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Mastering Geospatial Development with QGIS 3.x

You're reading from   Mastering Geospatial Development with QGIS 3.x An in-depth guide to becoming proficient in spatial data analysis using QGIS 3.4 and 3.6 with Python

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781788999892
Length 466 pages
Edition 3rd Edition
Languages
Tools
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Authors (6):
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Luigi Pirelli Luigi Pirelli
Author Profile Icon Luigi Pirelli
Luigi Pirelli
Richard Smith Jr., GISP Richard Smith Jr., GISP
Author Profile Icon Richard Smith Jr., GISP
Richard Smith Jr., GISP
Kurt Menke, GISP Kurt Menke, GISP
Author Profile Icon Kurt Menke, GISP
Kurt Menke, GISP
Shammunul Islam Shammunul Islam
Author Profile Icon Shammunul Islam
Shammunul Islam
John Van Hoesen, GISP John Van Hoesen, GISP
Author Profile Icon John Van Hoesen, GISP
John Van Hoesen, GISP
Simon Miles Simon Miles
Author Profile Icon Simon Miles
Simon Miles
+2 more Show less
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Introduction FREE CHAPTER
2. A Refreshing Look at QGIS 3. Section 2: Getting Started
4. Styling Raster and Vector Data 5. Creating Spatial Databases 6. Preparing Vector Data for Processing 7. Preparing Raster Data for Processing 8. Section 3: Diving Deeper
9. Advanced Data Creation and Editing 10. Advanced Data Visualization 11. Section 4: Becoming a Master
12. The Processing Toolbox 13. Automating Workflows with the Graphical Modeler 14. Creating QGIS Plugins with PyQGIS and Problem Solving 15. PyQGIS Scripting 16. Other Books You May Enjoy

Automating Workflows with the Graphical Modeler

This chapter will provide you with an overview of the Graphical Modeler (GM). First, we will introduce the modeler and explore the various inputs and algorithms available for models. Then, we will demonstrate via step-by-step examples how to develop a model that can be added to the Processing Toolbox. We will also cover more advanced topics, including nesting models and executing models interactively. The specific topics that we will cover in this chapter are as follows:

  • An introduction to the graphical modeler
  • Working with your model
  • Executing model algorithms iteratively
  • Nesting models
  • Using batch processing with models
  • Converting a model into a Python script
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