| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| geospatialanalytics:gsa:start [25/11/2024 alle 10:09 (13 mesi fa)] – [Calendar] Luca Pappalardo | geospatialanalytics:gsa:start [02/12/2025 alle 09:57 (11 giorni fa)] (versione attuale) – [Calendar] Mirco Nanni |
|---|
| ====== 783AA Geospatial Analytics A.A. 2024/25 ====== | ====== 783AA Geospatial Analytics A.A. 2025/26 ====== |
| |
| ===Instructors:=== | ===Instructors:=== |
| * **Luca Pappalardo** | * **Luca Pappalardo** |
| * [[luca.pappalardo@isti.cnr.it]] | * [[luca.pappalardo@isti.cnr.it]] |
| * KDD Laboratory, ISTI-CNR, Pisa | * KDD Laboratory, ISTI-CNR and Scuola Normale Superiore, Pisa |
| * [[http://www-kdd.isti.cnr.it]] | * [[http://www-kdd.isti.cnr.it]] |
| |
| * [[http://www-kdd.isti.cnr.it]] | * [[http://www-kdd.isti.cnr.it]] |
| |
| ===Tutors:=== | ===Teaching assistants:=== |
| * **Giuliano Cornacchia**, PhD student, University of Pisa | * **Giuliano Cornacchia**, Postdoc researcher, ISTI-CNR |
| * **Giovanni Mauro**, PhD student, University of Pisa | * **Giovanni Mauro**, Postdoc researcher, Scuola Normale Superiore |
| * **Daniele Gambetta**, PhD student, University of Pisa | |
| |
| ===== Hours and Rooms ===== | ===== Hours and Rooms ===== |
| ^ Day of Week ^ Hour ^ Room ^ | ^ Day of Week ^ Hour ^ Room ^ |
| | Thursday | 14:00 - 16:00 | Room Fib L1 | | | Monday | 16:00 - 18:00 | Room Fib L1 | |
| | Friday | 14:00 - 16:00 | Room Fib C1 | | | Tuesday | 16:00 - 18:00 | Room Fib M1 | |
| |
| * Beginning of lectures: 21 September 2023 | * Beginning of lectures: 15 September 2025 |
| * End of lectures: 7 December 2023 | * End of lectures: 16 December 2025 |
| * Possible lessons recovered: 8–15 December 2023 | |
| |
| __**The lectures will be only in presence and will NOT be live-streamed**__ | __**The lectures will be only in person and will NOT be live-streamed**__ |
| |
| |
| ====== News and communications ====== | |
| |
| __No lesson__ on November 21st and 22nd | |
| |
| **APPELLI**: The dates of the exams are the following (remember to register for the appello in time): | |
| * January 16th, 14:00 | |
| * February 7th, 09:00 | |
| |
| __No lesson__ on October 31st; | |
| | |
| __No lessons__ on October 10 and 11 (because of the evento "Orientamento studenti") | |
| |
| ====== Learning goals ====== | ====== Learning goals ====== |
| * Digital spatial and mobility data | * Digital spatial and mobility data |
| * Preprocessing mobility data | * Preprocessing mobility data |
| * Privacy issues in mobility data | |
| * Individual and collective mobility laws | * Individual and collective mobility laws |
| * Next-location and flow prediction | * Next-location and flow prediction |
| * Applications | * Applications |
| |
| ===== Module 1: Spatial and Mobility Data Analysis ===== | ===== Module 1: Data Analysis ===== |
| |
| * Fundamentals of Geographical Information Systems | * Fundamentals of Geographical Information Systems |
| * Spatial Tessellations | * Spatial Tessellations |
| * Flows | * Flows |
| * **Practice**: Python packages for geospatial analysis (Shapely, GeoPandas, folium, scikit-mobility) | * **Practice** |
| * Digital spatial and mobility data | * Digital spatial and mobility data |
| * Mobile Phone Data | * Mobile Phone Data |
| * Social media data | * Social media data |
| * Other data (POIs, Road Networks, etc.) | * Other data (POIs, Road Networks, etc.) |
| * **Practice**: reading and exploring spatial and mobility datasets in Python | * **Practice** |
| * Preprocessing mobility data | * Preprocessing mobility data |
| * filtering compression | * filtering compression |
| * trajectory segmentation | * trajectory segmentation |
| * trajectory similarity and clustering | * trajectory similarity and clustering |
| * **Practice**: data preprocessing with scikit-mobility | * **Practice** |
| |
| ===== Module 2: Mobility Patterns and Laws ===== | ===== Module 2: Patterns and Laws ===== |
| |
| * individual mobility laws/patterns | * individual mobility laws |
| * collective mobility laws/patterns | * collective mobility laws |
| * Practice: analyze mobility data with Python | * mobility pattern mining |
| | * **Practice** |
| |
| ===== Module 3: Predictive and Generative Models ===== | ===== Module 3: Models ===== |
