Questa è una vecchia versione del documento!
Indice
<html> <!– Google Analytics –> <script type=“text/javascript” charset=“utf-8”> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-34685760-1', 'auto', 'personalTracker', {'allowLinker': true}); ga('personalTracker.require', 'linker'); ga('personalTracker.linker:autoLink', ['pages.di.unipi.it', 'enforce.di.unipi.it', 'didawiki.di.unipi.it'] );
ga('personalTracker.require', 'displayfeatures'); ga('personalTracker.send', 'pageview', 'courses/gsa/'); setTimeout(“ga('send','event','adjusted bounce rate','30 seconds')”,30000);
</script> <!– End Google Analytics –> <!– Global site tag (gtag.js) - Google Analytics –> <script async src=“https://www.googletagmanager.com/gtag/js?id=G-LPWY0VLB5W”></script> <script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-LPWY0VLB5W');
</script> <!– Capture clicks –> <script> jQuery(document).ready(function(){
jQuery('a[href$=".pdf"]').click(function() {
var fname = this.href.split('/').pop();
ga('personalTracker.send', 'event', 'GSA', 'PDFs', fname);
});
jQuery('a[href$=".r"]').click(function() {
var fname = this.href.split('/').pop();
ga('personalTracker.send', 'event', 'GSA', 'Rs', fname);
});
jQuery('a[href$=".zip"]').click(function() {
var fname = this.href.split('/').pop();
ga('personalTracker.send', 'event', 'GSA', 'ZIPs', fname);
});
jQuery('a[href$=".mp4"]').click(function() {
var fname = this.href.split('/').pop();
ga('personalTracker.send', 'event', 'GSA', 'Videos', fname);
});
}); </script> </html>
783AA Geospatial Analytics A.A. 2023/24
Instructors:
- Luca Pappalardo
- KDD Laboratory, ISTI-CNR, Pisa
- Mirco Nanni
- KDD Laboratory, ISTI-CNR, Pisa
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 | 09:00 - 11:00 | Room Fib M1 |
| Friday | 09:00 - 11:00 | Room Fib M1 |
- 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
- List of available projects
- Projects Bidding form. Please express your preference for projects by Sunday, December 3rd, 2023. The project will be assigned on Monday, December 4th, 2023.
- 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
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
- 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