Learning R for Geospatial Analysis: Leverage the Power of R to Elegantly Manage Crucial Geospatial Analysis Tasks

364 Pages·2014·5.83 MB·English

by  Michael Dorman

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Learning R for Geospatial
Analysis
Leverage the power of R to elegantly manage crucial
geospatial analysis tasks
Michael Dorman
BIRMINGHAM - MUMBAI
www.it-ebooks.info
Learning R for Geospatial Analysis
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First published: December 2014
Production reference: 1191214
Published by Packt Publishing Ltd.
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ISBN 978-1-78398-436-7
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Credits
Author Project Coordinator
Michael Dorman Rashi Khivansara
Reviewers Proofreaders
Dr. Amrinder Arora Simran Bhogal
Dan Hammer Stephen Copestake
Baburao Kamble Maria Gould
Dr. Robin Lovelace Ameesha Green
Dipanjan Sarkar
Dr. Makhan Virdi Indexer
Rekha Nair
Commissioning Editor
Akram Hussain Graphics
Abhinash Sahu
Acquisition Editor
Greg Wild Production Coordinator
Alwin Roy
Content Development Editor
Mohammed Fahad Cover Work
Alwin Roy
Technical Editor
Ankita Thakur
Copy Editors
Pranjali Chury
Adithi Shetty
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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 scientiic
literature and actively participated in 18 scientiic conferences.
I would like to express my gratitude to all the people from whom
I have learned more about R and to those who have created this
wonderful programming language. A special thanks goes to the
excellent team at Packt Publishing for making this book possible.
This book is dedicated to my wife, Hila, who unconditionally
supported me every step of the way.
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About the Reviewers
Dr. Amrinder Arora is an adjunct faculty member in the Department of
Computer Science at the George Washington University. He teaches graduate and
undergraduate courses in Computer Science, mostly related to the design and
analysis of computer algorithms and the design of data structures. He is also the
author of the book Analysis and Design of Algorithms, Cognella Academic Publishing.
He has been conferred the Instructor of the Year Award by the Department of
Computer Science at the George Washington University and has received a VIP
Grants Award by the Bowie State University. You can read more about his research
at http://www.standardwisdom.com.
As part of his industry experience, he has served in the management teams of
leading technology companies, including BizMerlin, Edifecs, and NTELX. As part of
the Affordable Care Act, Dr. Arora designed a health exchange connector, a leading
product in the $200 million market to connect insurance companies (payers) to the
health insurance exchanges. As a leading expert in risk targeting, Dr. Arora led the
technical design for US FDA's PREDICT system, which currently screens more than
16 million imports a year. His efforts in supporting FDA's PREDICT program were
recognized by the FDA commissioner, Dr. Margaret Hamburg. The transportation
management system designed by Dr. Arora for the port of Aqaba in Jordan won the
award for the most innovative product by the Intelligent Transportation Society
of America.
Dr. Arora earned an undergraduate degree in Computer Science from the Indian
Institute of Technology, Delhi, and a Master's degree and doctorate, both in
Computer Science, from the George Washington University. He served as a reviewer
for numerous journals and conferences and many of his reviews have also been
published in ACM Computing Reviews.
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Dan Hammer is a data scientist and environmental economist who served as a
Presidential Innovation Fellow at NASA as part of the White House program. He is
a PhD student at University of California, Berkeley, and was formerly the Chief Data
Scientist at the World Resources Institute, where he led the technical team behind
Global Forest Watch. Dan writes code in Python, R, and Clojure on subjects ranging
from spatial econometrics to information theory. He is currently reviewing Clojure for
Data Science, Packt Publishing.
Baburao Kamble is an assistant research professor of Remote Sensing and
Geospatial Data Analytics at the University of Nebraska-Lincoln (UNL). Currently,
he works at UNL on developing machine learning and data mining algorithms using
Big Data tools and techniques for climate and weather data. He has been involved
in teaching Geospatial Information Sciences, Data Analysis using R, Python for
Geospatial Data Analytics, and MATLAB courses at the graduate level. He is also
the author of the upcoming book Practical Data Analysis Cookbook, Packt Publishing. He
likes to spend his free time with new and interesting data science developments.
Dr. Robin Lovelace is an environmental geographer with 5 years of experience
using R for spatial analysis, map making, and statistics. He has coauthored the
popular free and open source online tutorial Introduction to visualising spatial
data in R (2014), and teaches R to a range of professional and academic audiences.
Robin's latest book Spatial microsimulation with R, CRC Press (which will be published
in 2015) demonstrates methods to generate and analyze multilevel data. By
combining individual and geographical-level data, the technique can provide new
insights into complex behaviors, for example, as an input into agent-based models.
Robin believes passionately in using open source technology to empower people
to create a sustainable, post-carbon world—one in which we no longer depend on
burning fossil fuels for a high quality of life.
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Dipanjan Sarkar is a data engineer at DataWeave, one of India's top Big Data
analytics start-ups, where he works on data semantics, information extraction, natural
language processing, and machine learning. Prior to joining DataWeave, he worked
as a graduate technical intern at Intel and received a Master's degree in Information
Technology from the International Institute of Information Technology, Bangalore.
Dipanjan is a technology enthusiast and loves Python and the start-up culture.
Dr. Makhan Virdi is a researcher at the Oak Ridge National Laboratory. He
received his PhD from the University of South Florida in 2013. His current research
interests include management and visualization of geospatial and time series data
from satellite imagery for biogeochemical dynamics.
Dr. Virdi is also an independent researcher with a passion for using embedded
electronics, robotics, and knowledge discovery to create machine augmented
intelligence systems. In his spare time, he works on robots, ambient intelligence, and
smart homes. You can read more about his research at TheXLabs.com.
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Table of Contents
Preface 1
Chapter 1: The R Environment 7
Installing R and using the command line 7
Downloading R 8
Installing R 8
Using R as a calculator 9
Coding with R beyond the command line 11
Approaches to editing R code 11
Installation of RStudio 14
Using RStudio 14
Evaluating expressions 15
Using arithmetic and logical operators 15
Using functions 18
Dealing with warning and error messages 20
Getting help 21
Exploring the basic object types in R 21
Everything is an object 22
Storing data in data structures 23
Calling functions to perform operations 23
A short sample session 23
Summary 25
Chapter 2: Working with Vectors and Time Series 27
Vectors – the basic data structures in R 28
Different types of vectors 28
Using the assignment operator to save an object 30
Removing objects from memory 32
Summarizing vector properties 32
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