Book Image

QGIS:Becoming a GIS Power User

By : Ben Mearns, Alex Mandel, Alexander Bruy, Anita Graser, Víctor Olaya Ferrero
Book Image

QGIS:Becoming a GIS Power User

By: Ben Mearns, Alex Mandel, Alexander Bruy, Anita Graser, Víctor Olaya Ferrero

Overview of this book

The first module Learning QGIS, Third edition covers the installation and configuration of QGIS. You’ll become a master in data creation and editing, and creating great maps. By the end of this module, you’ll be able to extend QGIS with Python, getting in-depth with developing custom tools for the Processing Toolbox. The second module QGIS Blueprints gives you an overview of the application types and the technical aspects along with few examples from the digital humanities. After estimating unknown values using interpolation methods and demonstrating visualization and analytical techniques, the module ends by creating an editable and data-rich map for the discovery of community information. The third module QGIS 2 Cookbook covers data input and output with special instructions for trickier formats. Later, we dive into exploring data, data management, and preprocessing steps to cut your data to just the important areas. At the end of this module, you will dive into the methods for analyzing routes and networks, and learn how to take QGIS beyond the out-of-the-box features with plug-ins, customization, and add-on tools. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: ? Learning QGIS, Third Edition by Anita Graser ? QGIS Blueprints by Ben Mearns ? QGIS 2 Cookbook by Alex Mandel, Víctor Olaya Ferrero, Anita Graser, Alexander Bruy
Table of Contents (6 chapters)

Chapter 6. Estimating Unknown Values

In this chapter, we will use interpolation methods to estimate the unknown values at one location based on the known values at other locations.

Interpolation is a technique to estimate unknown values entirely on their geographic relationship with known location values. As space can be measured with infinite precision, data measurement is always limited by the data collector's finite resources. Interpolation and other more sophisticated spatial estimation techniques are useful to estimate the values at the locations that have not been measured. In this chapter, you will learn how to interpolate the values in weather station data, which will be scored and used in a model of vulnerability to a particular agricultural condition: mildew. We've made the weather data a subset to provide a month in the year during which vulnerability is usually historically high. An end user could use this application to do a ground truthing of the model,...