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Course, academic year 2022/2023
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Environmental Informatics - MO550P83E
Title: Environmental Informatics
Guaranteed by: Institute for Environmental Studies (31-550)
Faculty: Faculty of Science
Actual: from 2020
Semester: summer
E-Credits: 3
Examination process: summer s.:
Hours per week, examination: summer s.:0/2, MC [HT]
Capacity: 50
Min. number of students: unlimited
For 4EU+ students: no
Virtual mobility / capacity: no
State of the course: taught
Language: English
Additional information:
Note: enabled for web enrollment
Guarantor: Ing. Luboš Matějíček, Ph.D.
Teacher(s): Ing. Luboš Matějíček, Ph.D.
Annotation -
Last update: Ing. Luboš Matějíček, Ph.D. (27.10.2019)
The lecture is primarily focused on computer processing of spatially related information. For the students of natural science, the lecture sums up the introductory course that is extended by other optional lectures, exercises and seminars. Presentations of individual aspects of data processing are based on the ESRI (Environmental Systems Research Institute) guidebooks. The lecture contains computer presentations of selected exercises, which are consecutively processed by students in the framework of exercises.
Literature -
Last update: Ing. Luboš Matějíček, Ph.D. (27.10.2019)

ArcGIS Pro/ArcGIS 10: Using ArcCatalog, Using ArcMap, Using ArcGIS Spatial Analyst, Geoprocessing in ArcGIS, Using ArcGIS 3D Analyst, Using ArcGIS Geostatistical Analyst, ArcGIS Network Analyst Tutorial, Using ArcScan for ArcGIS, Using Maplex for ArcGIS, Using ArcReader, Using ArcGIS Publisher.

Crosier, S., Booth, B., Dalton, K., Mitchell, A., Clark, K. ArcGIS 9. Starting with ArcGIS. ESRI, Redlands, 2004.

Mitchell, A. The ESRI Guide to GIS Analysis. ESRI, Redlands, 1999.

Zeiler, M. Modeling Our World. The ESRI Guide to Geodatabase Design. ESRI, Redlands, 1999.

Goodchild, M. GIS and Environmental Modeling: Progress and Research Issues. John Wiley&Sons, New York, 1996.

Aronoff, S. Remote Sensing for GIS Managers. ESRI, Redlands, 2005.

Cooke, D. Fun with GPS. ESRI, Redlands, 2005.

Requirements to the exam -
Last update: Ing. Luboš Matějíček, Ph.D. (08.02.2023)

In order to receive credit, it is required to work out tasks according to the instructions for the exercise, see further instructions in MOODLE.

Note: The exercise can be completed remotely using MOODLE upon agreement with the teacher.

Syllabus -
Last update: Ing. Luboš Matějíček, Ph.D. (27.10.2019)

1. Basic data formats focused on raster data, vector data, irregular triangulated networks and attributes of spatial objects in relation to living environment.
2. Basic procedures for data acquisition, processing and display of spatially related data, creation of thematic maps, visualization methods of spatial objects.
3. Processing of raster data, calculation focused on terrain morphology, using of raster algebra, spatial optimization tasks.
4. Processing of vector data, spatial interactions, using of set operations, selection procedures and interpretation of physical-chemical variables.
5. Processing of triangulated irregular networks and their using in the framework of digital terrain model construction, display of physical-chemical variables.
6. Interpolation of spatial related variables with deterministic techniques (IDW, spline) and geostatistical methods (kriging), integration in the framework of thematic maps.
7. Construction of network models, route optimization, optimization of spatial object accessibility.
8. Basic methods of digital spatial data acquisition focused on digitization of paper maps, aerial and satellite images, development of spatial database, sharing data through the Internet.
9. Advanced methods of spatial data processing focused on display with thematic maps and 3D visualization.
10. Presentation of projects through the Internet, display methods based on spatial search functions.
11. Introduction to GPS, estimation of the positional accuracy, basic methods for data processing and data integration, sharing of physical-chemical variables
12. Introduction to remote sensing, an overview of available data sources, basic methods of image processing and image classifications.
13. Using of CAD for spatial data processing and visualization, examples of spatial models of urban areas with visualization of data focused on living environment.
14. Examples of the projects focused on processing data about living environment.

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