Geographic Data Science with R: Visualizing and Analyzing Environmental Change

310 Pages·2023·45.923 MB·English

by  Michael C. Wimberly

Geographic Data
Science with R
The burgeoning field of data science has provided a wealth of techniques for analysing large and
complex geospatial datasets, including descriptive, explanatory, and predictive analytics. How-
ever, applying these methods is just one part of the overall process of geographic data science.
Other critical steps include screening for suspect data values, handling missing data, harmoniz-
ing data from multiple sources, summarizing the data, and visualizing data and analysis results.
Although there are many books available on statistical and machine learning methods, few en-
compass the broader topic of scientific workflows for geospatial data processing and analysis.
The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials
aimed at teaching good practices for using geospatial data to address problems in environmental
geography. It is based on the R language and environment, which currently provides the best
option for working with diverse spatial and non-spatial data in a single platform. Fundamental
techniques for processing and visualizing tabular, vector, and raster data are introduced through
a series of practical examples followed by case studies that combine multiple types of data to ad-
dress more complex problems.
The book will have a broad audience. Both students and professionals can use it as a workbook
to learn high-level techniques for geospatial data processing and analysis with R. It is also suit-
able as a textbook. Although not intended to provide a comprehensive introduction to R, it is
designed to be accessible to readers who have at least some knowledge of coding but little to no
experience with R.
Key Features:
• Focus on developing practical workflows for processing and integrating multiple sources of
geospatial data in R
• Example-based approach that teaches R programming and data science concepts through
real-world applications related to climate, land cover and land use, and natural hazards.
• Consistent use of tidyverse packages for tabular data manipulation and visualization.
• Strong focus on analysing continuous and categorical raster datasets using the new terra package
• Organized so that each chapter builds on the topics and techniques covered in the preceding
chapters
• Can be used for self-study or as the textbook for a geospatial science course.
CHAPMAN & HALL/CRC DATA SCIENCE SERIES
Reflecting the interdisciplinary nature of the field, this book series brings together researchers, practi-
tioners, and instructors from statistics, computer science, machine learning, and analytics. The series
will publish cutting-edge research, industry applications, and textbooks in data science.
The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of
the series includes titles in the areas of machine learning, pattern recognition, predictive analytics,
business analytics, Big Data, visualization, programming, software, learning analytics, data wrangling,
interactive graphics, and reproducible research.
Published Titles
Statistical Foundations of Data Science
Jianqing Fan, Runze Li, Cun-Hui Zhang, and Hui Zou
A Tour of Data Science: Learn R and Python in Parallel
Nailong Zhang
Explanatory Model Analysis
Explore, Explain, and, Examine Predictive Models
Przemyslaw Biecek, Tomasz Burzykowski
An Introduction to IoT Analytics
Harry G. Perros
Data Analytics
A Small Data Approach
Shuai Huang and Houtao Deng
Public Policy Analytics
Code and Context for Data Science in Government
Ken Steif
Supervised Machine Learning for Text Analysis in R
Emil Hvitfeldt and Julia Silge
Massive Graph Analytics
Edited by David Bader
Data Science
An Introduction
Tiffany-Anne Timbers, Trevor Campbell and Melissa Lee
Tree-Based Methods
A Practical Introduction with Applications in R
Brandon M. Greenwell
Urban Informatics
Using Big Data to Understand and Serve Communities
Daniel T. O’Brien
Introduction to Environmental Data Science
Jerry Douglas Davis
Hands-On Data Science for Librarians
Sarah Lin and Dorris Scott
For more information about this series, please visit: https://www.routledge.com/Chapman--Hall-
CRC-Data-Science-Series/book-series/CHDSS
Geographic Data
Science with R
Visualizing and Analyzing
Environmental Change
Michael C. Wimberly
Designed cover image: © Michael C. Wimberly
First edition published 2023
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2023 Michael C. Wimberly
Reasonable efforts have been made to publish reliable data and information, but the author and pub-
lisher cannot assume responsibility for the validity of all materials or the consequences of their use.
The authors and publishers have attempted to trace the copyright holders of all material reproduced
in this publication and apologize to copyright holders if permission to publish in this form has not
been obtained. If any copyright material has not been acknowledged please write and let us know so
we may rectify in any future reprint.
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hereafter invented, including photocopying, microfilming, and recording, or in any information stor-
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978-750-8400. For works that are not available on CCC please contact [email protected]
Trademark notice: Product or corporate names may be trademarks or registered trademarks and are
used only for identification and explanation without intent to infringe.
ISBN: 978-1-032-34771-4 (hbk)
ISBN: 978-1-032-35288-6 (pbk)
ISBN: 978-1-003-32619-9 (ebk)
DOI: 10.1201/9781003326199
Typeset in Latin modern
by KnowledgeWorks Global Ltd.
Publisher’s note: This book has been prepared from camera-ready copy provided by the authors.
Access the companion website: insert CW URL
To my parents,
who taught me to value learning and supported my academic endeavors

