Hands-On Geospatial Analysis with R and QGIS

Hands-On Geospatial Analysis with R and QGIS

Practical examples with real-world projects in GIS, Remote sensing, Geospatial data management and Analysis using the R programming language

Table of Contents

Chapter 1: Setting Up R and QGIS Environments for Geospatial Tasks

Chapter 2: Fundamentals of GIS Using R and QGIS

GIS in R

Vector data in QGIS



Further reading

Chapter 3: Creating Geospatial Data

Getting data from the web

Creating vector data

Digitizing a map

Working with databases



Further reading

Chapter 4: Working with Geospatial Data

Working with vector data in R

Working with vector data in QGIS


Chapter 5: Remote Sensing Using R and QGIS

Basics of remote sensing

Working with raster data in R

Working with raster data in QGIS



Chapter 6: Point Pattern Analysis

Introduction to point pattern analysis

Analysis of point patterns


Chapter 7: Spatial Analysis

Testing autocorrelation

Modeling autocorrelation

Generalized linear model

Spatial interpolation



Chapter 8: GRASS, Graphical Modelers, and Web Mapping


Web mapping


Chapter 9: Classification of Remote Sensing Images

Classification of raster data

Supervised classification

Unsupervised classification


Chapter 10: Landslide Susceptibility Mapping

Landslides in Bangladesh

Landslide susceptibility modeling



Install R and QGIS

Get familiar with the basics of R programming and QGIS Visualize quantitative and qualitative data to create maps Find out the basics of raster data and how to use them in R and QGIS Perform geoprocessing tasks and automate them using the graphical modeler of QGIS Apply different machine learning algorithms on satellite data for landslide susceptibility mapping and prediction


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.