Book Image

Hands-On Geospatial Analysis with R and QGIS

By : Shammunul Islam, Brad Hamson
Book Image

Hands-On Geospatial Analysis with R and QGIS

By: Shammunul Islam, Brad Hamson

Overview of this book

Managing spatial data has always been challenging and it's getting more complex as the size of data increases. Spatial data is actually big data and you need different tools and techniques to work your way around to model and create different workflows. R and QGIS have powerful features that can make this job easier. This book is your companion for applying machine learning algorithms on GIS and remote sensing data. You’ll start by gaining an understanding of the nature of spatial data and installing R and QGIS. Then, you’ll learn how to use different R packages to import, export, and visualize data, before doing the same in QGIS. Screenshots are included to ease your understanding. Moving on, you’ll learn about different aspects of managing and analyzing spatial data, before diving into advanced topics. You’ll create powerful data visualizations using ggplot2, ggmap, raster, and other packages of R. You’ll learn how to use QGIS 3.2.2 to visualize and manage (create, edit, and format) spatial data. Different types of spatial analysis are also covered using R. Finally, you’ll work with landslide data from Bangladesh to create a landslide susceptibility map using different machine learning algorithms. By reading this book, you’ll transition from being a beginner to an intermediate user of GIS and remote sensing data in no time.
Table of Contents (12 chapters)
GRASS, Graphical Modelers, and Web Mapping


This chapter introduced you to a slightly advanced concept in QGIS. We focused on using some functionalities of GRASS. GRASS has many other functions that we haven't covered in this chapter and, after getting knowledge from this book on the basics of GRASS, we expect that you will have some comfort in exploring other functionalities independently. This chapter only touched upon some of the key concepts in GRASS GIS that are compatible with QGIS, such as learning how to use a mapset and how to put data inside it. We also learned how to use Processing Toolbox – or, more specifically, a graphical modeler – to automate different spatial tasks. Finally, we learned how to use the gis2web plugin for web mapping directly from QGIS.

In the next chapter, we will familiarize you with the concept of raster image classification using the SCP of QGIS.