The course will provide different competences: understanding raster and vector models, differences, advantages and disadvantages, using an open source GIS (QGIS) for reading spatial data. Raster, vectors and layers will be combined for extracting information via geoprocessing tools and interpolation procedures will be presented, as well as statistical methods to assess differences in data distribution extracted using GIS tools. Data mining through internet services, and OGC services (WMS/WFS/WCS) will be also explained.
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.
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 constructive criticism.
Seminars will add information on the potential uses of GIS for spatial analysis.
20% on assignments given during the course.
80% evaluation of “lab-project” report.
The report for the lab-project is a 6-10 pages report on an investigation using spatial data analysis using GIS. Objectives, data and methods are chosen freely by the candidate.
Type of assessment: 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.
Marking scale: 30-point grading scale
Re-exam: Re-examination identical to ordinary examination.