Geocomputation with R: 1st Edition (Hardback) book cover

Geocomputation with R

1st Edition

By Robin Lovelace, Jakub Nowosad, Jannes Muenchow

Chapman and Hall/CRC

337 pages

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Hardback: 9781138304512
pub: 2019-03-15
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Description

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data.

The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.

Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

Reviews

"Geocomputation with R offers several advantages. Firstly, it uses up-to-date packages, mainly the 'sf' package for vector processing which was not available at the time the previous books were written. 'sf’' is truly a game-changer in the field of working with spatial data in R. I believe this alone makes writing the new book worthwhile. Secondly, the book offers a very broad overview, trying—and in my opinion succeeding—to encompass all non-statistical themes involved in geo-computation, including subjects such as location and transport modeling in R (chapters 7-8) which were never published before. Thirdly, the book offers a lot of illustrations and clearly demonstrates key concepts in GIS and geo-computation from the R point of view. I believe these characteristics will give the book an advantage and quite possibly make it the most popular choice in the category of spatial analysis in R for several years to come…The book can be used both as reference and as a textbook…The present book will definitely become the main textbook for this course once published."

~Michael Dorman, Ben-Gurion University of the Negev

Table of Contents

1. Introduction

What is geocomputation?

Why geocomputation with R?

Software for geocomputation

R’s spatial ecosystem

The history of R-spatial

Exercises

I Foundations

2. Geographic data in R

Introduction

Vector data

An introduction to simple features

Why simple features?

Basic map making

Base plot arguments

Geometry types

Simple feature geometries (sfg)

Simple feature columns (sfc)

The sf class

Raster data

An introduction to raster

Basic map making

Raster classes

Coordinate Reference Systems

Geographic coordinate systems

Projected coordinate systems

CRSs in R

Units

Exercises

3. Attribute data operations

Introduction

Vector attribute manipulation

Vector attribute subsetting

Vector attribute aggregation

Vector attribute joining

Creating attributes and removing spatial information

Manipulating raster objects

Raster subsetting

Summarizing raster objects

Exercises

4. Spatial data operations

Introduction

Spatial operations on vector data

Spatial subsetting

Topological relations

Spatial joining

Non-overlapping joins

Spatial data aggregation

Distance relations

Spatial operations on raster data

Spatial subsetting

Map algebra

Local operations

Focal operations

Zonal operations

Global operations and distances

Merging rasters

Exercises

5. Geometry operations

Introduction

Geometric operations on vector data

Simplification

Centroids

Buffers

Affine transformations

Clipping

Geometry unions

Type transformations

Geometric operations on raster data

Geometric intersections

Extent and origin

Aggregation and disaggregation

Raster-vector interactions

Raster cropping

Raster extraction

Rasterization

Spatial vectorization

Exercises

6. Reprojecting geographic data

Introduction

When to reproject?

Which CRS to use?

Reprojecting vector geometries

Modifying map projections

Reprojecting raster geometries

Exercises

7. Geographic data I/O

Introduction

Retrieving open data

Geographic data packages

Geographic web services

File formats

Data Input (I)

Vector data

Raster data

Data output (O)

Vector data

Raster data

Visual outputs

Exercises

II Extensions

8. Making maps with R

Introduction

Static maps

tmap basics

Map objects

Aesthetics

Color settings

Layouts

Faceted maps

Inset maps

Animated maps

Interactive maps

Mapping applications

Other mapping packages

Exercises

9. Bridges to GIS software

Introduction

(R)QGIS

(R)SAGA

GRASS through rgrass

When to use what?

Other bridges

Bridges to GDAL

Bridges to spatial databases

Exercises

10. Scripts, algorithms and functions

Introduction

Scripts

Geometric algorithms

Functions

Programming

Exercises

11. Statistical learning

Introduction

Case study: Landslide susceptibility

Conventional modeling approach in R

Introduction to (spatial) cross-validation

Spatial CV with mlr

Generalized linear model

Spatial tuning of machine-learning hyperparameters

Conclusions

Exercises

III Applications

12. Transportation

Introduction

A case study of Bristol

Transport zones

Desire lines

Routes

Nodes

Route networks

Prioritizing new infrastructure

Future directions of travel

Exercises

13. Geomarketing

Introduction

Case study: bike shops in Germany

Tidy the input data

Create census rasters

Define metropolitan areas

Points of interest

Identifying suitable locations

Discussion and next steps

Exercises

14. Ecology

Introduction

Data and data preparation

Reducing dimensionality

Modeling the floristic gradient

mlr building blocks

Predictive mapping

Conclusions

Exercises

15. Conclusion

Introduction

Package choice

Gaps and overlaps

Where next?

The open source approach

About the Authors

Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems.

Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes.

Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping.

All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

About the Series

Chapman & Hall/CRC The R Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
SCI031000
SCIENCE / Earth Sciences / Geology