Basic Linear Geostatistics
Course description sheet
Basic information
- Field of study
- Geospatial Computer Science
- Major
- -
- Organisational unit
- Faculty of Geo-Data Science, Geodesy, and Environmental Engineering
- Study level
- First-cycle (engineer) programme
- Form of study
- Full-time studies
- Profile
- General academic
- Didactic cycle
- 2022/2023
- Course code
- DGEIS.Ii20.06399.22
- Lecture languages
- English
- Mandatoriness
- Elective
- Block
- Elective Modules in Foreign Language
- Course related to scientific research
- Yes
Lecturer
Marcin Ligas
|
Period
Semester 6
|
Method of verification of the learning outcomes
Completing the classes
Activities and hours
Lectures:
15
Project classes: 15 |
Number of ECTS credits
3
|
Course's learning outcomes
| Code | Outcomes in terms of | Learning outcomes prescribed to a field of study | Methods of verification |
| Knowledge – Student knows and understands: | |||
| W1 | definitions and concepts related to the fundamentals of theory of regionalized variables. | GEI1A_W05, GEI1A_W06 | Execution of a project, Test |
| W2 | fundamentals of geostatistical analysis and interpretation of results. | GEI1A_W05, GEI1A_W06 | Activity during classes, Test, Project |
| Skills – Student can: | |||
| U1 | propose a mathematical model representing a given spatial phenomenon. | GEI1A_U03, GEI1A_U04 | Activity during classes, Test, Project, Case study |
| U2 | perform geostatistical analysis on his own and to interpret and present the results | GEI1A_U03, GEI1A_U04, GEI1A_U10, GEI1A_U13 | Activity during classes, Execution of a project, Test, Project |
| U3 | geostatistical software for modeling and interpreting spatial phenomena | GEI1A_U03, GEI1A_U10 | Activity during classes, Execution of exercises, Execution of a project |
| Social competences – Student is ready to: | |||
| K1 | work in a team, use geostatistical methods to solve problems from other scientific disciplines and is aware of the importance of constant improvement of the language in terms of professional terminology | GEI1A_K01, GEI1A_K03 | Activity during classes, Participation in a discussion, Involvement in teamwork, Presentation |
Program content ensuring the achievement of the learning outcomes prescribed to the module
This course is intended to give a student a basic insight into specific probabilistic models and corresponding statistical methods for spatial data.
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 15 | |
| Project classes | 15 | |
| Preparation for classes | 11 | |
| Realization of independently performed tasks | 14 | |
| Examination or final test/colloquium | 2 | |
| Preparation of project, presentation, essay, report | 18 | |
| Student workload |
Hours
75
|
|
| Workload involving teacher |
Hours
30
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
Introductory stage: reminder of statistics (expected value, variance, correlation coefficient, linear regression), matrix algebra and systems of equations Computation of empirical semivariogram and covariance function in 1D case (e.g. for time series) and in 2D case (for spatial data), manual and least squares fitting of an authorized model, computation of practical range, interpretation of semivariogram parameters Exact prediction in standard formulation of kriging predictor. Filtered prediction. Differences between the two as to the predicted values and kriging variance. Geostatistical software: Geostatistical Analyst (ArcGIS extension), R - CRAN packages dedicated to kriging prediction |
U1, U2, U3, K1 | Project classes |
| 2. |
Introduction to Geostatistics, notion of a random field, Matheron's theory of regionalized variables, main fields of application. Stationarity assumptions (second order stationarity and intrinsic stationarity), covariance function and semivariogram, relation between semivariogram and covariance function for second order stationary spatial processes Estimation of empirical semivariogram, theoretical semivariogram models, semivariogram fitting, semivariogram parameters (nugget effect, partial sill, sill and range of autocorrelation) Spatial prediction and filtering by means of ordinary kriging Quality control of kriging parameters - crossvalidation |
W1, W2, U1 | Lectures |
Extended information/Additional elements
Teaching methods and techniques :
Case study, Discussion, Lectures
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Activity during classes, Execution of a project, Test, Project, Case study | |
| Project classes | Activity during classes, Participation in a discussion, Execution of exercises, Execution of a project, Test, Project, Case study, Involvement in teamwork, Presentation |
Conditions and the manner of completing each form of classes, including the rules of making retakes, as well as the conditions for admission to the exam
Attendance at lectures is not compulsory but it is strongly encouraged. If a student has received a failing grade and has not completed the course in a primary term, he or she may be reassessed twice. A make - up assessment will have a written form and will encompass the entire presented material. The lecturer sets proper terms and conditions of reassessment.
Method of determining the final grade
Student has to pass all tests (weight 0.6) and complete assignments (weight 0.4). The final grade will be the weighted average.
Manner and mode of making up for the backlog caused by a student justified absence from classes
The way and mode of catching up on project classes resulting from the student's absence will be determined individually.
Prerequisites and additional requirements
Basics of mathematics and statistics. Intermediate level of English is required. The knowledge of basic English terminology in statistics will be appreciated.
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Lectures: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego. Project classes: Studenci wykonują prace praktyczne mające na celu uzyskanie kompetencji zakładanych przez syllabus. Ocenie podlega sposób wykonania projektu oraz efekt końcowy.
Literature
Obligatory- Armstrong M., 1998, Basic Linear Geostatistics, Springer
- Isaaks E.H., Srivastava R.M., 1990, An Introduction to Applied Geostatistics, Oxford University Press
- Leuangthong, O., Khan, D., and Deutsch, C.V., 2008, Solved Problems in Geostatistics, Wiley Interscience
- Olea R.A., 2006, A six-step practical approach to semivariogram modeling, Stochastic Environmental Research and Risk Assessment, 20(5), 307-318
Scientific research and publications
Publications- Ligas M., Kulczycki M., (2010), Simple spatial prediction – least squares prediction, simple kriging, and conditional expectation of normal vector, Geodesy and Cartography, 59 (2), 69–81.
- Ligas M., Kulczycki M., (2017), Kriging and moving window kriging on a sphere in geometric (GNSS/levelling) geoid modeling, Survey Review, http://dx.doi.org/10.1080/00396265.2016.1247131.
- Lenda G., Ligas M., (2012), Application of splines supported by kriging for precise shape analysis of incompletely measured structures, Journal of Computing in Civil Engineering, 26 (2), 214–224.
- Ligas M., Kulczycki M., (2014), Kriging approach for local height transformations, Geodesy and Cartography, 63 (1), 25–37.
- Ligas M., Szombara S., (2018), Geostatistical prediction of a local geometric geoid – kriging and cokriging with the use of EGM2008 geopotential model, Studia Geophysica et Geodaetica, https://doi.org/10.1007/s11200-017-0713-7