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Mathematical Statistics in Geoinformatics

Course developers

Course abstract

Students learn to understand the basic concepts of mathematical statistics and its practical application in the analysis of geospatial information. The course teaches mathematical statistical methods that can be used in GIS analysis and geospatial data research. The course develops skills to use statistical methods in the analysis of geospatial information problems.

Learning outcomes and their assessment

After the study course students will have: knowledge and understanding of the classification and basic principles of the choice of mathematical statistical methods according to tasks of the research (laboratory works); skills to apply mathematical statistical methods for course projects and diploma, as well as professional research tasks (laboratory works, tests); competence to analyse, systematize data processing results and use them for evaluation of GIS data (independent work).

Course Content(Calendar)

Full-time studies:
1. The role of statistics in geoinformatics. Application of mathematical methods in geospatial data processing. 2h
2. Random variables and probability distributions. 2h
3. Random sampling and data description. 2h
4. Types of descriptive statistics. 2h
5. Evaluation of population parameters. Statistical hypotheses. Classification of statistical tests. 2h
6. Test Nr.1. 2h
7. Normal distribution law. 2h
8. t-test for dependent samples. 2h
9. t-test for independent samples and Fisher's F test. 2h
10. Test Nr.2. 2h
11. Analysis of correlation. 2h
12. Simple linear regression analysis. 2h
13. Basics of nonlinear regression. 2h
14. χ2 as a statistical independence test. 2h
15. Non-parametric statistical methods.2h
16. Test Nr.3. h
Part-time studies:
All topics specified for full-time studies are implemented, but the number of contact hours is 1/2 of the specified number of hours

Requirements for awarding credit points

3 tests during the course;
submission of independent work results.

Description of the organization and tasks of students’ independent work

During the semester the student has to perform independent work. The work must be written and submitted electronically on the e-learning site.

Criteria for Evaluating Learning Outcomes

Cumulative course grade will be determined during the semester by the relative weights given:
independent work – 10%;
test Nr. 1 – 30%;
test Nr. 2 – 30%;
test Nr. 3 – 30%.

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā un biznesā. Rīga: Datorzinību Centrs, 2006. 362 lpp.
2. Smotrovs J. Varbūtību teorija un matemātiskā statistika. Rīga: Zvaigzne ABC, 2007. 136 lpp.
3. Grīnglazs L., Kopitovs J. Matemātiskā statistika: ar datoru lietojuma paraugiem uzdevumu risināšanai. Rīga: Rīgas Starptautiskās ekonomikas un biznesa administrācijas augstskola, 2003. 310 lpp.
4. Chun Y., Griffith D. A. Spatial Statistics and Geostatistics. SAGE, 2013. 200 p.
5. Fischer M. M., Getis A. Handbook of Applied Spatial Analysis. Springer, 2010. 352 p.

Further reading

1. Krastiņš O. Ciemiņa I. Statistika. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp.
2. Krastiņš O. Statistika un ekonometrija. Rīga: LR Centrālā statistikas pārvalde, 1998. 435 lpp.
3. Dale P. Mathematical Techniques in GIS. CRC Press, 2014. 359 p.
4. Schabenberger, O., Gotway, C. Statistical Methods for Spatial Data Analysis. Boca Raton [etc.]:Chapman &Hall/CRC, 2005. 512 p.

Periodicals and other sources

1. Centrālā statistikas pārvalde. Pieejams: https://www.csb.gov.lv
2. Official Statistics Portal [online] Available: https://stat.gov.lv/en
3. Eurostat [online] Available: https://ec.europa.eu/eurostat/web/main
4. FAOSTAT [online] Available: https://www.fao.org/faostat/en/#home

Notes

Full-time and non full-time studies of the academic higher education bachelor's study program “Geoinformatics and Remote Sensing”