  This work aims to explore the community structure of Santiago de Chile by
analyzing the movement patterns of its residents. We use a dataset containing
the approximate locations of home and work places for a subset of anonymized
residents to construct a network that represents the movement patterns within
the city. Through the analysis of this network, we aim to identify the
communities or sub-cities that exist within Santiago de Chile and gain insights
into the factors that drive the spatial organization of the city. We employ
modularity optimization algorithms and clustering techniques to identify the
communities within the network. Our results present that the novelty of
combining community detection algorithms with segregation tools provides new
insights to further the understanding of the complex geography of segregation
during working hours.
