| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| geospatialanalytics:gsa:start [18/11/2025 alle 15:37 (5 settimane fa)] – [Calendar] Luca Pappalardo | geospatialanalytics:gsa:start [02/12/2025 alle 09:57 (4 settimane fa)] (versione attuale) – [Calendar] Mirco Nanni |
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| |17. |17.11 16:00-18:00 Fib L1| Mobility Models II | **[slides]** {{ :geospatialanalytics:gsa:lesson_07_-_mobility_models.pdf | Collective Mobility models}} | **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]] (Section 4.2); **[paper]** [[https://www.nature.com/articles/nature10856|A universal model for mobility and migration patterns]]; **[paper]** [[https://arxiv.org/abs/1506.04889|Systematic comparison of trip distribution laws and models]]: **[paper]** [[https://www.nature.com/articles/s41467-021-26752-4|A Deep Gravity model for mobility flows generation]] | L. Pappalardo | | |17. |17.11 16:00-18:00 Fib L1| Mobility Models II | **[slides]** {{ :geospatialanalytics:gsa:lesson_07_-_mobility_models.pdf | Collective Mobility models}} | **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]] (Section 4.2); **[paper]** [[https://www.nature.com/articles/nature10856|A universal model for mobility and migration patterns]]; **[paper]** [[https://arxiv.org/abs/1506.04889|Systematic comparison of trip distribution laws and models]]: **[paper]** [[https://www.nature.com/articles/s41467-021-26752-4|A Deep Gravity model for mobility flows generation]] | L. Pappalardo | |
| |18. |18.11 16:00-18:00 Fib M1| Practical session on Collective Mobility Laws and Models | | | L. Pappalardo, G. Mauro | | |18. |18.11 16:00-18:00 Fib M1| Practical session on Collective Mobility Laws and Models | | | L. Pappalardo, G. Mauro | |
| | |19. |24.11 16:00-18:00 Fib L1| Mobility Pattern Mining I | **[slides]** {{ :geospatialanalytics:gsa:08_-_mobility_patterns_25_26.pdf |Mobility Patterns}} | **[paper]** [[https://arxiv.org/abs/2303.05012v2|Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative Study]], Sections 1-2-3 | M. Nanni | |
| | |20. |25.11 16:00-18:00 Fib M1| Mobility Pattern Mining II | | **[paper]** [[https://dl.acm.org/doi/10.1145/1183471.1183479|Computing longest duration flocks in trajectory data]], Section 1; **[paper]** [[https://arxiv.org/abs/1002.0963v1|Discovery of Convoys in Trajectory Databases]], Section 3; **[paper]** [[https://doi.org/10.1007/11535331_21|On Discovering Moving Clusters in Spatio-temporal Data]], Sections 1, 2, 4.1; **[paper]** [[https://dl.acm.org/doi/10.1145/1281192.1281230|Trajectory pattern mining]], Section 3; **[paper]** [[https://arxiv.org/abs/2003.0135|DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis]], Section II | M. Nanni | |
| | |21. |01.12 16:00-18:00 Fib L1| Next-location prediction | **[slides]** {{ :geospatialanalytics:gsa:09_-_location_prediction_25_26.pdf |Next Location Prediction}} | **[paper]** [[https://ieeexplore.ieee.org/document/8570749|Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications]], Sections I-IV; **[book chapter]** [[https://web.stanford.edu/~jurafsky/slp3/A.pdf|Speech and Language Processing]], Chapter A - Hidden Markov Models; **[paper]** {{:geospatialanalytics:gsa:mcleod_1996_do_fielders_know_where_to_go_to_catch_the_ball_or_only_how_to_get_there.pdf |Do Fielders Know Where to Go to Catch the Ball...?}} | Nanni | |
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| |19. |24.11 16:00-18:00 Fib L1| Mobility Pattern Mining I | | | M. Nanni | | |22. |02.12 16:00-18:00 Fib M1| Practical session on Mobility Pattern Mining and Next-location Prediction | | **[library doc]** [[https://hmmlearn.readthedocs.io/en/latest/|HMMlearn library]] | M. Nanni, G. Mauro | |
| |20. |25.11 16:00-18:00 Fib M1| Mobility Pattern Mining II | | | M. Nanni | | |
| |21. |01.12 16:00-18:00 Fib L1| Next-location prediction | | | M. Nanni | | |
| |22. |02.12 16:00-18:00 Fib M1| Practical session on Mobility Pattern Mining and Next-location Prediction | | | M. Nanni, G. Mauro | | |
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