Learning R for Geospatial Analysis | Leverage the power of R to elegantly manage crucial geospatial analysis tasks

Michael Dorman

ISBN 10: 1783984368 / ISBN 13: 9781783984367

Published by Packt Publishing, 2014

About this Item

Learning R for Geospatial Analysis | Leverage the power of R to elegantly manage crucial geospatial analysis tasks | Michael Dorman | Taschenbuch | Kartoniert / Broschiert | Englisch | 2014 | Packt Publishing | EAN 9781783984367 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 120645057

Bibliographic Details

• Title: Learning R for Geospatial Analysis | Leverage the power of R to elegantly manage crucial geospatial analysis tasks
• Publisher: Packt Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 1783984368
• ISBN 13: 9781783984367
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 20D millimeters 235 millimeters width by 191 millimeters height by 20 millimeters depth
• Weight: 679 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software a working installation of R is all that is necessary to begin.

About the Author

Michael Dorman is currently a PhD candidate at the Department of Geography and Environmental Development, Ben-Gurion University of the Negev. His research explores the response of planted pine forests to changing climate through remote sensing and dendrochronology. He uses R extensively for time series and spatial statistical analyses and visualization. In spring 2013, he prepared and taught a course named Introduction to Programming for Spatial Data Analysis at the Ben-Gurion University of the Negev, introducing R as an environment for spatial data analysis to undergraduate Geography students. The course material served as a foundation for this book. Michael holds a Master's degree in Life Sciences from the Ben-Gurion University of the Negev and a Bachelor's degree in Plant Sciences in Agriculture from The Hebrew University of Jerusalem. He has authored or coauthored eight papers in scientific literature and actively participated in 18 scientific conferences.