FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: Geographic Data Science with R: Visualizing and Analyzing Environmental Change
 Author(s): Michael C. Wimberly
 Publisher: Chapman and Hall/CRC; 1st edition (May 8, 2023); eBook (Creative Commons Licensed)
 License(s): Creative Commons License (CC)
 Paperback/Hardcover: 284 pages
 eBook: HTML
 Language: English
 ISBN10: 1032347716
 ISBN13: 9781032347714
 Share This:
Book Description
This book provides a series of tutorials aimed at teaching good practices for using Time Series and geospatial data to address topics related to environmental change. It is based on the R language and environment, which currently provides the best option for working with diverse sources of spatial and nonspatial data using a single platform.
About the Authors Dr. Michael Wimberly is a Professor in the Department of Geography and Environmental Sustainability at the University of Oklahoma.
 Geographic Information Science and Systems (GIS), Spatial Analysis, etc.
 Data Science
 The R Programming Language
 Data Analysis and Data Mining
 Statistics, and SAS Programming
 Geographic Data Science with R: Visualizing and Analyzing Environmental Change
 The Mirror Site (1)  PDF

R for Geographic Data Science (Stefano De Sabbata)
This book introduces readers to tools in the R programming language for accessing and analyzing Census data from the United States Census Bureau and shows how to carry out demographic analyses in a single computing environment.

Spatial Data Science: With Applications in R
Introduces and explains the concepts underlying Spatial Data, 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.

Geographic Data Science with Python (Sergio Rey, et al.)
Provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data, by using geographical and computational reasoning to unlock new insights hidden within data.

Geocomputation with R (Robin Lovelace, et al.)
This book is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities.

R for Data Science: Visualize, Model, Transform, Tidy, Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

R Graphics Cookbook: Practical Recipes for Visualizing Data
This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate highquality graphs quickly  without having to comb through all the details of R's graphing systems.

Geospatial Analysis with Python (Ujaval Gandhi)
Suitable for GIS practitioners with no programming background or python knowledge. The course will introduce basic Python programming concepts, libraries for spatial analysis, geospatial APIs and techniques for building spatial data processing pipelines.

Python Scripting for Spatial Data Processing (Pete Bunting, et al)
This book is a Python tutorial for beginners aiming at teaching spatial data processing. It is used as part of the courses taught in Remote Sensing and GIS, using psycopg2, and ogr2ogr, etc., at Aberystwyth University, UK.

Geospatial Analysis: Principles, Techniques and Software Tools
This book provides a compehensive guide to concepts, methods and tools, with many examples being provided using a variety of software tools such as ArcGIS, Idrisi, Grass, Surfer and many others to clarify the concepts discussed

Efficient R Programming: Practical Guide to Smarter Programming
This book is about increasing the amount of work you can do with R in a given amount of time. It's about both computational and programmer efficiency. This book is for anyone who wants to make their use of R more reproducible, scalable, and faster.

Cookbook for R: Best R Programming TIPs (Winston Chang)
The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

HandsOn Programming with R: Functions and Simulations
This book not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.

R Programming for Data Science (Roger D. Peng)
This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

R Packages: Organize, Test, Document, and Share Your Code
Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.

Forecasting, Principles and Practice, Using R (R. J. Hyndman)
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.
:






















