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Geospatial Analysis using Python and QGIS Training Course

Course Overview:

This is a 5-day course offered by IRES designed to introduce participants to Geospatial Analysis using Python and QGIS. The course will provide practical knowledge and hands-on experience in using Python for geospatial data analysis and automating geospatial tasks within the QGIS environment. Participants will learn how to manipulate spatial data, perform advanced analyses, and automate workflows to support decision-making processes across a variety of sectors such as urban planning, environmental monitoring, and disaster management.

Course Duration:

5 days

Personal Impact:

• Master the integration of Python scripting with QGIS for efficient geospatial data processing.
• Develop skills in automating common GIS tasks using Python.
• Gain hands-on experience with key Python libraries for spatial analysis (such as GeoPandas and Shapely).
• Learn to analyze, visualize, and manipulate spatial data with QGIS, enhancing your GIS workflows.

Organizational Impact:

• Improve the ability to automate geospatial data tasks, saving time and resources.
• Enhance decision-making processes through advanced spatial analysis.
• Provide advanced geospatial analysis capabilities within the organization using QGIS and Python.
• Empower teams with skills to manipulate and analyze spatial data for improved operational performance.

Course Objectives:

• To introduce the fundamentals of Python for geospatial analysis.
• To equip participants with the knowledge to use QGIS for spatial data visualization and manipulation.
• To teach participants how to integrate Python with QGIS to automate workflows and enhance data processing.
• To provide practical experience in geospatial analysis, including working with spatial data formats, performing spatial queries, and generating geospatial reports.
• To empower participants to apply their knowledge to real-world projects, including environmental monitoring and urban planning.

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Course Outline:

Module 1: Introduction to Geospatial Analysis with Python and QGIS

• Overview of geospatial data types and formats (e.g., shapefiles, GeoJSON, raster data)
• Introduction to QGIS interface and basic functionalities (layer handling, map rendering, etc.)
• Introduction to Python in GIS: key libraries (GeoPandas, Shapely, Fiona, Rasterio)
• Setting up the development environment: installing QGIS and Python libraries
• Case Study 1: Use of geospatial analysis in environmental monitoring (e.g., deforestation mapping)
• Hands-On Exercise 1: Loading and visualizing spatial data in QGIS.

Module 2: Python Scripting for Geospatial Data Manipulation

• Introduction to GeoPandas: manipulating vector data with Python
• Loading, reading, and writing spatial data using GeoPandas
• Spatial operations: buffering, merging, and intersecting geometries
• Working with spatial indexes for faster querying
• Case Study 2: Spatial analysis for land use planning and urban development
• Hands-On Exercise 2: Using GeoPandas to read and process shapefiles, perform basic spatial operations.

Module 3: Spatial Data Visualization and Analysis with Python and QGIS

• Visualization techniques: working with maps, styling layers, and adding attributes in QGIS
• Visualizing geospatial data with Matplotlib and Plotly
• Spatial analysis in QGIS: buffering, clipping, and overlay analysis
• Conducting proximity analysis and spatial queries using QGIS and Python
• Case Study 3: Using spatial analysis for disaster management (e.g., flood zone mapping)
• Hands-On Exercise 3: Visualizing and analyzing a sample geospatial dataset (buffer analysis, heatmap generation).

Module 4: Advanced Geospatial Analysis in QGIS with Python Integration

• Automating common spatial analysis tasks using Python scripts
• Working with raster data in QGIS: analysis using Rasterio and PyQGIS
• Performing advanced geospatial analyses (e.g., spatial joins, interpolation)
• Writing custom Python scripts to automate geospatial analysis workflows in QGIS
• Case Study 4: Environmental impact assessment using raster-based analysis
• Hands-On Exercise 4: Writing Python scripts for raster analysis, such as land cover classification or suitability modeling.

Module 5: Automating and Extending QGIS with Python

• Using QGIS Python console and PyQGIS for advanced automation
• Creating custom QGIS plugins using Python for repetitive spatial tasks
• Integrating external data sources (e.g., APIs, web services) into QGIS projects with Python
• Performance optimization: Efficient handling of large spatial datasets in QGIS and Python
• Case Study 5: Building a custom QGIS plugin for automated spatial report generation
• Hands-On Exercise 5: Developing a simple Python-based QGIS plugin to automate a common GIS task (e.g., buffer creation).