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Geographic Data Science with R: Visualizing and Analyzing Environmental Change

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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. However, 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, harmonizing 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 encompass 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 address 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 suitable 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.

    Author(s): Michael C. Wimberly
    Series: Chapman & Hall/CRC Data Science Series
    Publisher: CRC Press/Chapman & Hall
    Year: 2023

    Language: English
    Pages: 309
    City: Boca Raton

    Cover
    Half Title
    Series Page
    Title Page
    Copyright Page
    Dedication
    Contents
    List of Figures
    List of Tables
    Preface
    About the Author
    1. Introduction to R
    1.1. Basic Calculations
    1.2. R Objects
    1.2.1. Vectors
    1.2.2. Matrices and lists
    1.2.3. Data frames
    1.3. R Functions
    1.3.1. Data input and graphics
    1.3.2. Statistical analysis
    1.4. Tips for Programming in R
    1.5. Practice
    2. Graphics with ggplot2
    2.1. Creating a Simple Plot
    2.2. Aesthetic Mappings
    2.3. Facets
    2.4. Geometric Objects
    2.5. Scales
    2.6. Themes
    2.7. Combining ggplot Functions
    2.8. Other Types of Plots
    2.8.1. Scatterplots
    2.8.2. Bar charts
    2.8.3. Histograms
    2.8.4. Boxplots
    2.9. Practice
    3. Processing Tabular Data
    3.1. Single Table Verbs
    3.1.1. Select and rename
    3.1.2. The pipe operator
    3.1.3. Filter
    3.1.4. Arrange
    3.1.5. Mutate and transmute
    3.1.6. Application
    3.2. Summarizing
    3.2.1. Counts
    3.2.2. Summary functions
    3.3. Pivoting Data
    3.4. Joining Tables
    3.5. Practice
    4. Dates in R
    4.1. Converting Characters to Dates
    4.2. Other lubridate Operators and Functions
    4.3. Practice
    5. Vector Geospatial Data
    5.1. Importing Geospatial Data
    5.2. Creating Simple Maps
    5.3. Overlaying Vector Datasets
    5.4. Choropleth Maps
    5.5. Modifying the Appearance of the Map
    5.6. Exporting Graphics Output
    5.7. Practice
    6. Raster Geospatial Data—Continuous
    6.1. Importing Raster Data
    6.2. Maps of Raster Data
    6.3. Multilayer Rasters
    6.4. Computations on Raster Objects
    6.5. Practice
    7. Raster Geospatial Data—Discrete
    7.1. Importing and Mapping Land Cover Data
    7.2. Reclassifying Raster Data
    7.3. Focal Analysis of Raster Data
    7.4. Land Cover Change Analysis
    7.5. Land Cover Transition Matrices
    7.6. Mapping Specific Land Cover Changes
    7.7. Practice
    8. Coordinate Reference Systems
    8.1. Reprojecting Vector Data
    8.2. Reprojecting Raster Data
    8.3. Specifying Coordinate Reference Systems
    8.4. Practice
    9. Combining Vector Data with Continuous Raster Data
    9.1. Accessing Data with R Packages
    9.2. Zonal Statistics
    9.3. Zone Size and Raster Cell Size
    9.4. Extracting Raster Values with Point Data
    9.5. Practice
    10. Combining Vector Data with Discrete Raster Data
    10.1. Visualizing and Manipulating Vector Data
    10.2. Zonal Summaries of Discrete Raster Data
    10.3. Summarizing Land Cover With Stream Buffers
    10.4. Summarizing Land Cover With Point Buffers
    10.5. Practice
    11. Application—Wildfire Severity Analysis
    11.1. Remote Sensing Image Analysis
    11.2. Burn Severity Classification
    11.3. The Wildland-Urban Interface
    11.4. Topographic Effects
    11.4.1. Data processing
    11.4.2. Generalized additive modeling
    11.5. Practice
    12. Application—Species Distribution Modeling
    12.1. Tree Species Data
    12.2. WorldClim Historical Climate Data
    12.3. Modeling the Climate Niche
    12.3.1. Subalpine fir
    12.3.2. Douglas-fir
    12.4. Accuracy Assessment
    12.5. Climate Change Projections
    12.6. Practice
    Appendix
    Bibliography
    Index