| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| geospatialanalytics:gsa:start [30/11/2023 alle 10:58 (10 mesi fa)] – [News and communications] Luca Pappalardo | geospatialanalytics:gsa:start [26/09/2024 alle 12:12 (11 ore fa)] (versione attuale) – [Learning goals] Luca Pappalardo |
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| <html> | ====== 783AA Geospatial Analytics A.A. 2024/25 ====== |
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| ====== 783AA Geospatial Analytics A.A. 2023/24 ====== | |
| |
| ===Instructors:=== | ===Instructors:=== |
| ===== Hours and Rooms ===== | ===== Hours and Rooms ===== |
| ^ Day of Week ^ Hour ^ Room ^ | ^ Day of Week ^ Hour ^ Room ^ |
| | Thursday | 09:00 - 11:00 | Room Fib M1 | | | Thursday | 14:00 - 16:00 | Room Fib L1 | |
| | Friday | 09:00 - 11:00 | Room Fib M1 | | | Friday | 14:00 - 16:00 | Room Fib C1 | |
| |
| * Beginning of lectures: 21 September 2023 | * Beginning of lectures: 21 September 2023 |
| |
| __**The lectures will be only in presence and will NOT be live-streamed**__ | __**The lectures will be only in presence and will NOT be live-streamed**__ |
| | |
| |
| ====== News and communications ====== | ====== News and communications ====== |
| * **List of available {{ :geospatialanalytics:gsa:projects_2023_2024.pdf |projects}}** | |
| * **Projects Bidding [[https://forms.gle/pZ2E5VD3BJ9wDQZC8|form]]**. Please express your preference for projects by __**Sunday, December 3rd, 2023**__. The project will be assigned on Monday, December 4th, 2023. | __No lessons__ on October 10 and 11 (because of the evento "Orientamento studenti") |
| * **Temporary fixes for scikit-mobility library** | |
| * some users of the library might experience issues due to updates in shapely (2.0.0). The quick fix for that is to modify line 635 of file "utils/plot.py" (in the library folder) into "vertices = [list(zip(*p.exterior.xy)) for p in gway.geoms]" -- basically, add a ".geoms". | |
| * the NYC foursquare dataset was recently moved. To use it with the load_dataset() function, you should update the URL to the new one: "url":"http://www-public.tem-tsp.eu/~zhang_da/pub/dataset_tsmc2014.zip". This is currently used by datasets foursquare_nyc and flow_foursquare_nyc | |
| * Both issues above will be soon fixed in the library. | |
| |
| ====== Learning goals ====== | ====== Learning goals ====== |
| * Applications | * Applications |
| |
| ===== Module 1: Spatial and Mobility Data ===== | ===== Module 1: Spatial and Mobility Data Analysis ===== |
| |
| * Fundamentals of Geographical Information Systems | * Fundamentals of Geographical Information Systems |
| |
| ^ ^ Day ^ Topic ^ Slides/Code ^ Material ^ Teacher| | ^ ^ Day ^ Topic ^ Slides/Code ^ Material ^ Teacher| |
| |1. |21.09 09:00-11:00| 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. |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, Nanni | |
| |2. |22.09 09:00-11:00| NO LESSON | | | | | |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 | |
| |3. |28.09 09:00-11:00| 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 | | |3. |26.09 04:00-16:00| Fundamental Concepts (practice)| **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson2_fundamental_concepts | 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 | |
| |4. |29.09 09:00-11:00| Fundamental Concepts (practice)| **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson2_fundamental_concepts | 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; | Pappalardo, Mauro | | |
| |5. |05.10 09:00-11:00| Geographic and Mobility data (theory) | **[slides]** {{ :geospatialanalytics:gsa:03_-_spatial_and_mobility_data_2023.pdf |Geospatial 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"; | Nanni | | |
| |6. |06.10 09:00-11:00| Geographic and Mobility data (practice) | **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson3_spatial_and_mobility_data | Geospatial and Mobility data in Python]] | **[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 ]]; | Nanni | | |
| |7. |12.10 09:00-11:00| Data preprocessing (theory) | **[slides]** {{ :geospatialanalytics:gsa:04_-_preprocessing_-_full.pdf |Trajectory 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]] | Nanni | | |
| |8. |13.10 09:00-11:00| NO LESSON, for atheneum ordinance | | | | | |
| |9. | 19.10 09:00-11:00 | Data preprocessing (theory and practice) | **[slides]** {{ :geospatialanalytics:gsa:lesson_04-part2_-_preprocessing.pdf | Semantic Enrichment}}, **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson4_preprocessing | Preprocessing Mobility data]] | **[paper]** [[https://eprints.gla.ac.uk/128784/1/128784.pdf|Analysis of human mobility patterns from GPS trajectories and contextual information]]; **[paper]** [[https://www.researchgate.net/publication/233197970_Using_Mobile_Positioning_Data_to_Model_Locations_Meaningful_to_Users_of_Mobile_Phones|Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones]]; **[paper]** [[https://www.pnas.org/doi/10.1073/pnas.1408439111|Dynamic population mapping using mobile phone data]]; **[paper]** [[https://dl.acm.org/doi/abs/10.1145/2505821.2505830|Inferring human activities from GPS tracks]]| Nanni | | |
