Spatial Data Analysis in Ecology and Agriculture Using R
1st Edition
Published on 7. March 2012
648 pages
978-1-4398-1914-2 (ISBN)
Description
Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology and agriculture. Written in terms of four data sets easily accessible online, this book guides the reader through the analysis of
Reviews / Votes
Professor Plant presents an excellent treatise on applied (computational) spatial problems using R, and I personally thank him for this painstaking enterprise. ... Despite the availability of several nice text-books on spatial statistics covering a wide variety of topics, there was a dearth of such books mainly catered toward ecologists and agricultural scientists interested in applied exploration of spatially referenced data. Professor Plant fills this void! ...Written in a lucid language, the author did a fabulous job in properly sequencing the concept development. ... I can certainly say with confidence that this book is expected to enjoy a long shelf life. If you want to get your hands dirty with some applied spatial data analysis, I highly recommend buying it.-Dipankar Bandyopadhyay, Journal of Agricultural, Biological, and Environmental Statistics, October 2012
More details
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Illustrations
165 s/w Abbildungen, 28 s/w Tabellen
165 b/w images and 28 tables
ISBN-13
978-1-4398-1914-2 (9781439819142)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Richard E. Plant
Spatial Data Analysis in Ecology and Agriculture Using R
Book
04/2012
CRC Press
€89.12
Article exhausted; check for reprint
Content
Working with Spatial Data. The R Programming Environment. Statistical Properties of Spatially Autocorrelated Data. Measures of Spatial Autocorrelation. Sampling and Data Collection. Preparing Spatial Data for Analysis. Preliminary Exploration of Spatial Data. Multivariate Methods for Spatial Data Exploration. Spatial Data Exploration via Multiple Regression. Variance Estimation, the Effective Sample Size, and the Bootstrap. Measures of Bivariate Association between Two Spatial Variables. The Mixed Model. Regression Models for Spatially Autocorrelated Data. Bayesian Analysis of Spatially Autocorrelated Data. Analysis of Spatiotemporal Data. Analysis of Data from Controlled Experiments. Assembling Conclusions.
