First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Agricultural Sciences and Veterinary Medicine
Course unit
AVP4060697, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
AV2091, Degree course structure A.Y. 2017/18, A.Y. 2019/20
bring this page
with you
Degree course track Common track
Number of ECTS credits allocated 2.0
Type of assessment Evaluation
Course unit English denomination INTRODUCTION TO GIS
Website of the academic structure
Department of reference Department of Land, Environment, Agriculture and Forestry
E-Learning website
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit CANNOT be attended under the option Single Course unit attendance
Optional Course unit The Course unit is available ONLY for students enrolled in FOREST SCIENCE - SCIENZE FORESTALI

Teacher in charge FRANCESCO PIROTTI ICAR/06

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Other -- -- 2.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 2.0 16 34.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Board From To Members of the board
6 Commissione a.a. 2019/20 01/12/2019 30/11/2020 PIROTTI FRANCESCO (Presidente)
VETTORE ANTONIO (Membro Effettivo)
5 Commissione a.a. 2018/19 01/12/2018 30/11/2019 PIROTTI FRANCESCO (Presidente)
VETTORE ANTONIO (Membro Effettivo)

Prerequisites: Knowledge of basic statistics is strongly encouraged (if you know how to correctly use a z-test and a t-test you are ok) and familiarity with basic computer software for data analysis (e.g. MS Excel) is encouraged but not strictly requested.
Target skills and knowledge: 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
Examination methods: 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.
Assessment criteria: 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.
Course unit contents: Students will learn the models and formats of digital representation of spatial data, the structure of a geographic information system (GIS); they will use an open source GIS software package (QGIS+SAGA+GRASS) for visual representation of spatial data and analysis of raster and vector data.
The students will acquire knowledge on using GIS tools to interpret spatial data and process multiple layers with environmental variables to extract information, assess and predict dynamics related to the environment.

- 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 (raster data (digital terrain models), 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 calculation, interpolation etc...).
- interpolate vector data to raster data and evaluate accuracy through k-fold validation
- use multi-criteria evaluation with spatial data to model and predict environmental factors and correlated factors;
Planned learning activities and teaching methods: 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.
Additional notes about suggested reading: The material used for the course will be made available to students through the Moodle platform of the School at
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Climate Action Life on Land