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


Where is something happening? Are there similarities between different areas with respect to an attribute of interest? Which area is most susceptible to a particular hazard? These and many other questions can be answered if you take location into account in your analysis. Location plays an important role and has critical implications for many policy decisions regarding environment, biodiversity, and socio-economy. This area is increasingly being studied by researchers and practitioners from many disciplines. In particular, in the realization of Sustainable Development Goals (SDGs), Geographic Information Systems (GIS), and remote sensing (RS), data can play a pivotal role.

R and QGIS are two examples of open source software that can be used free of charge for working with spatial data. By using them, we can answer many of our questions regarding location. For the last couple of years, R, a language originally intended for statisticians, has also been used as GIS software. We can readily call any spatial package in R and apply it to our data. QGIS is very powerful GIS software that enables users to perform many complex spatial tasks. QGIS and R both have a very strong user community and, by combining these two according to their relative advantages, you can perform very sophisticated and complex spatial analysis tasks.

This book covers both R and QGIS, unlike the other books on the market. Assuming you have zero, or rudimentary, knowledge of GIS and RS, this book will have transformed you from a beginner to an intermediate user of GIS and RS by the time you finish it. This book guides you from the initial step of setting up the software, to spatial analysis, geostatistics, and applying different models for landslide susceptibility mapping by providing hands-on examples, code, and screenshots. After reading this book, you should be able to generalize the examples to your spatial problems and create susceptibility maps using machine learning algorithms.