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

Analysis of point patterns

One of the point pattern processes is called Complete Spatial Randomness (CSR), which means that the probability of finding events at any point is the same everywhere. There are a couple of methods for testing this, which we'll be discussing next.

Quadrat test

In this test, the window is divided into parts and the number of points in each part is computed. If the process is CSR, these numbers in subregions come from a Poisson distribution. Then, using a chi-square test, we can test for CSR.

We'll work with ppp3.csv, in this case, and use the quadrat.test() function from the spstat package to conduct the quadrat test:

# update address as necessary
location3 = read.csv("F:/Hands-on-Geospatial...