Description

Book Synopsis

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors.

The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes array data with spatial and temporal dimensions. It also shows how geometrical operations ch

Trade Review

“I think that this is an important book. I am convinced it will be seen as a reference for scientists working with spatial data in R but also as a textbook for scientists and postgraduate students who are learning the concepts and how to do it practically in R (admittedly at a very advanced level!). It has certainly be on the shelf of everyone working with and teaching spatial data in R.”
-Hanna Meyer, Institute of Landscape Ecology, University of Münster, Germany



Table of Contents

Part 1. Spatial Data 1. Getting Started 2. Coordinates 3. Geometries 4. Spherical Geometries 5. Attributes and Support 6. Data Cubes Part 2. R for Spatial Data Science 7. Introduction to sf and stars 8. Plotting spatial data 9. Large data and cloud native Part 3. Models for Spatial Data 10. Statistical modelling of spatial data 11. Point Pattern Analysis 12. Spatial Interpolation 13. Multivariate and Spatiotemporal Geostatistics 14. Proximity and Areal Data 15. Measures of spatial autocorrelation 16. Spatial Regression 17. Spatial econometrics models Appendix A. Older R Spatial Packages Appendix B. R basics

Spatial Data Science

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    £73.14

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    RRP £76.99 – you save £3.85 (5%)

    Order before 4pm today for delivery by Thu 9 Jul 2026.

    A Hardback by Edzer Pebesma, Roger Bivand

    15 in stock

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      Publisher: Taylor & Francis Ltd
      Publication Date: 1/10/2023 12:05:00 AM
      ISBN13: 9781138311183, 978-1138311183
      ISBN10: 1138311189

      Description

      Book Synopsis

      Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors.

      The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes array data with spatial and temporal dimensions. It also shows how geometrical operations ch

      Trade Review

      “I think that this is an important book. I am convinced it will be seen as a reference for scientists working with spatial data in R but also as a textbook for scientists and postgraduate students who are learning the concepts and how to do it practically in R (admittedly at a very advanced level!). It has certainly be on the shelf of everyone working with and teaching spatial data in R.”
      -Hanna Meyer, Institute of Landscape Ecology, University of Münster, Germany



      Table of Contents

      Part 1. Spatial Data 1. Getting Started 2. Coordinates 3. Geometries 4. Spherical Geometries 5. Attributes and Support 6. Data Cubes Part 2. R for Spatial Data Science 7. Introduction to sf and stars 8. Plotting spatial data 9. Large data and cloud native Part 3. Models for Spatial Data 10. Statistical modelling of spatial data 11. Point Pattern Analysis 12. Spatial Interpolation 13. Multivariate and Spatiotemporal Geostatistics 14. Proximity and Areal Data 15. Measures of spatial autocorrelation 16. Spatial Regression 17. Spatial econometrics models Appendix A. Older R Spatial Packages Appendix B. R basics

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