveness and usefulness of our approach by
building networks with data collected in February 2020 and performing a general
descriptive analysis on them. We found that contact networks in Mexico City are
very sparse, characterized by a largest connected component, and with a
heavy-tailed degree distribution. On the other hand, we observed that paths
conformed by the highest-degrree nodes of mobility networks resemble Mexico
City's street network; moreover, we found interesting qualitative differences
in the degree distribution of these networks between weekends and weekdays. We
present these results along with the release of contact and mobility networks.