| |10. |20.10 09:00-11:00| Alternative Routing (theory and practice) | **[slides]** {{ :geospatialanalytics:gsa:05_-_alternative_routing.pdf | Alternative Routing}}; **[code]** [[ https://github.com/jonpappalord/geospatial_analytics/tree/main/AlternativeRouting | Alternative Routing in Python]] | **[paper]** [[https://dl.acm.org/doi/10.1145/3357000.3366137| Shortest-Path Diversification through Network Penalization: A Washington DC Area Case Study]]; **[paper]** [[https://arxiv.org/pdf/2306.13704.pdf | One-Shot Traffic Assignment with Forward-Looking Penalization]]; **[paper]** [[ https://arxiv.org/abs/2006.08475 | Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks]] | Pappalardo | | |
| |11. |26.10 09:00-11:00| Individual Human Mobility Laws and Models (theory) | **[slides]** {{ :geospatialanalytics:gsa:06_-_individual_models.pdf | Individual Mobility Laws and Models}} | **[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 | | |
| |12. |27.10 09:00-11:00| Individual Human Mobility Laws and Models (practice)| **[code]**[[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson5_mobilitylaws_and_models | Mobility laws and models]] | [[https://scikit-mobility.github.io/scikit-mobility/reference/measures.html | scikit-mobility documentation: measures]], [[https://scikit-mobility.github.io/scikit-mobility/reference/models.html | scikit-mobility documentation: models]] | Pappalardo, Mauro | | |
| |13. |02.11 09:00-11:00| Mobility Patterns (theory) | **[slides]** {{ :geospatialanalytics:gsa:07_-_mobility_patterns_2023.pdf |Mobility Patterns}} | **[paper]** [[https://dl.acm.org/doi/10.1145/3440207|A Survey on Trajectory Data Management, Analytics, and Learning]], Section 3; **[paper]** [[https://faculty.ist.psu.edu/jessieli/Publications/VLDB10-ZLi-Swarm.pdf|Swarm: Mining Relaxed Temporal Moving Object Clusters]]; **[paper]** [[https://dl.acm.org/doi/10.1145/1183471.1183479|Computing longest duration flocks in trajectory data]]; **[paper]** [[https://dl.acm.org/doi/10.1145/1281192.1281230|Trajectory pattern mining]]; **[paper]** [[https://www.researchgate.net/publication/225140109_On_Discovering_Moving_Clusters_in_Spatio-temporal_Data|On Discovering Moving Clusters in Spatio-temporal Data]] | Nanni | | |
| |14. |03.11 09:00-11:00| Collective Mobility Laws and Models (theory and practice) | **[slides]** {{ :geospatialanalytics:gsa:lesson_08_-_collective_models.pdf | Collective mobility laws and models}} | **[paper]** [[ https://arxiv.org/abs/1710.00004 |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]] | Pappalardo | | |
| |15. |09.11 09:00-11:00| Spatial segregation models (theory) | **[slides]** {{ :geospatialanalytics:gsa:09_-_segregation.pdf | Segregation Models}} | **[paper]** [[https://www.tandfonline.com/doi/abs/10.1080/0022250X.1971.9989794 |Dynamic models of segregation, Schelling]]; **[paper]** [[https://www.nature.com/articles/s41598-023-38519-6 |Mobility constraints in segregation models]]; | Mauro | | |
| |16. |10.11 09:00-11:00| Spatial segregation models (practice) | **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson9_segregation|Implementing the Schelling model with MESA]] | **[tutorial]** [[https://mesa.readthedocs.io/en/latest/tutorials/intro_tutorial.html|Introduction to MESA]] | Mauro, Gambetta | | |
| |17. |16.11 09:00-11:00| Next-Location Prediction (theory) | **[slides]** {{ :geospatialanalytics:gsa:10_-_location_prediction.pdf | Slides}} | [[https://hmmlearn.readthedocs.io/en/latest/|HMMlearn library]]; **[paper]** [[https://ieeexplore.ieee.org/document/8570749|Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications]]; **[paper]** [[https://ieeexplore.ieee.org/document/9756903|A Survey on Trajectory-Prediction Methods | |
| for Autonomous Driving]], Sections IV and V; **[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 | | |
| |18. |17.11 09:00-11:00| Next-Location Prediction (theory and practice) + Introduction to QGIS (practice) | **[code]** {{ :geospatialanalytics:gsa:hmm.zip |HMM notebook}} | https://www.qgis.org/it/site/ | Nanni, Özge Öztürk | | |
| |19. |23.11 09:00-11:00| Traffic Simulation with SUMO (theory and practice) | **[slides]** {{ :geospatialanalytics:gsa:11_-_traffic_simulation_with_sumo.pdf | Traffic simulation with SUMO}}; **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson10_sumo|Traffic simulation with SUMO]] | | Cornacchia | | |
| |20. |24.11 09:00-11:00| Traffic Simulation with SUMO (theory and practice) | **[code]** [[https://github.com/jonpappalord/geospatial_analytics/tree/main/lesson10_sumo|Routing on road networks]] | | Cornacchia | | |
| |21. |30.11 09:00-11:00| Presentation of projects | | | Pappalardo, Nanni, Cornacchia, Mauro, Gambetta | | |
| |22. |01.12 9:00-11:00| NO LESSON (Laurea sessions) | | | | | |
| |23. |07.12 9:00-11:00| Seminars by PhD students | | | Gambetta, Landi | | |
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| ==== Previous Geospatial Analytics websites ==== | ==== Previous Geospatial Analytics websites ==== |
| [[geospatialanalytics:gsa:gsa2022|]] | [[geospatialanalytics:gsa:gsa2023|]] |
| |
