====== 783AA Geospatial Analytics A.A. 2026/27 ====== ===Instructors:=== * **Luca Pappalardo** * [[luca.pappalardo@isti.cnr.it]] * KDD Laboratory, ISTI-CNR and Scuola Normale Superiore, Pisa * [[http://www-kdd.isti.cnr.it]] * **Mirco Nanni** * [[mirco.nanni@isti.cnr.it]] * KDD Laboratory, ISTI-CNR, Pisa * [[http://www-kdd.isti.cnr.it]] * **Giovanni Mauro** * [[giovanni.mauro@sns.it]] * KDD Laboratory, Scuola Normale Superiore, Pisa * [[http://www-kdd.isti.cnr.it]] ===== Hours and Rooms ===== ^ Day of Week ^ Hour ^ Room ^ | Monday | 14:00 - 16:00 | Room FIB I-LAB | | Friday | 11:00 - 13:00 | Room FIB I-LAB | * Beginning of lectures: 18 September 2026 * End of lectures: 18 December 2026 __**The lectures will be only in person and will NOT be live-streamed**__ ====== Learning goals ====== 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 assessment of the course consists of an oral exam, aimed to test the knowledge acquired by the student during the course. Topics: * Spatial Reference Systems * Data formats * Trajectory and Flows * Spatial Tessellations * Open-source tools for geospatial analysis * Digital spatial and mobility data * Preprocessing mobility data * Individual and collective mobility laws * Next-location and flow prediction * Trajectory and flow generation * Applications ===== Module 1: Data Analysis ===== * Fundamentals of Geographical Information Systems * Geographic coordinates systems * Vector data model * Trajectories * Spatial Tessellations * Flows * **Practice** * Digital spatial and mobility data * Mobile Phone Data * GPS data * Social media data * Other data (POIs, Road Networks, etc.) * **Practice** * Preprocessing mobility data * filtering compression * stop detection * trajectory segmentation * trajectory similarity and clustering * **Practice** ===== Module 2: Patterns and Laws ===== * individual mobility laws * collective mobility laws * mobility pattern mining * **Practice** ===== Module 3: Models ===== * Prediction * Next-location prediction * Crowd flow prediction * Spatial interpolation * Generation * Trajectory generation * Flow generation * **Practice** ====== News and communications ====== On **__Friday, October 16th__**, there will be **__no lesson__** (because of UnipiOrienta). ====== Calendar ====== ^ ^ Day ^ Topic ^ Slides/Code ^ Material ^ Teacher| |1. |**18/09**, 11:00-13:00, FIB I-LAB| Introduction to the Course | **[Slides]** {{ :geospatialanalytics:gsa:lesson_00_-_about_the_course_-_26_27.pdf | About the course}} {{ :geospatialanalytics:gsa:lesson_01_-_introduction_-_26_27.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, G. Mauro| |2. |**21/09**, 14:00-16:00, FIB I-LAB| Fundamental Concepts - Part I | **[Slides]** {{ :geospatialanalytics:gsa:lesson_02_-_fundamental_concepts_-_part_i_-_26_27.pdf | Fundamental concepts Part 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]], Chapter 2 (Coordinate Systems); **[video]** [[https://www.youtube.com/watch?v=HnWNhyxyUHg | Intro to coordinate systems and UTM projection]]; | L. Pappalardo| |3. |**25/09**, 11:00-13:00, FIB I-LAB| Fundamental Concepts II| **[Slides]** {{ :geospatialanalytics:gsa:lesson_02_-_fundamental_concepts_-_part_ii_-_26_27.pdf | Fundamental Concepts Part II}} | **[paper]** [[https://arxiv.org/abs/2012.02825 | A survey of deep learning for human mobility]], Section 2.1, Appendix A; | L. Pappalardo| |4. |**28/09**, 14:00-16:00, FIB I-LAB| Practical session on Fundamental Concepts | **[code]** {{ :geospatialanalytics:gsa:lesson1.zip | Notebook}} | | G. Mauro| |5. |**02/10**, 