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geospatialanalytics:gsa:start

783AA Geospatial Analytics A.A. 2024/25

Instructors:

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 C1
  • 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

News and communications

No lessons on October 10 and 11 (because of the evento “Orientamento studenti”)

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] About the course; [slides] Introduction to Geospatial Analytics [book chapter] Introduction to geographic information systems, Chapter 1; [paper] Human Mobility: Models and Applications, Section 1 Pappalardo, Nanni
2. 20.09 14:00-16:00 Fundamental Concepts (theory) [slides] Fundamental Concepts [book chapter] Introduction to geographic information systems, Chapter 2 (Coordinate Systems); [paper] A survey of deep learning for human mobility, Section 2.1, Appendix A; Essentials of Geographic Information Systems,Chapter 4, Section 4.2 (Vector Data Models); [video] Intro to coordinate systems and UTM projection Pappalardo
3. 26.09 14:00-16:00 Fundamental Concepts (practice) [code] Introduction to shapely, geopandas, folium, and scikit-mobility [book chapter] Automating GIS-processes, Lesson 1 (Shapely and geometric objects); [article] Analyze Geospatial Data in Python: GeoPandas and Shapely; [paper] scikit-mobility: a Python library for the Analysis, Generation, and Risk Assessment of Mobility Data, Sections 1, 2; Mauro
4. 27.09 14:00-16:00 Spatial Data Analysis I (theory) [slides] Spatial Data Analysis I [book chapter] Introduction to geographic information systems, Sect. 3.1, 3.3, 4.1-4.3, 4.7, 8.5, Chapter 11; [book chapter] Intro to GIS and Spatial Analysis, Chapter 11, 13; [book section] Encyclopedia of GIS: Geary’s C Nanni
5. 03.10 14:00-16:00 Spatial Data Analysis II (theory) [slides] Spatial Data Analysis II [book chapter] Introduction to geographic information systems, Chapter 15; [book chapter] Intro to GIS and Spatial Analysis, Chapter 14; [book section] Spatial data science for sustainable development, Tutorial 3 (Spatial Regression); [paper] Spatial co-location patterns, Sect. 3.1; [paper] Trend Detection in Spatial Databases , Sect. 4 Nanni
6. 04.10 14:00-16:00 Spatial Data Analysis II (practice) [code] Spatial Analysis exercises PySAL: Python Spatial Analysis Library; Scikit-learn KNeighborsRegressor; PyKrige Nanni
7. 17.10 14:00-16:00 Geographic and Mobility data

Previous Geospatial Analytics websites

geospatialanalytics/gsa/start.txt · Ultima modifica: 07/10/2024 alle 08:29 (9 giorni fa) da Mirco Nanni

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