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


In this chapter, we learned how to test for autocorrelation in spatial data using Moran's I index. We also learned how to model autocorrelation using a SAR model. This was followed by modeling count data using a Poisson GLM. After that, we learned the basics of geostatistics, with variograms and kriging, in particular. Then, we learned how to understand the degree of spatial dependence in a random field using variograms. We learned about the different ways of plotting and modeling spatial dependence using the geoR and gstat packages. We then learned how to predict or interpolate data from covariograms using a method called kriging, and we learned how to compute exceedance probability and looked at checking the residuals of a prediction.

In the upcoming chapters, we'll learn how to automate different spatial processing and analysis tasks. We'll then learn...