Basic information
- Field of study
- Geospatial Computer Science
- Major
- Remote Sensing and GIS
- Organisational unit
- Faculty of Geo-Data Science, Geodesy, and Environmental Engineering
- Study level
- Second-cycle (engineer) programme
- Form of study
- Full-time studies
- Profile
- General academic
- Didactic cycle
- 2024/2025
- Course code
- DGEITGS.IIi2.07206.24
- Lecture languages
- English
- Mandatoriness
- Obligatory
- Block
- Major Modules
- Course related to scientific research
- Yes
|
Period
Semester 2
|
Method of verification of the learning outcomes
Completing the classes
Activities and hours
Lectures:
15
Project classes: 30 |
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 | has knowledge about Spatial Decision Support Systems and their application areas | GEI2A_W03 | Test |
| W2 | knows and understand the role of GIS in Multicriteria Decision Analysis and Spatial Decision Support Systems | GEI2A_W03 | Test |
| W3 | knows GIS technologies useful for SDSS and possibilities of their application for decision support | GEI2A_W03 | Test |
| W4 | has knowledge about sources of uncertainty in GIS analysis and uncertainty influence on GIS analysis outputs | GEI2A_W03 | Test |
| Skills – Student can: | |||
| U1 | can implement GIS analysis for multiattribute and multiobjective decision support | GEI2A_U01, GEI2A_U05 | Execution of exercises, Project |
| U2 | can evaluate the impact of different uncertainty sources on spatial analysis outputs | GEI2A_U01, GEI2A_U05, GEI2A_U07 | Execution of exercises, Project |
| Social competences – Student is ready to: | |||
| K1 | use the Spatial Decision Support Systems responsibly | GEI2A_K01 | Execution of a project |
Program content ensuring the achievement of the learning outcomes prescribed to the module
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 15 | |
| Project classes | 30 | |
| Preparation for classes | 8 | |
| Examination or final test/colloquium | 2 | |
| Contact hours | 2 | |
| Preparation of project, presentation, essay, report | 20 | |
| Student workload |
Hours
77
|
|
| Workload involving teacher |
Hours
45
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
Introduction to Multicriteria Decision Analysis and Spatial Decision Support Systems. The role of GIS: historical backround and recent progress. GIS in multiattribute and multiobjective decision analysis. Decision making under uncertainty: sources of uncertainty, modeling error and uncertainty in GIS, fuzzy methods, probabilistic methods, sensitivity analysis. GIS technologies in Spatial Decision Support: Desktop GIS, Geovisualization, WebGIS, Collaborative and Participatory GIS, MobileGIS, machine learning and deep learning techniques in Spatial Decision Support. |
W1, W2, W3, W4, K1 | Lectures |
| 2. |
Implementation of GIS for spatial decision support. Uncertainty assessment in GIS analysis. Application of machine learning and deep learning techniques to spatial analyses and decision support - demostration of examples and hands-on activities. |
U1, U2, K1 | Project classes |
Extended information/Additional elements
Teaching methods and techniques :
Project Based Learning, Problem Based Learning, E-learning, Group work, Discussion, Lectures, Demonstration
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Execution of a project, Test | |
| Project classes | Execution of exercises, Execution of a project, Project |
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
Credit of the project clases on the basis of attendance at classes and a positive evaluation of the project reports. In case of project classes a final grade is calculated as an average of individual project grades. A student who fails the project report evaluation may proceed to a resit twice.
Method of determining the final grade
Final grade = 0.7*project clsses grade + 0.3* final test grade Both, project and final test, has to be graded at least 3.0
Manner and mode of making up for the backlog caused by a student justified absence from classes
Compensating for the backlog caused by absence: depending on the classes subject – self-realisation of excercises with the help of individual consultations with the instructor.
Prerequisites and additional requirements
Prerequisities: basic knowledge of GIS.
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Lectures: students participate in the class by learning the subsequent teaching content according to the course syllabus. Students should ask questions and clarify doubts on an ongoing basis. Audio-visual recording of the lecture requires the consent of the instructor. Project classes: students perform practical work aimed at achieving the competencies assumed by the syllabus. The manner of execution of the project and the final result are evaluated.
Literature
Obligatory- Malczewski J., Rinner C.: Multicriteria Decision Analysis in Geographic Information Science. Springer 2015
- Sugumaran R., DeGroote J.: Spatial Decision Support Systems. Principles and Practices. CRC Press 2011.
Scientific research and publications
Publications- Pirowski T., Drzewiecki W., Orzińska E.: Simple method for incorporation of topographical factor into GIS-supported multi-variant rail route selection. W: SGEM 2014 : GeoConference on Informatics, geoinformatics and remote sensing: international multidisciplinary scientific geoconference : 17–26 June, 2014, Albena, Bulgaria : conference proceedings. Vol. 3, Photogrammetry and remote sensing cartography and GIS. — Sofia : STEF92 Technology Ltd., 841-851.
- Wojciech DRZEWIECKI, Sebastian Ziętara: Wpływ algorytmu określania dróg spływu powierzchniowego na wyniki oceny zagrożenia gleb erozją wodną w skali zlewni z zastosowaniem modelu RUSLE - The influence of flow routing algorithm on the results of RUSLE-based catchment-wide erosion risk assessment. Roczniki Geomatyki = Annals of Geomatics / Polskie Towarzystwo Informacji Przestrzennej, 2013 t. 11 z. 1, s. 57–68
- Wojciech Drzewiecki, Emilia Orzińska, Tomasz Pirowski:Analizy przestrzenne jako wsparcie projektowania przebiegu infrastrukturalnych obiektów liniowych — Spatial analyses environment as a supporting tool for infrastructural linear object routing. Roczniki Geomatyki = Annals of Geomatics /2012 t. 10 z. 4, s. 65–76
- Drzewiecki W., Hejmanowska B., Pirowski T.: Przykładowe analizy przestrzenne w oparciu o komputerowy atlas Województwa Krakowskiego KAWK, Archiwum Fotogrametrii, Kartografii i Teledetekcji, Vol.9. Polska, Olsztyn 1999;