Contents
List of Figures xi
List of Tables xvii
Preface xix
About the Author xxv
1 Introduction to R 1
1.1 Basic Calculations . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 R Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Matrices and lists. . . . . . . . . . . . . . . . . . . . . . 9
1.2.3 Data frames . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 R Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.3.1 Data input and graphics . . . . . . . . . . . . . . . . . . 14
1.3.2 Statistical analysis . . . . . . . . . . . . . . . . . . . . . 19
1.4 Tips for Programming in R . . . . . . . . . . . . . . . . . . . . 22
1.5 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2 Graphics with ggplot2 25
2.1 Creating a Simple Plot . . . . . . . . . . . . . . . . . . . . . . . 27
2.2 Aesthetic Mappings. . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3 Facets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4 Geometric Objects . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.5 Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.6 Themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.7 Combining ggplot Functions . . . . . . . . . . . . . . . . . . . . 43
2.8 Other Types of Plots . . . . . . . . . . . . . . . . . . . . . . . . 44
2.8.1 Scatterplots . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.8.2 Bar charts . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.8.3 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.8.4 Boxplots . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.9 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
vii
viii Contents
3 Processing Tabular Data 49
3.1 Single Table Verbs . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.1.1 Select and rename . . . . . . . . . . . . . . . . . . . . . 50
3.1.2 The pipe operator . . . . . . . . . . . . . . . . . . . . . 53
3.1.3 Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.1.4 Arrange . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.1.5 Mutate and transmute . . . . . . . . . . . . . . . . . . . 56
3.1.6 Application . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 Summarizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.2.1 Counts. . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.2.2 Summary functions. . . . . . . . . . . . . . . . . . . . . 64
3.3 Pivoting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.4 Joining Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.5 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4 Dates in R 73
4.1 Converting Characters to Dates . . . . . . . . . . . . . . . . . . 73
4.2 Other lubridate Operators and Functions . . . . . . . . . . . . 79
4.3 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5 Vector Geospatial Data 83
5.1 Importing Geospatial Data . . . . . . . . . . . . . . . . . . . . 84
5.2 Creating Simple Maps . . . . . . . . . . . . . . . . . . . . . . . 85
5.3 Overlaying Vector Datasets . . . . . . . . . . . . . . . . . . . . 89
5.4 Choropleth Maps . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.5 Modifying the Appearance of the Map . . . . . . . . . . . . . . 94
5.6 Exporting Graphics Output . . . . . . . . . . . . . . . . . . . . 98
5.7 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6 Raster Geospatial Data—Continuous 103
6.1 Importing Raster Data . . . . . . . . . . . . . . . . . . . . . . . 104
6.2 Maps of Raster Data . . . . . . . . . . . . . . . . . . . . . . . . 107
6.3 Multilayer Rasters . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.4 Computations on Raster Objects . . . . . . . . . . . . . . . . . 116
6.5 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
7 Raster Geospatial Data—Discrete 123
7.1 Importing and Mapping Land Cover Data . . . . . . . . . . . . 124
7.2 Reclassifying Raster Data . . . . . . . . . . . . . . . . . . . . . 128
7.3 Focal Analysis of Raster Data . . . . . . . . . . . . . . . . . . . 133
7.4 Land Cover Change Analysis . . . . . . . . . . . . . . . . . . . 135
7.5 Land Cover Transition Matrices . . . . . . . . . . . . . . . . . . 141
7.6 Mapping Specific Land Cover Changes . . . . . . . . . . . . . . 147
7.7 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Contents ix
8 Coordinate Reference Systems 151
8.1 Reprojecting Vector Data . . . . . . . . . . . . . . . . . . . . . 152
8.2 Reprojecting Raster Data . . . . . . . . . . . . . . . . . . . . . 160
8.3 Specifying Coordinate Reference Systems . . . . . . . . . . . . 167
8.4 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
9 Combining Vector Data with Continuous Raster Data 171
9.1 Accessing Data with R Packages . . . . . . . . . . . . . . . . . 171
9.2 Zonal Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
9.3 Zone Size and Raster Cell Size . . . . . . . . . . . . . . . . . . 176
9.4 Extracting Raster Values with Point Data . . . . . . . . . . . . 183
9.5 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
10 Combining Vector Data with Discrete Raster Data 189
10.1 Visualizing and Manipulating Vector Data . . . . . . . . . . . . 191
10.2 Zonal Summaries of Discrete Raster Data . . . . . . . . . . . . 196
10.3 Summarizing Land Cover With Stream Buffers . . . . . . . . . 203
10.4 Summarizing Land Cover With Point Buffers . . . . . . . . . . 210
10.5 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
11 Application—Wildfire Severity Analysis 215
11.1 Remote Sensing Image Analysis . . . . . . . . . . . . . . . . . . 216
11.2 Burn Severity Classification . . . . . . . . . . . . . . . . . . . . 221
11.3 The Wildland-Urban Interface. . . . . . . . . . . . . . . . . . . 223
11.4 Topographic Effects . . . . . . . . . . . . . . . . . . . . . . . . 229
11.4.1 Data processing. . . . . . . . . . . . . . . . . . . . . . . 230
11.4.2 Generalized additive modeling . . . . . . . . . . . . . . 234
11.5 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
12 Application—Species Distribution Modeling 241
12.1 Tree Species Data. . . . . . . . . . . . . . . . . . . . . . . . . . 242
12.2 WorldClim Historical Climate Data . . . . . . . . . . . . . . . . 247
12.3 Modeling the Climate Niche . . . . . . . . . . . . . . . . . . . . 251
12.3.1 Subalpine fir . . . . . . . . . . . . . . . . . . . . . . . . 251
12.3.2 Douglas-fir . . . . . . . . . . . . . . . . . . . . . . . . . 255
12.4 Accuracy Assessment . . . . . . . . . . . . . . . . . . . . . . . . 259
12.5 Climate Change Projections . . . . . . . . . . . . . . . . . . . . 263
12.6 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
Appendix 269
Bibliography 277
Index 281