| |
| * Prediction | * Prediction |
| * Trajectory generation | * Trajectory generation |
| * Flow generation | * Flow generation |
| * Practice: mobility prediction and generation in Python | * **Practice** |
| |
| ===== Module 4: Applications ===== | |
| |
| * Urban segregation models | |
| * Routing and navigation apps | |
| * Traffic simulation with SUMO | |
| |
| |
| |
| ^ ^ Day ^ Topic ^ Slides/Code ^ Material ^ Teacher| | ^ ^ Day ^ Topic ^ Slides/Code ^ Material ^ Teacher| |
| |1. |19.09 14:00-16:00| Introduction to the Course | **[slides]** {{ :geospatialanalytics:gsa:00_-_about_the_course_24_25.pdf | About the course}}; **[slides]** {{ :geospatialanalytics:gsa:01_-_introduction_24_25.pdf | Introduction to Geospatial Analytics}} | **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Chapter 1; **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]], Section 1| Pappalardo | | |1. |**15/09**, 16:00-18:00, Fib L1| Introduction to the Course | **[slides]** {{ :geospatialanalytics:gsa:lesson_0_-_about_the_course.pdf | About the course}} {{ :geospatialanalytics:gsa:lesson_01_-_introduction.pdf | Introduction to Geospatial Analytics and Human Mobility}}| **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Chapter 1; **[paper]** [[https://arxiv.org/pdf/1710.00004.pdf | Human Mobility: Models and Applications]], Section 1 | L. Pappalardo, M. Nanni | |
| |2. |20.09 14:00-16:00| Fundamental Concepts (theory)| **[slides]** {{ :geospatialanalytics:gsa:02_-_fundamental_concepts_24_25.pdf | Fundamental Concepts}} | **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Chapter 2 (Coordinate Systems); **[paper]** [[https://arxiv.org/abs/2012.02825 | A survey of deep learning for human mobility]], Section 2.1, Appendix A; [[https://saylordotorg.github.io/text_essentials-of-geographic-information-systems/s08-02-vector-data-models.html | Essentials of Geographic Information Systems,Chapter 4, Section 4.2 (Vector Data Models)]]; **[video]** [[https://www.youtube.com/watch?v=HnWNhyxyUHg | Intro to coordinate systems and UTM projection]] | Pappalardo | | |2. |**16.09** 16:00-18:00 Fib M1| Fundamental Concepts I | **[slides]** {{ :geospatialanalytics:gsa:lesson_02_-_fundamental_concepts.pdf | Fundamental concepts}} | **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Chapter 2 (Coordinate Systems); **[video]** [[https://www.youtube.com/watch?v=HnWNhyxyUHg | Intro to coordinate systems and UTM projection]] | L. Pappalardo | |
| |3. |26.09 14:00-16:00| Fundamental Concepts (practice)| **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/2024/1-%20Fundamental%20Concepts | Introduction to shapely, geopandas, folium, and scikit-mobility]] | **[book chapter]** [[ https://autogis-site.readthedocs.io/en/latest/notebooks/L1/geometric-objects.html | Automating GIS-processes, Lesson 1 (Shapely and geometric objects)]]; **[article]** [[ https://www.learndatasci.com/tutorials/geospatial-data-python-geopandas-shapely/ | Analyze Geospatial Data in Python: GeoPandas and Shapely]]; **[paper]** [[https://www.jstatsoft.org/article/view/v103i04 | scikit-mobility: a Python library for the Analysis, Generation, and Risk Assessment of Mobility Data]], Sections 1, 2; | Mauro | | |3. |**29.09** 16:00-18:00 Fib L1| Fundamental Concepts II | **[slides]** {{ :geospatialanalytics:gsa:lesson_02_-_fundamental_concepts.pdf | Fundamental concepts}} | **[paper]** [[https://arxiv.org/abs/2012.02825 | A survey of deep learning for human mobility]], Section 2.1, Appendix A; | L. Pappalardo | |
| |4. |27.09 14:00-16:00| Spatial Data Analysis I (theory) | **[slides]** {{ :geospatialanalytics:gsa:03_-_spatial_data_analysis_24_25.pdf | Spatial Data Analysis I}} | **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Sect. 3.1, 3.3, 4.1-4.3, 4.7, 8.5, Chapter 11; **[book chapter]** [[ https://mgimond.github.io/Spatial | Intro to GIS and Spatial Analysis]], Chapter 11, 13; **[book section]** [[ https://doi.org/10.1007/978-0-387-35973-1_446 | Encyclopedia of GIS: Geary’s C]] | Nanni | | |4. |**30.09** 16:00-18:00 Fib M1| Practical session on Fundamental Concepts | **[code]**{{ :geospatialanalytics:gsa:gsa1.zip | Notebook}} | | L. Pappalardo, G. Mauro | |
