| Course ID: | IGI-SE>GISI |
| Course title: | SPATIAL INFORMATION SYSTEMS I |
| Semester: | 3 / Winter |
| ECTS: | 6 |
| Lectures/Classes: | 30 / 30 hours |
| Field of study: | Geodesy and Cartography |
| Study cycle: | 1st cycle |
| Type of course: | compulsory |
| Prerequisites: | Computer basics |
| Contact person: | dr inż. Adam Michalski; adam.michalski@upwr.edu.pl |
| Short description: | During the lectures, students learn the basic knowledge in the field of collection, analysis and visualization of spatial data. During the classes students exercise mentioned above tasks in selected GIS software. |
| Full description: | Spatial data models - vector and raster. Database. SQL. Applications of GIS. Data formats used in GIS (GML et al.). UML class diagram. Standards, the legal basis for GIS. Basic information about spatial data infrastructure. Polish resources of spatial data in digital form. Digital terrain model. Spatial data interpolation methods. Tools for data analysis in the model vector. Tools for data analysis in raster model. |
| Bibliography: | . Longley P., Goodchild M., Maguire D., Rhind D.: GIS. Teoria i praktyka, PWN, Warszawa, 2007; 2. Praca zbiorowa, Geomatyka w Lasach Państwowych cz I (2010) oraz cz II (2013), Centrum Informacyjne Lasów Państwowych 3. Gotlib D., Iwaniak A., Olszewski R.: GIS. Obszary zastosowań, PWN, Warszawa, 2007; 4. Bielecka E., Maj K.: Systemy informacji przestrzennej. Podstawy teoretyczne, Wydawnictwo WAT, Warszawa 2009. 5. Urbański J.: GIS w badaniach przyrodniczych, Wydawnictwo Uniwersytetu Gdanskiego, 2008. 6. YouTube – Geospatial revolution |
| Learning outcomes: | Knowledge The student knows the basic concepts of spatial data and their representation in vector and raster model, knows the basics of databases; he is able to list and briefly describe the commonly available digital spatial data resources in Poland, he is able to describe the basic tools for the analysis of spatial data. Skills Student knows the basics of the selected GIS software; he can register raster map, he knows how to create a feature class, he can create and manipulate features on the map, he can join attribute data to features on the map, he performs a simple analysis on spatial data. Social competences The student is aware of his responsibility for collaborative tasks in a team. |
| Assessment methods and assessment criteria: | classes (50%) + completion group project + grade obtained at lectures (50%) |