| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| geospatialanalytics:gsa:start [19/11/2024 alle 15:36 (4 mesi fa)] – [Learning goals] Luca Pappalardo | geospatialanalytics:gsa:start [26/03/2025 alle 14:26 (5 giorni fa)] (versione attuale) – [News and communications] Mirco Nanni |
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| * **Giuliano Cornacchia**, PhD student, University of Pisa | * **Giuliano Cornacchia**, PhD student, University of Pisa |
| * **Giovanni Mauro**, PhD student, University of Pisa | * **Giovanni Mauro**, PhD student, University of Pisa |
| * **Daniele Gambetta**, PhD student, University of Pisa | |
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| ===== Hours and Rooms ===== | ===== Hours and Rooms ===== |
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| ====== News and communications ====== | ====== News and communications ====== |
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| | ** Exams sessions**: |
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| | * the first session of exams (i.e., first "appello") will be on __January 16th, 09:00, room C29 (aula Faedo)__ at [[https://hiis.isti.cnr.it/images/how_to_reach_room_c29.jpg | ISTI-CNR]]. Bring your identity card (or passport) for identification. |
| | * the second session of exams will be held on three days: __February 7 at 10:00__, __February 10 at 9:00__, __February 12 at 9:00__. Room: Aula C-29 "A. Faedo" ([[https://hiis.isti.cnr.it/images/how_to_reach_room_c29.jpg | ISTI-CNR]]). Bring your identity card (or passport) for identification. Interested students should have received the detailed calendar of exams. Contact the teachers in case of issues. |
| | * the third session of exams will be held on __April 1st at 10:00__ in Aula C-38 (office of prof. Nanni) at [[https://hiis.isti.cnr.it/images/how_to_reach_room_c29.jpg | ISTI-CNR]]. |
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| | **__Instruction for the exam__**: The exam will consist of a combination of general concept questions and short, specific questions. The questions may span the entire course content, testing your understanding of the key topics. Additionally, we will present a problem and ask you to work through the reasoning process for its solution. You will then be required to implement the solution by writing Python code "live", similar to the approach we used in the practical sessions during the course. During this part of the exam, you will be able to use resources such as Google and ChatGPT to assist with coding, reflecting the collaborative and problem-solving nature of the course. |
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| __No lesson__ on November 21st and 22nd | __No lesson__ on November 21st and 22nd |
| |9. | 24.10 14:00-16:00| Data Preprocessing (theory) | **[slides]** {{ :geospatialanalytics:gsa:05_-_preprocessing_-_light.pdf |Data Preprocessing}} | **[paper]** [[https://journals.sagepub.com/doi/pdf/10.1177/15501477211050729?download=true|Review and classification of trajectory summarisation algorithms: From compression to segmentation]]; **[paper]** [[http://www2.ipcku.kansai-u.ac.jp/~yasumuro/M_InfoMedia/paper/Douglas73.pdf|Algorithms for the reduction of the number of points required to represent a digitized line or its caricature (Douglas-Peucker)]]; **[paper]** [[https://www.researchgate.net/publication/314207447_A_Trajectory_Segmentation_Map-Matching_Approach_for_Large-Scale_High-Resolution_GPS_Data|A Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data]]; **[paper]** [[https://www.ismll.uni-hildesheim.de/lehre/semSpatial-10s/script/6.pdf|Hidden Markov Map Matching Through Noise and Sparseness]] | Cornacchia| | |9. | 24.10 14:00-16:00| Data Preprocessing (theory) | **[slides]** {{ :geospatialanalytics:gsa:05_-_preprocessing_-_light.pdf |Data Preprocessing}} | **[paper]** [[https://journals.sagepub.com/doi/pdf/10.1177/15501477211050729?download=true|Review and classification of trajectory summarisation algorithms: From compression to segmentation]]; **[paper]** [[http://www2.ipcku.kansai-u.ac.jp/~yasumuro/M_InfoMedia/paper/Douglas73.pdf|Algorithms for the reduction of the number of points required to represent a digitized line or its caricature (Douglas-Peucker)]]; **[paper]** [[https://www.researchgate.net/publication/314207447_A_Trajectory_Segmentation_Map-Matching_Approach_for_Large-Scale_High-Resolution_GPS_Data|A Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data]]; **[paper]** [[https://www.ismll.uni-hildesheim.de/lehre/semSpatial-10s/script/6.pdf|Hidden Markov Map Matching Through Noise and Sparseness]] | Cornacchia| |
