GIS Mapping, Visualization & Analysis Using QGIS Course

Course Overview

Effective management and visualization of data are critical for gaining a competitive edge in any organization. Spatial data, in particular, can transform raw information into actionable insights, enabling quicker decision-making and efficient program implementation. Geographic Information Systems (GIS) and Remote Sensing (RS) offer powerful tools to visualize, analyze, and interpret spatial data across various fields such as agriculture, business, forestry, crime, mining, health, and transport. This comprehensive course is designed to equip participants with practical skills in using Quantum GIS (QGIS) to manage, visualize, and analyze spatial data. Through hands-on exercises, participants will learn to implement GIS projects, master GIS and RS principles, acquire and manipulate spatial data, and design and publish maps. By the end of the course, attendees will be proficient in utilizing GIS as a decision-making tool, enhancing their ability to deliver accurate and timely information.

Target Audience

This course targets:

• Data Editors
• GIS Database Designers
• GIS Managers
• GIS Analysts
• Map Designers
• GIS Technical Leads
• GIS Desktop Application Developers

Are you in one of these roles and looking to enhance your GIS skills? This course is perfect for you!

Course Objectives

By the end of this course, participants should be able to:

• Develop and implement a GIS project.
• Master the principles and concepts of GIS and Remote Sensing.
• Acquire, manipulate, and save data sources in a GIS environment.
• Understand how GIS databases work and how to update them.
• Edit and manage GIS data effectively.
• Design, symbolize, and publish maps.
• Output and disseminate results of a GIS project.
• Explore available software and data sources in the current GIS market.
• Learn digital image processing techniques.
• Utilize specific GIS functionalities for spatial analysis.
• Use the internet for publishing maps.

Course Outline

Module 1: Introduction to GPS, GIS, and Remote Sensing Principles

• Principles of GIS and Remote Sensing: Understand the core concepts.
• Components of GIS Systems: Learn about the key elements.
• GIS Capabilities and Functions: Explore what GIS can do.
• Spatial Data Infrastructure: Understand the importance of infrastructure.
• Introduction to GPS and Global Navigation Satellite Systems: Basics of GPS technology.

Module 2: Data Acquisition Using Mobile-Based GPS (ODK)

• Principles and Concepts of GPS: Dive into the fundamentals.
• Introduction to Mobile Data Gathering: Learn how to gather data on the go.
• Getting Started with Survey Authoring: Create and manage surveys.
• GIS Mapping of ODK Collected Data: Integrate field data into GIS.
• Online Web Mapping with Google Maps: Use Google Maps for visualization.
• Exporting to GIS/RS Environment for Further Analysis: Prepare for deeper analysis.
• Hosting Data Online: Learn to share data effectively.

Module 3: Data Acquisition from Other Sources

• Extracting Data from Satellite Images: Learn to gather satellite data.
• Extracting Data from Online GIS Sources: Utilize online resources.
• Importing Data to GIS: Understand the import process.
• Obtaining Data from Topographic Sheets: Use traditional data sources.
• Downloading Data from Open Street Maps: Access open-source data.

Module 4: Database Design and Development

• Working with Spreadsheet Data in GIS: Integrate spreadsheets effectively.
• Conceptual and Logical Database Design: Design robust databases.
• Building and Coding Attributes: Manage and code data attributes.
• Digitizing and Scanning Maps: Convert physical maps to digital.
• Relating Spatial and Attribute Data: Connect different data types.

Module 5: Editing and Management of GIS

• Adding Features to GIS Data: Enhance your data sets.
• Reducing GIS Data: Optimize data for analysis.
• Cutting Points of Interest in Image Datasets: Focus on crucial data.
• Transforming GIS Data: Adapt data to your needs.

Module 6: Integrating Imagery and Remote Sensing into GIS

• Radiometric and Atmospheric Correction: Improve image quality.
• Temporal Normalization: Consistent data over time.
• Geo-coding and Geo-referencing: Accurate location data.
• Transformation Techniques: Modify data formats.
• Signature Development: Identify unique data patterns.
• Supervised and Unsupervised Classification: Group similar data.
• Change Detection: Track changes over time.
• Accuracy Assessment: Ensure data reliability.

Module 7: Geo-Spatial Analysis

• Geo-processing Techniques: Advanced data processing.
• Creating Views and Themes: Visualize data effectively.
• Working with Attribute Tables: Manage data attributes