Richard E. Plant

Spatial Data Analysis in Ecology and Agriculture Using R (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The book provides instruction on using R programming language to analyze spatial data from research in ecology, agriculture, and environmental science. It guides readers in analyzing data sets, setting research objectives, designing sampling plans, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 684
• Erscheinungstermin: 7. Dezember 2018
• Englisch
• ISBN-13: 9781351189897
• Artikelnr.: 55170690

Autorenporträt

Richard E. Plant received his Ph.D. in Theoretical and Applied Mechanics from Cornell University in 1975. After receiving his Ph.D., he joined the Mathematics Department at the University of California, Davis. Recognizing the opportunity to work with researchers at one of the world's leading agricultural institutions, he transferred his research effort from nerve membrane modeling to problems in agriculture.

He has received awards from the Division of Agriculture and Natural Resources of the University of California and hte American Society of Agronomy for his work in the application of academic research methods to the resolution of important agricultural problems. He was awarded a Fulbright Fellowship to carry out research in rice production in Uruguay, some of which serves as one of the case studies in his book. He is a Professor Emeritus of Biological and Agricultural Engineering and Plant Sciences at the University of California, Davis. His research interests include the application of systems analysis, geographic information systems, and statistical models to landscape level problems in crop production and resource management.

Inhaltsangabe

Working with Spatial Data. 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. Using Non-Spatial
Methods to Explore Spatial Data. Variance Estimation, the Effective Sample
Size, and the Bootstrap. Measures of Bivariate Association between Two
Spatial Variables. 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. Appendices. Review of Mathematical
Concepts. The Data Sets. An R Thesaurus. References.