Corsi di Laurea Corsi di Laurea Magistrale Corsi di Laurea Magistrale
a Ciclo Unico
Scuola di Agraria e Medicina Veterinaria
AVP5073817, A.A. 2019/20

Informazioni valide per gli studenti immatricolati nell'A.A. 2019/20

Principali informazioni sull'insegnamento
Corso di studio Corso di laurea magistrale in
AV2293, ordinamento 2016/17, A.A. 2019/20
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Crediti formativi 4.0
Tipo di valutazione Giudizio
Sito della struttura didattica
Dipartimento di riferimento Dipartimento Agronomia Animali Alimenti Risorse Naturali e Ambiente (DAFNAE)
Sito E-Learning
Obbligo di frequenza No
Lingua di erogazione INGLESE
Corso singolo È possibile iscriversi all'insegnamento come corso singolo
Corso a libera scelta Insegnamento riservato SOLO agli iscritti al corso di SUSTAINABLE AGRICULTURE - AGRICOLTURA SOSTENIBILE


Dettaglio crediti formativi
Tipologia Ambito Disciplinare Settore Scientifico-Disciplinare Crediti
ALTRO Altre conoscenze utili per l'inserimento nel mondo del lavoro -- 4.0

Organizzazione dell'insegnamento
Periodo di erogazione Primo semestre
Anno di corso I Anno
Modalità di erogazione frontale

Tipo ore Crediti Ore di
Ore Studio
LEZIONE 4.0 32 68.0

Inizio attività didattiche 30/09/2019
Fine attività didattiche 18/01/2020
Visualizza il calendario delle lezioni Lezioni 2019/20 Ord.2016

Prerequisiti: Knowledge of basic statistics is strongly encouraged (if you know how to calculate a mean, median, standard deviation and a quantile you are find,if you know how to correctly use a z-test and a t-test you are doing very well ); familiarity with basic computer software for data analysis (e.g. MS Excel) is encouraged but not strictly requested.
Conoscenze e abilita' da acquisire: The course will provide the following competences:
1. understanding raster and vector models, differences, advantages and disadvantages;
2. using an open source GIS (QGIS) for reading spatial data and applying thematic color scales;
3. combining raster and vector data for extracting information, such as zonal statistics and extracting data at points;
4. combining vector layers via geoprocessing tools;
5. combining raster layers to extract information;
6. interpolate point data via different methods, assessing accuracies via k-fold validation;
7. apply statistical methods to assess differences in data distribution extracted using GIS tools;
8. data mining through internet services, and OGC services (WMS/WFS/WCS)
9. extract transform and load (ETL) data from different formats;
10. join data spatially and via common columns;
11. create a project via scientific methods, formulating an hypothesis and testing it through GIS methods;
12. write a scientific report applying the IMRaD structure;

Technical skills - from 1 to 10
Soft skills - 11 and 12
Modalita' di esame: 20% on assignments given during the course.
80% evaluation of "lab-project" report.

The report for the lab-project is a 6-10 page report on an investigation using spatial data analysis using GIS. Objectives, data and methods are chosen freely by the candidate.
Criteri di valutazione: Ability of the candidate to solve problems and analyse spatial data using GIS tools. These abilities will be evaluated during the course and by examination of the lab-project report.
The candidate must successfully carry out the tasks required in his/her lab-project and must provide a well-written report, with a convincing research question, method and conclusions.
Contenuti: - spatial data definition, common models of digital representation of spatial data (vector, raster, TIN etc..)
- data source types (file-based, web-based, geodatabases, web services etc…);
- sources of spatial data (satellite images – Sentinels/Landsat, regional and national cartographic data – topographic geodatabases, global datasets – e.g. global forest cover etc…);
- visualizing data, color representations and production of thematic maps from attributes;
- analysis of raster and vector data using GIS tools over single or multiple layers (geospatial relations, raster calculations, interpolation etc...).
Attivita' di apprendimento previste e metodologie di insegnamento: Lectures will be theoretical and practical at the same time: i.e. “learn by doing” principle. Students will use the data and apply the taught methods using GIS tools provided in the lab.
Proactivity is requested on the lab-project work - students will have to propose their own ideas on put to practice the methods they learned over the chosen study area and on data that they find over the internet.
Open-lab hours will be used for open student-student and student-teacher interaction. Students will develop team-work skills (soft-skills) by working on their project, exposing ideas and affronting/giving costructive criticism.
Seminars will add information on the potential uses of GIS for spatial analysis.
Eventuali indicazioni sui materiali di studio: The material used for the course will be made available to students through the Moodle platform of the School at
Testi di riferimento:

Didattica innovativa: Strategie di insegnamento e apprendimento previste
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Problem solving

Didattica innovativa: Software o applicazioni utilizzati
  • Moodle (files, quiz, workshop, ...)

Obiettivi Agenda 2030 per lo sviluppo sostenibile
Agire per il clima La vita sulla Terra