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It is often necessary to introduce the main characteristics of populationmobility dynamics to model critical social phenomena such as the economy,violence, transmission of information, or infectious diseases. In this work, wefocus on modeling and inferring urban population mobility using the geospatialdata of its inhabitants. The objective is to estimate mobility and timesinhabitants spend in the areas of interest, such as zip codes and censusgeographical areas. The proposed method uses the Brownian bridge model foranimal movement in ecology. We illustrate its possible applications usingmobile phone GPS data in 2020 from the city of Hermosillo, Sonora, in Mexico.We incorporate the estimated residence-mobility matrix into a multi-patchcompartmental SEIR model to assess the effect of mobility changes due togovernmental interventions

Use of mobile phone sensing data to estimate residence and mobility  times in urban patches during the COVID-19 epidemic: The case of the 2020  outbreak in Hermosillo, Mexico