In geostatistics, observed variables are random, with the assumption that we observe them as outcomes of random processes. Usually, the data is observed in discrete locations of a spatially continuous regions, and we are interested in the prediction of values in non-observed locations in the given region. For example, soil salinity is measured in certain points in an area, and we are interested in predicting or interpolating soil salinity in every point under this area. Now, soil salinity in a point could depend on multiple factors: it could be due to its proximity to the saline-affected area, its spatial autocorrelation, the climate, or a random process. Other examples of geostatistical data include rainfall data, air quality, and measurements of chemical components at multiple locations in an area.
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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