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Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Educational activities
CSimilar or integrative activities
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2018/2019
School School of Civil
Environmental and Land Management Engineering
School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (495) Geoinformatics Engineering
Track GEC - Geoinformatics Engineering - CS
Programme Year 1

Course Details
ID Code 053799
Course Title GEOSPATIAL DATA ANALYSIS [I.C.]
Course Type Integrated Course
Credits (CFU / ECTS) 10.0
Semester First Semester
Course Description The course deals with a variety of predicting techniques applied to environmental variables expressed as functions of time, of space or both (signals, fields or time varying fields). Applications can be found in a large number of domains, hydrology, geophysics, geodesy, oceanography, just to mention those close to the environmental engineering. The predicting techniques are mainly based on the idea that close in time and space values of the phenomena under study are similar, or, in other words, have a smooth behavior; this idea is expressed in different forms also according to the kind of modeling: deterministic or stochastic. The proposed course aims at giving the mathematical tools needed to perform the data analysis by selecting and organizing the topics according to a possible realistic processing flow: the pre-processing phase techniques, a first processing phase consisting in a deterministic de-trending and a final processing refinement by a stochastic analysis of the residuals. In more detail, pre-processing includes outlier detection and removal, clustering and gridding. Deterministic processing includes least squares interpolation with linear combination of known functions (Spline interpolation and Discrete Fourier Transform are specifically described): hybrid norm or Tychonov interpolation is described in this context. Finally, the stochastic modeling of the residuals is introduced with the concepts of stationary signals and homogeneous and isotropic random fields, empirical variogram and covariance function estimation and the linear prediction with kriging techniques. The course is complemented by a number of laboratory sessions, using R and Matlab software suites, devoted to the implementation of the studied techniques.
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
C
ICAR/06
SURVEYING AND MAPPING
10.0

Schedule, add and removeAlphabetical groupCodeModule DescriptionProfessorCFUSem.LanguageCourse details
From (included)To (excluded)
--AZZZZ053798GEOSPATIAL DATA ANALYSIS [MOD. B]Venuti Giovanna5.01
053797GEOSPATIAL DATA ANALYSIS [MOD. A]Mussio Luigi5.01
manifesti v. 3.4.7 / 3.4.7
Area Servizi ICT
17/01/2021