Abstract
Spatial data on human activity, including mobile phone data, has the potential to provide patterns of how the citizens use the urban space. The availability of this data boosted research on city dynamics and human behavior. In this context, we address the question: Can we generate a sufficiently accurate picture of the main places of individuals from highly noisy and sparse data generated by mobile phone operators?
This paper studies different kinds of anonymized mobile phone data and proposes a model, that uses a density-based clustering algorithm to obtain the geographic profile of customers, by identifying their most visited locations at the antenna level. The individual routine, such as sleeping period and work hours, is dynamically identified according to slots of minimums of activity in the network. Then, based on those slots, areas of Home, Second Home, and Work are inferred. Ground truth is used to validate and evaluate the model.
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This work is funded by the FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CisUC R &D Unit - UIDB/00326/2020 or project code UIDP/00326/2020.
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Rodrigues, C., Veloso, M., Alves, A., Ferreira, G., Bento, C. (2022). Generating the Users Geographic Map Using Mobile Phone Data. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_25
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