We learned how to use different tools in R and QGIS for working with vector data and modifying or creating new data by doing different operations such as merging, clipping, intersecting, creating buffers, and more. We also learned how to use different spatial queries in QGIS and R. In doing so, we have seen how important it is for two different layers to have the same projection system. We haven't touched upon many other tasks, such as using the rgeos package for calculating distance between geometries using gDistance() or using gUnaryUnion() for taking the union of two shapefiles. With the tools you are equipped with now, you can easily understand other functionalities not covered here and should now be able to perform most of these data manipulation tasks. In doing so, you might already have developed some preferences between QGIS and R for performing each operation...
Hands-On Geospatial Analysis with R and QGIS
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Hands-On Geospatial Analysis with R and QGIS
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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.
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Current Title:
Hands-On Geospatial Analysis with R and QGIS
Table of Contents (12 chapters)
Preface
Free Chapter
Setting Up R and QGIS Environments for Geospatial Tasks
Fundamentals of GIS Using R and QGIS
Creating Geospatial Data
Working with Geospatial Data
Remote Sensing Using R and QGIS
Point Pattern Analysis
Spatial Analysis
GRASS, Graphical Modelers, and Web Mapping
Classification of Remote Sensing Images
Landslide Susceptibility Mapping
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