The course is aimed at the acquisition of the principles of analysis of regionalized variables addressed to a geological and paleoclimatic context. The course also aims at mastering the main numerical analysis tools and provides numerous practical examples with the help of a standard programming language for data analysis. The numerical analysis principles in this course have potential applications in many other fields and are an important tool for quantitative analysis.

The course will cover the following topics from both a theoretical and practical standpoint, also using computer assistance:

1. Introduction to the course

   1.1. Regionalized variables

   1.2. Tools for analysis

2. Geostatistical model

   2.1. Semivariogram

   2.2. Parametric models of semivariogram

   2.3. Best-fit of parametric models, least squares method

   2.4. Anisotropy

3. Kriging

   3.1. Ordinary Kriging, theory

   3.2. Ordinary Kriging, predictor and variance

   3.3. Universal Kriging, theory

   3.4. Universal Kriging, predictor and variance

4. Regional trend

   4.1. Linear detrending

   4.2. Advanced techniques

None

Knowledge and understanding. The student must demonstrate the basic knowledge of the types of numerical analysis covered in the course program. Specifically, they should be able to describe the basic principles of geostatistical analysis and their application. They must demonstrate the understanding of various data analysis techniques and their application to real cases. These skills will be verified through oral questions.

Applied knowledge and understanding. The student must show an understanding of the concepts and theories covered in the course; be able to independently analyze complex data series by choosing the appropriate techniques for specific cases; be able to use data analysis tools; and be able to evaluate the significance of the results. These skills will be assessed through a written exam.

Autonomy of judgment. The student must demonstrate the ability to use knowledge and concepts that allow reasoning according to the specific logic of the discipline. In particular, they must be able to identify appropriate analysis methods for various contexts and propose analysis hypotheses in non-trivial cases.

Communication skills. The student must demonstrate the ability to communicate their knowledge, ideas, and any issues related to the discipline clearly and using appropriate language.

Learning skills. The student must be able to build their own path of scientific growth critically and independently and be able to correctly use the study materials provided by the teacher as well as supplementary materials they can find themselves. These skills, as much as possible, will be stimulated by the teacher through proposing in-depth studies and providing exercises that will be explained and discussed during lessons or practice sessions.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Teaching

Lectures and computer-based exercises.

Independent data analysis projects.

Innovative teaching methods

Flipped classroom (classe capovolta)

Attendance

- Knowlwdge of vector and matrix algebra.

- Basic programming knowledge.

Assessment

Completion of a simulation project, agreed upon with the teacher.

The project must be completed and submitted within a maximum of two weeks. The evaluation criteria are the number of requirements actually implemented; the quality of the implementation; the correctness of the validity analysis of the results.

A subsequent oral exam, which starts with the discussion of the project but is aimed at verifying competence in other aspects of the program.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

Self study of teaching material

Attendance

- Knowlwdge of vector and matrix algebra.

- Basic programming knowledge.

Assessment

Completion of a simulation project, agreed upon with the teacher.

The project must be completed and submitted within a maximum of two weeks. The evaluation criteria are the number of requirements actually implemented; the quality of the implementation; the correctness of the validity analysis of the results.

A subsequent oral exam, which starts with the discussion of the project but is aimed at verifying competence in other aspects of the program.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.