Environmental Data Examples
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Environmental Data Analysis
Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg.
Environmental Data Management
Author: Carl Oppenheimer
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Throughout the world a staggering amount of resources have been used to obtain billions of environmental data points. Some, such as meteorological data, have been organized for weather map display where many thousands of data points are synthesized in one compressed map. Most environmental data, however, are still widely scattered and generally not used for a systems approach, but only for the purpose for which they were originally taken. These data are contained in relatively small computer programs, research files, government and industrial reports, etc. This Conference was called to bring together some of the world's leaders from research centers and government agencies, and others concerned with environmental data management. The purpose of the Conference was to organize discussion on the scope of world environmental data, its present form and documentation, and whether a systematic approach to a total system is feasible now or in the future. This same subject permeated indirectly the Stockholm Conference on the environment, where, although no single recommendation came forth suggesting a consolidated environmental data pool, bank or network, each recommendation indicated that substantial environmental data needed to be obtained or needed to be pooled and analyzed from existing data sources.
Spatial Linear Models for Environmental Data
Many applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data. Spatial Linear Models for Environmental Data, aimed at students and professionals with a master’s level training in statistics, presents a unique, applied, and thorough treatment of spatial linear models within a statistics framework. Two subfields, one called geostatistics and the other called areal or lattice models, are extensively covered. Zimmerman and Ver Hoef present topics clearly, using many examples and simulation studies to illustrate ideas. By mimicking their examples and R code, readers will be able to fit spatial linear models to their data and draw proper scientific conclusions. Topics covered include: Exploratory methods for spatial data including outlier detection, (semi)variograms, Moran’s I, and Geary’s c. Ordinary and generalized least squares regression methods and their application to spatial data. Suitable parametric models for the mean and covariance structure of geostatistical and areal data. Model-fitting, including inference methods for explanatory variables and likelihood-based methods for covariance parameters. Practical use of spatial linear models including prediction (kriging), spatial sampling, and spatial design of experiments for solving real world problems. All concepts are introduced in a natural order and illustrated throughout the book using four datasets. All analyses, tables, and figures are completely reproducible using open-source R code provided at a GitHub site. Exercises are given at the end of each chapter, with full solutions provided on an instructor’s FTP site supplied by the publisher.