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

Working with Geospatial Data

This chapter introduces you to different types of spatial data manipulation in R and QGIS. We'll learn how to merge shapefiles and clip our data to a vector file, differences between shapefiles, how to get the intersection of point data and line data, and how to create a buffer around a feature. We'll use R and QGIS to demonstrate these operations and you'll find out that, for some of these operations, R is more convenient than QGIS, and vice versa. All of these techniques are very helpful to have in a geospatial analyst's toolbox.

After completing this chapter, you'll have hands-on knowledge of the following:

  • Combining shapefiles
  • Clipping points to the boundary of a shapefile
  • Difference
  • Intersection between two vector files
  • Creating buffers
  • Calculating the area of polygons
  • Converting vector data types
  • Creating statistical...