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

Setting Up R and QGIS Environments for Geospatial Tasks

This chapter will walk its readers through the different stages of setting up the R and QGIS environments. R and QGIS are both free and open source software that can be used for various geospatial tasks. R benefits from more than 10,000 packages developed by its community, and QGIS also benefits from a number of plugins that are available to QGIS users. QGIS can complement R, and vice versa, for the conduct of many sophisticated geospatial tasks, and many statistical and machine learning algorithms can be very easily applied using R with the help of QGIS.

The first segment of the book starts by discussing how to install R and getting to know its environment. That is followed by data types in R, and different operations in R, and then getting acquainted with writing functions and plotting. The second segment consists of installing QGIS, learning the QGIS environment, and getting help in QGIS.

The following topics are to be covered in this chapter:

  • Installing R
  • Basic data types and the data structure in R
  • Looping, functions, and apply family in R
  • Plotting in R
  • Installing QGIS
  • Getting to know the QGIS environment.