| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| geospatialanalytics:gsa:start [10/09/2024 alle 12:58 (15 mesi fa)] – Luca Pappalardo | geospatialanalytics:gsa:start [02/12/2025 alle 09:57 (7 giorni fa)] (versione attuale) – [Calendar] Mirco Nanni |
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| ====== 783AA Geospatial Analytics A.A. 2024/25 ====== | ====== 783AA Geospatial Analytics A.A. 2025/26 ====== |
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| ===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]] |
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| * [[http://www-kdd.isti.cnr.it]] | * [[http://www-kdd.isti.cnr.it]] |
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| ===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 | |
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| ===== 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 C | | | Tuesday | 16:00 - 18:00 | Room Fib M1 | |
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| * 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 | |
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| __**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**__ |
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| The analysis of geographic information, such as those describing human movements, is crucial due to its impact on several aspects of our society, such as disease spreading (e.g., the COVID-19 pandemic), urban planning, well-being, pollution, and more. This course will teach the fundamental concepts and techniques underlying the analysis of geographic and mobility data, presenting data sources (e.g., mobile phone records, GPS traces, geotagged social media posts), data preprocessing techniques, statistical patterns, predicting and generative algorithms, and real-world applications (e.g., diffusion of epidemics, socio-demographics, link prediction in social networks). The course will also provide a practical perspective through the use of advanced geoanalytics Python libraries. | The analysis of geographic information, such as those describing human movements, is crucial due to its impact on several aspects of our society, such as disease spreading (e.g., the COVID-19 pandemic), urban planning, well-being, pollution, and more. This course will teach the fundamental concepts and techniques underlying the analysis of geographic and mobility data, presenting data sources (e.g., mobile phone records, GPS traces, geotagged social media posts), data preprocessing techniques, statistical patterns, predicting and generative algorithms, and real-world applications (e.g., diffusion of epidemics, socio-demographics, link prediction in social networks). The course will also provide a practical perspective through the use of advanced geoanalytics Python libraries. |
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| The assessment of the course consists of: (1) an oral exam, aimed to test the knowledge acquired by the student during the course; (2) exercises to be done during the course; (3) the development of a project to test the practical ability acquired during the course. | The assessment of the course consists of an oral exam, aimed to test the knowledge acquired by the student during the course. |
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| Topics: | Topics: |
| * 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 |
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| ===== Module 1: Spatial and Mobility Data Analysis ===== | ===== Module 1: Data Analysis ===== |
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| * 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** |
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| ===== Module 2: Mobility Patterns and Laws ===== | ===== Module 2: Patterns and Laws ===== |
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| * individual mobility laws/patterns | * individual mobility laws |
| * collective mobility laws/patterns | * collective mobility laws |
| * Practice: analyze mobility data with Python | * mobility pattern mining |
| | * **Practice** |
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| ===== Module 3: Predictive and Generative Models ===== | ===== Module 3: Models ===== |
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| * Prediction | * Prediction |
| * Trajectory generation | * Trajectory generation |
| * Flow generation | * Flow generation |
| * Practice: mobility prediction and generation in Python | * **Practice** |
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| ===== Module 4: Applications ===== | |
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| * Urban segregation models | |
| * Routing and navigation apps | |
| * Traffic simulation with SUMO | |
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| ^ ^ 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_1_.pdf | About the course}}; **[slides]** {{ :geospatialanalytics:gsa:01_-_introduction_1_.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, Nanni | | |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.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. |**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. |**30.09** 16:00-18:00 Fib M1| Practical session on Fundamental Concepts | **[code]**{{ :geospatialanalytics:gsa:gsa1.zip | Notebook}} | | L. Pappalardo, G. Mauro | |
| | |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. |**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. |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. |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. |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. |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 | |
| | |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 | |
| | |12. |28.10 16:00-18:00 Fib M1| Preprocessing II | | | M. Nanni | |
| | |13. |03.11 16:00-18:00 Fib L1| Practical session on Preprocessing | | | M. Nanni, G. Cornacchia | |
| | |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| |
| | |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 | |
| | |16. |11.11 16:00-18:00 Fib M1| Practical session on Individual Mobility Laws and Models | | | L. Pappalardo, G. Mauro | |
| | |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 | |
| | |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:gsa2023|]] | * [[geospatialanalytics:gsa:gsa2024|]] |
| | * [[geospatialanalytics:gsa:gsa2023|]] |
| | * [[geospatialanalytics:gsa:gsa2022|]] |
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