In this chapter, we've learned how to create vector data and raster data. In doing so, this chapter showed how we can create point, line, and polygon data. Furthermore, it also covered how we can populate different features with attribute values and how we can use the Georeferencer plugin to digitize an image. We ended the chapter by learning how to create spatial databases and how to import shapefiles into them. We've covered just enough to proceed to the next chapters, where we will delve deep into different spatial operations, spatial analysis, and more. We haven't talked in detail about spatial databases and many other operations that could be performed using spatial databases. But the topics covered so far should have equipped you with sufficient resources to dig deeper and, in later chapters, to start applying machine learning models in spatial research...
Hands-On Geospatial Analysis with R and QGIS
By :
Hands-On Geospatial Analysis with R and QGIS
By:
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)
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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