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This work aims to explore the community structure of Santiago de Chile byanalyzing the movement patterns of its residents. We use a dataset containingthe approximate locations of home and work places for a subset of anonymizedresidents to construct a network that represents the movement patterns withinthe city. Through the analysis of this network, we aim to identify thecommunities or sub-cities that exist within Santiago de Chile and gain insightsinto the factors that drive the spatial organization of the city. We employmodularity optimization algorithms and clustering techniques to identify thecommunities within the network. Our results present that the novelty ofcombining community detection algorithms with segregation tools provides newinsights to further the understanding of the complex geography of segregationduring working hours.

Using network metrics to explore the community structure that underlies movement patterns