Manuel Mendoza

ML-proxkNN: spatially aware multi-label classification using mobile phone data

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Authors: Manuel Mendoza-Hurtado, Juan A. Romero-del-Castillo, Nicolás García-Pedrajas and Domingo Ortiz-Boyer Status: Manuscript submitted for publication and currently under review. ML-proxkNN is a new multi-label classification method designed to identify urban and regional functions from aggregated mobile phone activity. The method extends ML-localkNN, which assigns a different number of neighbours to each part of the feature space. … Read more

Milan mobility project

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Project overview The Milan Mobility Project investigates how anonymised and aggregated mobile-phone network activity can be used to identify population and functional patterns across an urban area. The project uses the Telecom Italia Big Data Challenge dataset for Milan, which contains aggregated Call Detail Record activity for incoming and outgoing calls, incoming and outgoing SMS … Read more

MAPLID: multi-label identification of urban functions using mobile phone data

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Authors: Manuel Mendoza-Hurtado, Juan A. Romero-del-Castillo and Domingo Ortiz-Boyer Overview Mobile-phone network data provide valuable information about the temporal activity and functional structure of urban and regional areas. However, conventional place-identification and clustering methods usually assign a single category—such as residential or work—to each location. This representation oversimplifies mixed-use urban environments, where several functions may coexist … Read more

Location tracking project

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Android application for location tracking and storage in MongoDB Atlas. For this project, an Android app was developed as a persistent service to acquire the user’s location and then store it in a non-relational database: MongoDB. MongoDB Atlas is used along with MongoDB Realm to synchronize the local database generated during the application use with … Read more

SAMPLID: supervised identification of meaningful places using mobile phone data

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Authors: Manuel Mendoza-Hurtado, Juan A. Romero-del-Castillo and Domingo Ortiz-Boyer Abstract Mobile-phone network data provide valuable information about the spatial and temporal distribution of population activity. These data can support the identification of meaningful urban areas, such as residential and employment zones, but most previous studies have relied on unsupervised clustering methods. This study introduces SAMPLID—Supervised Approach for … Read more