| |5. |03.10 14:00-16:00| Spatial Data Analysis II (theory) | **[slides]** {{ :geospatialanalytics:gsa:03bis_-_spatial_data_analysis_24_25_v1.pdf | Spatial Data Analysis II}} | **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Chapter 15; **[book chapter]** [[ https://mgimond.github.io/Spatial | Intro to GIS and Spatial Analysis]], Chapter 14; **[book section]** [[ https://sustainability-gis.readthedocs.io/en/latest/ | Spatial data science for sustainable development]], Tutorial 3 (Spatial Regression); **[paper]** [[ https://doi.org/10.1007/s10619-019-07278-7 | Spatial co-location patterns]], Sect. 3.1; **[paper]** [[ https://www.lri.fr/~sebag/Examens/Ester_KDD98.pdf | Trend Detection in Spatial Databases ]], Sect. 4 | Nanni | | |5. |**06.10** 16:00-18:00 Fib L1| Spatial Data Analysis I | **[slides]** {{ :geospatialanalytics:gsa:03_-_spatial_data_analysis_25_26.pdf |Spatial Data Analysis I}} | **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Sect. 3.1, 3.3, 4.1-4.3, 4.7, 8.5, Chapter 11; **[book chapter]** [[ https://mgimond.github.io/Spatial | Intro to GIS and Spatial Analysis]], Chapter 11, 13; **[book section]** [[ https://doi.org/10.1007/978-0-387-35973-1_446 | Encyclopedia of GIS: Geary’s C]] | M. Nanni | |
| |6. |04.10 14:00-16:00| Spatial Data Analysis II (practice) | **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/2024/2-%20Spatial%20Data%20Analysis | Spatial Analysis exercises]] | [[https://pysal.org/pysal/|PySAL: Python Spatial Analysis Library]]; [[https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html|Scikit-learn KNeighborsRegressor]]; [[https://geostat-framework.readthedocs.io/projects/pykrige/en/stable/|PyKrige]] | Nanni | | |6. |**07.10** 16:00-18:00 Fib M1| Spatial Data Analysis II | **[slides]** {{ :geospatialanalytics:gsa:03bis_-_spatial_data_analysis_25_26.pdf | Spatial Data Analysis II}} | **[book chapter]** [[ https://archive.org/details/kang-tsung-chang-introduction-to-geographic-information-systems-2019-mc-graw-hill-libgen.lc/page/15/mode/2up | Introduction to geographic information systems]], Chapter 15; **[book chapter]** [[ https://mgimond.github.io/Spatial | Intro to GIS and Spatial Analysis]], Chapter 14; **[book section]** [[ https://sustainability-gis.readthedocs.io/en/latest/ | Spatial data science for sustainable development]], Tutorial 3 (Spatial Regression); **[paper]** [[ https://doi.org/10.1007/s10619-019-07278-7 | Spatial co-location patterns]], Sect. 3.1; **[paper]** [[ https://www.lri.fr/~sebag/Examens/Ester_KDD98.pdf | Trend Detection in Spatial Databases ]], Sect. 4 | M. Nanni | |
| |7. |17.10 14:00-16:00| Geographic and Mobility data (theory) | **[slides]** {{ :geospatialanalytics:gsa:04-_spatial_and_mobility_data.pdf | Geographic and Mobility Data}} | **[paper]** [[ https://arxiv.org/abs/2012.02825 | A survey of deep learning for human mobility ]], Appendix C.1, C.2, C.3; **[paper]** [[ https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-021-00284-9 | Evaluation of home detection algorithms on mobile phone data using individual-level ground truth ]], Section 1 "Introduction", Section 2 "Mobile phone datasets"; **[paper]** [[ https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0046-0 | A survey of results on mobile phone datasets analysis ]], Section 1 "Introduction", Section 3 "Adding space - geographical networks"; **[paper]** [[ https://www.kdd.org/exploration_files/June_2019_-_1._Urban_Human_Mobility,_Data_Drive_Modeling_and_Prediction_.pdf | Urban Human Mobility: Data-Driven Modeling and Prediction]], Section 2.2 "Popular Urban Data"; | Pappalardo | | |7. |13.10 16:00-18:00 Fib L1| Practical session on Spatial Data Analysis | **[code]** {{ :geospatialanalytics:gsa:gsa_spatial_patterns_13oct_2025.ipynb.zip | Notebook}} | | M. Nanni, G. Cornacchia | |
