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Hands-On Geospatial Analysis with R and QGIS
Hands-On Geospatial Analysis with R and QGIS

Hands-On Geospatial Analysis with R and QGIS: A beginner's guide to manipulating, managing, and analyzing spatial data using R and QGIS 3.2.2

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Hands-On Geospatial Analysis with R and QGIS

Fundamentals of GIS Using R and QGIS

In this chapter, we will learn the basics of how geographic information system (GIS) data is handled in R and QGIS and we will also learn about the different facilities provided by these two software for spatial data visualization. The focus will be on understanding the basics of vector data in the light of R and QGIS. Starting with the basics of GIS, projection systems, and the loading and visualization of these data in R and QGIS, we will be covering all of these with hands-on examples. We will start with the basics of spatial (here, vector) data, which will be followed by basic vector data loading and visualization in R, and lastly cover basic vector data loading and visualization again, but this time using QGIS.

Completing this chapter, you will learn about the following topics:

  • The basics of GIS and vector data
  • Coordinate transformation...

GIS in R

GIS is a combination of software and data that informs us about the location of something and its relation to others. In GIS, every dataset is associated with a coordinate system, which is a system for representing the locations of different geographic features and different measurements. There are two main types of coordinate systems: geographic coordinate systems (GCS) and projected coordination systems. One example of GCS is using latitude-longitude, and one example of a projected coordination system is the transverse Mercator system. Whereas GCS uses a three-dimensional spherical surface, the projected coordination system uses two dimensions for representing spatial data. Data is used in GCS to define the position of the spheroid in relation to the center of the earth; a very commonly used GCS is WGS 84.

...

Vector data in QGIS

Now, we will work with a shapefile in QGIS and find out its projection system. First, we import a shapefile named BGD_adm4.shp from the folder Data under Chapter 2. To recall how to import a vector file, refer back to Chapter 1, Setting Up R and QGIS Environments for Geospatial Tasks.

Now, we will see the shapefile as follows:

Now, to check out the projection system it is using, we need to click on Project Properties under Project in the menu bar as instructed in the following screenshot:

  1. Click on Project and then Properties... under it:
  1. Now, click on CRS:

Now, we can see the coordinate system (a) and the spatial extent and projection (b) as shown in this screenshot:

We can also select and change CRS from the Layer panel. We can do so using the following steps:

  1. Right-click on the BGD_adm4 layer in the Layer panel. Click on Properties as highlighted...

Summary

In this chapter, we learned about the basics of GIS and learned particularly how vector data is stored in R and QGIS. We learned to import point data from an Excel file and how to plot it on a map in R. We used the ggmap package of R to accomplish this task along with various options for plotting these. The sp package of R has six main classes for dealing with spatial data: SpatialPoints, SpatialPointsDataFrame, SpatialLines, SpatialLinesDataFrame, SpatialPolygons, and SpatialPolygonsDataFrame. We learned to use the readOGR() function of the sp package for importing shapefiles containing different vector classes. In this chapter, we also learned to visualize quantitative and qualitative data in R.

We also learned how to use QGIS for vector data importation and how to visualize those. Importing Excel files to QGIS was also discussed, along with visualizing quantitative...

Questions

After completing this chapter, readers should be comfortable answering the following questions:

  • What are the spatial data classes available in R (sp package)?
  • How can users visualize spatial data in R?
  • How can users import shapefiles and use background maps in R?
  • How is CRS set in QGIS?
  • How is quantitative and qualitative data visualized in QGIS?
  • How are text diagrams used in QGIS?
  • How may background maps be added?

Further reading

When going through vector data manipulation in R, we have just touched the surface of what could be possible using R. We will cover other data manipulation techniques as we step on through new chapters. But if you want to have a more in-depth discussion about these topics, you can have a look at the book Learning R for Geospatial Analysis by Michael Dorman and An Introduction to R for Spatial Analysis and Mapping by Brunsdon and Comber. For QGIS, if you want better coverage of visualizing and stylizing vector data, QGIS by Example by Bruy and Svidzinska and Mastering QGIS by Menke et al. are two great reads.

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Key benefits

  • Understand the basics of R and QGIS to work with GIS and remote sensing data
  • Learn to manage, manipulate, and analyze spatial data using R and QGIS
  • Apply machine learning algorithms to geospatial data using R and QGIS

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.

Who is this book 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.

What you will learn

  • Install R and QGIS
  • Get familiar with the basics of R programming and QGIS
  • Visualize quantitative and qualitative data to create maps
  • Find out the basics of raster data and how to use them in R and QGIS
  • Perform geoprocessing tasks and automate them using the graphical modeler of QGIS
  • Apply different machine learning algorithms on satellite data for landslide susceptibility mapping and prediction
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 30, 2018
Length: 354 pages
Edition : 1st
Language : English
ISBN-13 : 9781788991674
Vendor :
QGIS Development Team
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Languages :
Tools :

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Product Details

Publication date : Nov 30, 2018
Length: 354 pages
Edition : 1st
Language : English
ISBN-13 : 9781788991674
Vendor :
QGIS Development Team
Category :
Languages :
Tools :

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Table of Contents

11 Chapters
Setting Up R and QGIS Environments for Geospatial Tasks Chevron down icon Chevron up icon
Fundamentals of GIS Using R and QGIS Chevron down icon Chevron up icon
Creating Geospatial Data Chevron down icon Chevron up icon
Working with Geospatial Data Chevron down icon Chevron up icon
Remote Sensing Using R and QGIS Chevron down icon Chevron up icon
Point Pattern Analysis Chevron down icon Chevron up icon
Spatial Analysis Chevron down icon Chevron up icon
GRASS, Graphical Modelers, and Web Mapping Chevron down icon Chevron up icon
Classification of Remote Sensing Images Chevron down icon Chevron up icon
Landslide Susceptibility Mapping Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 33.3%
2 star 33.3%
1 star 0%
Stringz Jan 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a good book for any one wanting a career as a geospatial Analyst. it is fully loaded with an awesome selection of topics. The topics can be leveraged in most big data science analysis. I really enjoyed the progression and the topic selection.
Amazon Verified review Amazon
kensuke May 14, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
QGISの解説部分は、日本語でこの書籍より安価な本が増えましたので、必要ないです。Rでのspdepパッケージの解説については、空間計量の初歩、重み行列の生成とモラン統計量の求め方、検定手法がソースコード付きでありますので、そのへんの需要があれば買っても良いかな。特に、空間計量の検定については具体的なソースコードの例が、日本語文献だと少ないので、お手本として見るにはよいかもしれない。ただ、その箇所を見るために5000円払うかは迷うところ。
Amazon Verified review Amazon
Cliente Amazon Apr 29, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Il libro è stampato da Amazon. Le pagine interne sono in scala di grigio e questo rende difficoltosa la fruizione delle parti illustrate relative alle.schermate di QGIS e R.
Amazon Verified review Amazon
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