| |10. | 25.10 14:00-16:00| Data Preprocessing (practice) | **[code]** [[https://github.com/jonpappalord/geospatial_analytics/blob/main/2024/4-%20Preprocessing/practice_preprocessing.ipynb | Exercise: implementing speed-based noise filtering]] | | Cornacchia| | |10. | 25.10 14:00-16:00| Data Preprocessing (practice) | **[code]** [[https://github.com/jonpappalord/geospatial_analytics/blob/main/2024/4-%20Preprocessing/practice_preprocessing.ipynb | Exercise: implementing speed-based noise filtering]] | | Cornacchia| |
| |12. | 07.11 14:00-16:00| Individual Mobility Patterns (theory) | **[slides]** {{ :geospatialanalytics:gsa:06_-_individual_models_1_compressed.pdf | Individual mobility patterns}} | **[paper]** [[ https://www.nature.com/articles/nature04292 | The scaling laws of human travel]]; **[paper]** [[ https://www.nature.com/articles/nature06958 | Understanding individual human mobility patterns]]; **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]], Sections 3.1 and 4; **[paper]** [[ https://www.nature.com/articles/ncomms9166 | Returners and Explorers dichotomy in Human Mobility]]; **[paper]** [[ https://barabasi.com/f/310.pdf | Limits of predictability in human mobility]]; **[paper]** [[ https://www.nature.com/articles/nphys1760 | Modelling the scaling properties of human mobility]]; | Pappalardo| | |11. | 07.11 14:00-16:00| Individual Mobility Patterns (theory) | **[slides]** {{ :geospatialanalytics:gsa:06_-_individual_models_1_compressed.pdf | Individual mobility patterns}} | **[paper]** [[ https://www.nature.com/articles/nature04292 | The scaling laws of human travel]]; **[paper]** [[ https://www.nature.com/articles/nature06958 | Understanding individual human mobility patterns]]; **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]], Sections 3.1 and 4; **[paper]** [[ https://www.nature.com/articles/ncomms9166 | Returners and Explorers dichotomy in Human Mobility]]; **[paper]** [[ https://barabasi.com/f/310.pdf | Limits of predictability in human mobility]]; **[paper]** [[ https://www.nature.com/articles/nphys1760 | Modelling the scaling properties of human mobility]]; | Pappalardo| |
| |13. | 08.11 14:00-16:00| Individual Mobility Patterns (practice) | {{ :geospatialanalytics:gsa:practice_individual_measures.zip | Practice session on individual measures}} | | Mauro| | |12. | 08.11 14:00-16:00| Individual Mobility Patterns (practice) | {{ :geospatialanalytics:gsa:practice_individual_measures.zip | Practice session on individual measures}} | | Mauro| |
| |14. | 14.11 14:00-16:00| Individual and Collective Mobility models (theory) | {{ :geospatialanalytics:gsa:07_-_mobility_models.pdf | Human Mobility Models}} | **[paper]** [[ https://arxiv.org/abs/1710.00004 |Modelling the scaling properties of human mobility]]; **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]]; **[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]] | Pappalardo| | |13. | 14.11 14:00-16:00| Individual and Collective Mobility models (theory) | {{ :geospatialanalytics:gsa:07_-_mobility_models.pdf | Human Mobility Models}} | **[paper]** [[ https://arxiv.org/abs/1710.00004 |Modelling the scaling properties of human mobility]]; **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]]; **[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]] | Pappalardo| |
| |15. | 15.11 14:00-16:00| Individual and Collective Mobility models (practice) | | | Mauro | | |14. | 15.11 14:00-16:00| Individual and Collective Mobility models (practice) | {{ :geospatialanalytics:gsa:gravity.zip | Practice session on the Gravity model}}| | Mauro | |
| |16. | 28.11 14:00:16:00| Mobility pattern mining | | | Nanni| | |15. | 28.11 14:00:16:00| Guest lecture | | | [[ https://www.riccardodiclemente.com/| Riccardo Di Clemente]]| |
| |17. | 29.11 14:00:16:00| Next-location prediction | | | Nanni| | | | 29.11 14:00:16:00| Cancelled for strike | | | | |
| |18. | 05.12 14:00:16:00| Guest lecture | | | Di Clemente | | |16. | 05.12 14:00:16:00| Mobility pattern mining | **[slides]** {{ :geospatialanalytics:gsa:08_-_mobility_patterns.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; **[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 | Nanni| |
| |19. | 06.12 14:00:16:00| Mobility pattern mining and next-location prediction (practice) | | | Cornacchia | | |17. | 06.12 14:00:16:00| Next-location prediction | **[slides]** {{ :geospatialanalytics:gsa:09_-_location_prediction.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...?}}; **[library doc]** [[https://hmmlearn.readthedocs.io/en/latest/|HMMlearn library]] | Nanni | |
| | |18. | ??.12 ??:??:?? | Mobility pattern mining and next-location prediction (practice) | | | Cornacchia | |
| ==== Previous Geospatial Analytics websites ==== | ==== Previous Geospatial Analytics websites ==== |
| * [[geospatialanalytics:gsa:gsa2023|]] | * [[geospatialanalytics:gsa:gsa2023|]] |
| * [[geospatialanalytics:gsa:gsa2022|]] | * [[geospatialanalytics:gsa:gsa2022|]] |
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