11:00-13:00, FIB I-LAB| Spatial Data Analysis I | **[slides]** {{ :geospatialanalytics:gsa:lesson_03_-_spatial_data_analysis_-_part_1_2026.pdf |Spatial Data Analysis I}} | **Main material:** **[Lecture notes]** [[https://unipiit.sharepoint.com/:b:/s/a__td_72112/IQACiFhdVpBJSqWDaiJtZYiIAS_n_xEil1ItHjvMRczQNLo?e=M3xrHv|Elements of spatial data representation and analysis]] Sect. 1-3, **Complementary material:** **[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. |**05/10**, 14:00-16:00, FIB I-LAB| Spatial Data Analysis II | **[slides]** {{ :geospatialanalytics:gsa:lesson_03_-_spatial_data_analysis_-_part_2_2026.pdf |Spatial Data Analysis II}} | **Main material:** **[Lecture notes]** [[https://unipiit.sharepoint.com/:b:/s/a__td_72112/IQACiFhdVpBJSqWDaiJtZYiIAS_n_xEil1ItHjvMRczQNLo?e=M3xrHv|Elements of spatial data representation and analysis]] Sect. 4-6, **Complementary material:** **[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; **[paper]** [[ https://doi.org/10.1007/s10619-019-07278-7 | Spatial co-location patterns]], Sect. 3.1 | M. Nanni| |7. |**09/10**, 11:00-13:00, FIB I-LAB| Practical session on Spatial Data Analysis | **[code]** {{ :geospatialanalytics:gsa:spatial_data.zip |Notebook}} | | G. Mauro| |8. |**12/10**, 14:00-16:00, FIB I-LAB| Mobility Data I | **[slides]** {{ :geospatialanalytics:gsa:mobility_data_i.pdf | Mobility Data I}} | **[paper]** [[ https://arxiv.org/abs/2012.02825 | A survey of deep learning for human mobility ]], Appendix C.1, C.2, C.3; | L. Pappalardo| |-|**16/10** | NO LESSON (for Unipi-Orienta) | | | | |9. |**19/10**, 11:00-13:00, FIB I-LAB| Mobility Data II | **[slides]** {{ :geospatialanalytics:gsa:mobility_data_ii.pdf | Mobility data II}} | **[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| |10. |**23/10**, 14:00-16:00, FIB I-LAB| Practical session on Mobility Data | | | G. Mauro| |11. |**26/10**, 11:00-13:00, FIB I-LAB| Preprocessing I | **[slides]** {{ :geospatialanalytics:gsa:lesson_05_-_preprocessing_2026.pdf |Data Preprocessing}} | **Main material:** **[Textbook Chapter]** Data Preprocessing; **Complementary material:** **[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. |**30/10**, 14:00-16:00, FIB I-LAB| Preprocessing II | | | M. Nanni| |13. |**06/11**, 11:00-13:00, FIB I-LAB| Individual Mobility Laws I | | | L. Pappalardo| |14. |**09/11**, 11:00-13:00, FIB I-LAB| Practical session on Preprocessing | | | G. Mauro| |15. |**13/11**, 14:00-16:00, FIB I-LAB| Individual Mobility Laws II | | | L. Pappalardo| |16. |**16/11**, 11:00-13:00, FIB I-LAB| Mobility Models I | | | G. Mauro| |17. |**20/11**, 14:00-16:00, FIB I-LAB| Mobility Models II | | | G. Mauro| |18. |**23/11**, 11:00-13:00, FIB I-LAB| Practical session on Mobility Laws and Models | | | G. Mauro| |19. |**27/11**, 14:00-16:00, FIB I-LAB| Mobility Pattern Mining I | | | M. Nanni| |20. |**30/11**, 11:00-13:00, FIB I-LAB| Mobility Pattern Mining II | | | M. Nanni| |21. |**04/12**, 14:00-16:00, FIB I-LAB| Next-location prediction | | | M. Nanni| |22. |**11/12**, 11:00-13:00, FIB I-LAB| Impact of preprocessing on next-location prediction | | | K. Smolak| |23. |**14/12**, 11:00-13:00, FIB I-LAB| Practical session on Mobility Pattern Mining and Next-location Prediction | | | G. Mauro| |24. |**18/12**, 14:00-16:00, FIB I-LAB| Location-based recommender systems | | | G. Mauro| ==== Previous Geospatial Analytics websites ==== * [[geospatialanalytics:gsa:gsa2025|]] * [[geospatialanalytics:gsa:gsa2024|]] * [[geospatialanalytics:gsa:gsa2023|]] * [[geospatialanalytics:gsa:gsa2022|]]