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

GIS in R

GIS is a combination of software and data that informs us about the location of something and its relation to others. In GIS, every dataset is associated with a coordinate system, which is a system for representing the locations of different geographic features and different measurements. There are two main types of coordinate systems: geographic coordinate systems (GCS) and projected coordination systems. One example of GCS is using latitude-longitude, and one example of a projected coordination system is the transverse Mercator system. Whereas GCS uses a three-dimensional spherical surface, the projected coordination system uses two dimensions for representing spatial data. Data is used in GCS to define the position of the spheroid in relation to the center of the earth; a very commonly used GCS is WGS 84.