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--- geospatialanalytics:gsa:start [30/11/2023 alle 10:58 (20 mesi fa)] – [News and communications] Luca Pappalardo
+++ geospatialanalytics:gsa:start [26/06/2025 alle 09:30 (2 settimane fa)] (versione attuale) – [News and communications] Luca Pappalardo
@@ Linea 1: / Linea 1: @@
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+====== 783AA Geospatial Analytics A.A. 2024/25 ======
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-====== 783AA Geospatial Analytics A.A. 2023/24 ======
 ===Instructors:===
@@ Linea 64: / Linea 15: @@
   * **Giuliano Cornacchia**, PhD student, University of Pisa
   * **Giovanni Mauro**, PhD student, University of Pisa
-  * **Daniele Gambetta**, PhD student, University of Pisa
 ===== Hours and Rooms =====
 ^  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
@@ Linea 76: / Linea 27: @@
 __**The lectures will be only in presence and will NOT be live-streamed**__
 ====== 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.
+** Exams sessions**:
-  * **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 fourth session of exams (i.e., fourth "appello") will be on __June 30th, 09:30, 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 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
+  * the third session of exams (i.e., third "appello") will be on __June 4th, 09:30, 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.
-    * Both issues above will be soon fixed in the library.
+  * 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]].
+**__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.
+__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 ======
@@ Linea 89: / Linea 56: @@
 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: (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.
 Topics:
@@ Linea 105: / Linea 72: @@
   * Applications
-===== Module 1: Spatial and Mobility Data =====
+===== Module 1: Spatial and Mobility Data Analysis =====
   * Fundamentals of Geographical Information Systems
@@ Linea 155: / Linea 122: @@
 ^ ^ 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 |
-|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  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 |
-|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 |
+|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 |
-|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 |
+|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 |
-|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 |
+|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 |
-|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 |
+|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 |
-|8. |13.10  09:00-11:00| NO LESSON, for atheneum ordinance  |  | |  |
+|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|
-|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 |
+|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. |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 |
+|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|
-|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 |
+|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|
-|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 |
+|12. | 08.11 14:00-16:00| Individual Mobility Patterns (practice) | {{ :geospatialanalytics:gsa:practice_individual_measures.zip | Practice session on individual measures}} | | 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 |
+|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|
-|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 |
+|14. | 15.11 14:00-16:00| Individual and Collective Mobility models (practice) | {{ :geospatialanalytics:gsa:gravity.zip | Practice session on the Gravity model}}| | Mauro |
-|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 |
+|15. | 28.11 14:00:16:00| Guest lecture | | | [[ https://www.riccardodiclemente.com/| Riccardo Di Clemente]]|
-|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 |
+| | 29.11 14:00:16:00| Cancelled for strike  |  | | |
-|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
+|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|
-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 |
+|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. |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 |
+|18. | ??.12 ??:??:?? | Mobility pattern mining and next-location prediction (practice) | | | Cornacchia |
-|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 |
 ==== Previous Geospatial Analytics websites ====
-[[geospatialanalytics:gsa:gsa2022|]]
+  * [[geospatialanalytics:gsa:gsa2023|]]
+  * [[geospatialanalytics:gsa:gsa2022|]]