Using network metrics to explore the community structure that underlies movement patterns
This work addresses urban planning and segregation issues in Santiago de Chile, but it is incremental as it applies existing methods to a new dataset.
This study tackled the problem of exploring community structure in Santiago de Chile by analyzing resident movement patterns, using network metrics to identify sub-cities and factors driving spatial organization, with results showing that combining community detection algorithms with segregation tools provides new insights into segregation during working hours.
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.