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====== 783AA Geospatial Analytics A.A. 2024/25 ======

===Instructors:===
  * **Luca Pappalardo**
    * [[luca.pappalardo@isti.cnr.it]]
    * KDD Laboratory, ISTI-CNR, 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]]

===Tutors:===
  * **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   | 14:00 - 16:00  |  Room Fib L1  | 
| Friday  | 14:00 - 16:00  |  Room Fib C  | 

  * Beginning of lectures: 21 September 2023
  * End of lectures: 7 December 2023
  * Possible lessons recovered:	8–15 December 2023

__**The lectures will be only in presence 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: (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.

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
  * Privacy issues in mobility data
  * Individual and collective mobility laws
  * Next-location and flow prediction
  * Trajectory and flow generation
  * Applications

===== Module 1: Spatial and Mobility Data Analysis =====

  * Fundamentals of Geographical Information Systems
    * Geographic coordinates systems
    * Vector data model
    * Trajectories
    * Spatial Tessellations
    * Flows
    * **Practice**: Python packages for geospatial analysis (Shapely, GeoPandas, folium, scikit-mobility)
  * Digital spatial and mobility data 
    * Mobile Phone Data 
    * GPS data 
    * Social media data 
    * Other data (POIs, Road Networks, etc.)
    * **Practice**: reading and exploring spatial and mobility datasets in Python
  * Preprocessing mobility data 
    * filtering compression 
    * stop detection 
    * trajectory segmentation 
    * trajectory similarity and clustering
    * **Practice**: data preprocessing with scikit-mobility

===== Module 2: Mobility Patterns and Laws =====

  * individual mobility laws/patterns
  * collective mobility laws/patterns
  * Practice: analyze mobility data with Python

===== Module 3: Predictive and Generative Models =====

  * Prediction
    * Next-location prediction
    * Crowd flow prediction
    * Spatial interpolation
  * Generation
    * Trajectory generation
    * Flow generation
  * Practice: mobility prediction and generation in Python

===== Module 4: Applications =====

  * Urban segregation models
  * Routing and navigation apps
  * Traffic simulation with SUMO



====== Calendar ======

^ ^ 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, 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 |


==== Previous Geospatial Analytics websites ====
[[geospatialanalytics:gsa:gsa2023|]]