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Lecturer(s)
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Course content
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Content of lectures: * Free/open source GIS - license, copyright, distribution, usage * Coordinate system - definition, category, catalogs. GDAL/OGR, proj4 library * Spatial data topology (validation, correctness), edit and vectorization of the data. * Analytical functions within free GIS SW, extension (plugins). * Spatial analysis of the vector data (its preprocessing and basic analyzing) with emphasize on point data. * Creation and definition of the digital elevation models (DEM). Derived attributes and spatial characteristics of DEM. * Spatial databases. Enterprise databases and spatial data. Metada in GIS. * QGIS, SAGA GIS, GRASS GIS - introduction and its usage for spatial data analysis. * Web services in free desktop GIS tools. Content of practicals: * Free/open source GIS tools (software) - introduction, installation, strong and weak points of usage. Overview of the analytical function, practical examples with spatial data. Integration of the programming language with GIS environment (e.g. python). * GDAL/OGR and GMT - spatial data transformation, data extraction and visualization. * QGIS - introduction to the QGIS GUI and basic manipulation. Setup environment for data editing and vectorization. Freely available data for GIS. Data preprocessing before spatial analysis in GIS. * QGIS - overlay analysis and spatial structure of the objects evaluation (spatial matrices) based on CORINE project data. * QGIS - distance analysis, point analysis, interpolation methods and "home range" analysis. * QGIS - cartography outputs, map layout, maps for the web environment. * SAGA (QGIS) - digital terrain modelling, DEM analysis, watershed delineation and basic hydrologic analysis. * SAGA (QGIS) - raster datasets analysis, point patterns analysis. * SAGA/QGIS/PostGIS - properties of the spatial data stored in spatial databases. Spatial functions in spatial databases. * GRASS GIS - raster manipulation, raster calculation and selected raster function for spatial analysis, spatial characteristics calculations (e.g. viewshed, solar radiation index). * gvSIG, openJUMP - web services used by GIS desktop, analytical function via web services and map layouts.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Work with multi-media resources (texts, internet, IT technologies), Individual tutoring
- Class attendance
- 56 hours per semester
- Semestral paper
- 40 hours per semester
- Preparation for exam
- 14 hours per semester
- Preparation for classes
- 6 hours per semester
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Learning outcomes
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The aim of the course is to extend the analytical skills of students in the processing of environmental spatial data. The topics within course are not focusing on theory of spatial ecology but mostly on practical tasks of data processing in free open source GIS software. An important aspect of the course is to selection of the software which is freely available and/or have open source code. The course uses knowledge of ecology and landscape ecology (individual tasks are within domain of ecology/landscape ecology). After completing the course, the student will be able to process spatial data in open source GIS and gain a basic overview of the possibilities in the current free GIS tools.
The student will acquire basic knowledge about processing of spatial data analysis with the help of free open source tools. The student will learn how to access the most common operations on raster and vector data. He will understand the principles of commonly used GIS functions based on appropriate combination of freely available GIS tools. Students will learn how to process, evaluate, create and present spatial data in QGIS and SAGA (shortly with GIS programs - Grass GIS, GDAL/OGR tools and GMT software).
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Prerequisites
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The course follows very freely the courses GIS I. (KBE / 548) and GIS II. (KBE/559), but it is a fully independent course in terms of practical GIS skills. The course aims more on practical spatial data analysis and how to select the appropriate tool according to the purpose of the analysis.
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Assessment methods and criteria
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Student performance assessment, Combined exam, Interim evaluation
Continuous elaboration of the practical assignment (individual exercises) on topics given by the lecturer. Practical exercises at PC are part of the exam.
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Recommended literature
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Neteler, M, Mitášová, H. (2010): Open Source GIS: A GRASS GIS Approach. Springer. 406 pp..
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Obe, R., Hsu L., Ramsey P. (2011): PostGIS in action. Manning. 520 pp..
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Sherman, G., Mitchel, T. (2012): The Geospatial Desktop. Locate Press, 384 pp..
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Web pro jednotlivé GIS projekty: QGIS (www.qgis.org) , GRASS (grass.osgeo.org), GDAL (www.gdal.org), PostGIS (postgis.net), GMT (gmt.soest.hawaii.edu), openJUMP (www.openjump.org), gvSIG (www.gvsig.org), SAGA (www.saga-gis.org).
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Westra, E. (2010): Python Geospatial Development. Packt Publishing. 508pp..
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