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COURSE OUTLINE

Course Presentation

  1. Getting to know each other: trainers and student's background
  2. Identifying student's needs
  3. Course objectives and schedule

The Open Source tools

  1. Linux environment
  2. Why and what to use
  3. Introducing the Ubuntu virtual machine


Lectures

  1. Basic modeling concepts and procedures in ecology
  2. Introduction to Spatial ecological modelling
  3. Models examples: nonparametric algorithms, ensemble classifiers, machine learning techniques
  4. Forest habitat suitability modelling
  5. Forest fire danger forecast model


UNIX/LINUX Bash programming

  1. Why to use Unix/Linux command line
  2. The basic commands
  3. Command syntax
  4. File management
  5. Read and explore a text file
  6. Metacharacters and their use
  7. Concatenate process
  8. The use of variable
  9. The for loop
  10. Case study and exercises


AWK Programming language

  1. Why to use AWK command line
  2. The basic commands, command syntax
  3. Built-in variables
  4. Import variables
  5. String functions
  6. Numerical functions
  7. Query functions
  8. Manipulating large files before importing in R
  9. Case study and exercises


GNUPLOT

  1. Why to use GNUPLOT command line
  2. Commands syntax
  3. Plotting data
  4. Using AWK language inside GNUPLOT
  5. Case study and exercises


Geotools

  1. Introduction to GDAL/OGR, Pk-tools, Openforis
  2. Command syntax
  3. Raster data manipulation
  4. Built up specific geotools
  5. Case study and exercises


R environment for statistical analysis and graphics

  1. Introduction to R environment
  2. R structure, libraries, scripting and getting help
  3. Command syntax, R objects
  4. Basic commands (input, output, data creation)
  5. Data manipulation
  6. Plotting data and graphical parameters
  7. Programming (functions, if and ifelse condition, for loop, while, system variable)
  8. Spatial and ecological modelling libraries
  9. Basic statistics
  10. A linear model and stepwise regression
  11. More complex algorithms (Classification and Regression Tree / Random Forest)
  12. Importing geodata
  13. Model prediction
  14. Exporting the data
  15. Case study and exercises


Quantum GIS

  1. Introduction to Quantum gis
  2. Import and export data
  3. Data visualization and map creation
  4. Qgis as Gui to GRASS and as a learning tool


GRASS Geographical Resources Analysis Support System

  1. Introduction to GRASS
  2. Data structure in GRASS
  3. Command syntax and general commands of data handling
  4. Raster and vector data import, export, display and conversion
  5. Raster map calculator
  6. Vector manipulation and processing
  7. GRASS / Qgis interface
  8. Scripting in GRASS: combine BASH commands with GRASS commands
  9. Environment variables and their importance for shell scripting
  10. Case study and exercises


wiki/content_of_the_course.txt · Last modified: 2017/12/05 22:53 (external edit)