| |8. | 18.10 14:00-16:00| Geographic and Mobility data (practice) | **[code]** [[https://github.com/jonpappalord/geospatial_analytics/blob/main/2024/3-%20Mobility%20Data/practice_CDR_GPS.ipynb | Exercise: converting a GPS trace into CDR one]] | **[paper]** [[https://www.jstatsoft.org/article/view/v103i04 | scikit-mobility: a Python library for the Analysis, Generation, and Risk Assessment of Mobility Data]], Section 4 "Plotting"; **[video]** [[ https://www.youtube.com/watch?v=FjJZsaHHuvw | scikit-mobility data module]]; **[tutorial]** [[https://geoffboeing.com/2016/11/osmnx-python-street-networks/| OSMnx: Python for Street Networks]]; **[paper]** [[ https://www.sciencedirect.com/science/article/pii/S0198971516303970?via%3Dihub | OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks]]; **[book chapter]** [[ https://automating-gis-processes.github.io/CSC/notebooks/L3/retrieve_osm_data.html | Intro to Python GIS, Retrieving OpenStreetMap data ]]; | Cornacchia| | |8. |14:10 16:00-18:00 Fib M1| Mobility Data I | **[slides]** {{ :geospatialanalytics:gsa:04-_spatial_and_mobility_data_25_26_1_.pdf | Mobility Data}} | **[paper]** [[ https://arxiv.org/abs/2012.02825 | A survey of deep learning for human mobility ]], Appendix C.1, C.2, C.3; **[paper]** [[ https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-021-00284-9 | Evaluation of home detection algorithms on mobile phone data using individual-level ground truth ]], Section 1 "Introduction", Section 2 "Mobile phone datasets"; **[paper]** [[ https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0046-0 | A survey of results on mobile phone datasets analysis ]], Section 1 "Introduction", Section 3 "Adding space - geographical networks"; | L. Pappalardo | |
| |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. |20.10 16:00-18:00 Fib L1| Mobility Data II | **[slides]** {{ :geospatialanalytics:gsa:04-_spatial_and_mobility_data_25_26_1_.pdf | Mobility Data}} | **[paper]** [[ https://www.kdd.org/exploration_files/June_2019_-_1._Urban_Human_Mobility,_Data_Drive_Modeling_and_Prediction_.pdf | Urban Human Mobility: Data-Driven Modeling and Prediction]], Section 2.2 "Popular Urban Data"; | L. Pappalardo | |
| |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. |21.10 16:00-18:00 Fib M1| Practical session on Mobility Data | **[code]**{{ :geospatialanalytics:gsa:gsa_cdr_and_gps_20oct_2025.ipynb.zip | Notebook}} | | L. Pappalardo, G. 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. |27.10 16:00-18:00 Fib L1| Preprocessing I | **[slides]** {{ :geospatialanalytics:gsa:05_-_preprocessing_25_26.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]] | M. Nanni | |
| |13. | 08.11 14:00-16:00| Individual Mobility Patterns (practice) | {{ :geospatialanalytics:gsa:practice_individual_measures.zip | Practice session on individual measures}} | | Mauro| | |12. |28.10 16:00-18:00 Fib M1| Preprocessing II | | | M. Nanni | |
| |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. |03.11 16:00-18:00 Fib L1| Practical session on Preprocessing | | | M. Nanni, G. Cornacchia | |
| |15. | 15.11 14:00-16:00| Individual and Collective Mobility models (practice) | | | Mauro | | |14. |04.11 16:00-18:00 Fib M1| Individual Mobility Laws | **[slides]** {{ :geospatialanalytics:gsa:lesson_06_-_individual_mobility_laws.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| |
| |16. | 28.11 14:00:16:00| Guest lecture | | | [[ https://www.riccardodiclemente.com/| Riccardo Di Clemente]]| | |15. |10.11 16:00-18:00 Fib L1| Mobility Models I | {{ :geospatialanalytics:gsa:lesson_07_-_mobility_models.pdf |}} | **[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]] (Section 4.1); | L. Pappalardo | |
| |17. | 29.11 14:00:16:00| Mobility pattern mining | | | Nanni| | |16. |11.11 16:00-18:00 Fib M1| Practical session on Individual Mobility Laws and Models | | | L. Pappalardo, G. Mauro | |
| |18. | 05.12 14:00:16:00| Next-location prediction | | | Nanni | | |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 | |
| |19. | 06.12 14:00:16:00| Mobility pattern mining and next-location prediction (practice) | | | Cornacchia | | |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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| | |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 | |
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| ==== Previous Geospatial Analytics websites ==== | ==== Previous Geospatial Analytics websites ==== |
| | * [[geospatialanalytics:gsa:gsa2024|]] |
| * [[geospatialanalytics:gsa:gsa2023|]] | * [[geospatialanalytics:gsa:gsa2023|]] |
| * [[geospatialanalytics:gsa:gsa2022|]] | * [[geospatialanalytics:gsa:gsa2022|]] |
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