Hands-On Geospatial Analysis with R and QGIS
Shammunul Islam, Brad Hamson
Sold by Rarewaves USA, OSWEGO, IL, U.S.A.
AbeBooks Seller since 10 June 2025
New - Soft cover
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Add to basketSold by Rarewaves USA, OSWEGO, IL, U.S.A.
AbeBooks Seller since 10 June 2025
Condition: New
Quantity: Over 20 available
Add to basketManaging spatial data has always been challenging and it's getting more complex as the size of data increases. This book is your companion to understand, manage, and analyze spatial data effectively using R and QGIS. You'll learn to use different statistical analyses with spatial data and automate spatial tasks. You'll also learn to classify remote sensing data and use a machine learning approach to map landslide susceptibility.
Seller Inventory # LU-9781788991674
Practical examples with real-world projects in GIS, Remote sensing, Geospatial data management and Analysis using the R programming language
Key Features
Book Description
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.
What you will learn
Who this book is for:
This book is great for geographers, environmental scientists, statisticians, and every professional who deals with spatial data. If you want to learn how to handle GIS and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful but is not necessary.
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