|Author||: Jiaping Wu,Junyu He,George Christakos|
|Release Date||: 2020-12-15|
|ISBN 10||: 9780128163412|
|Pages||: 420 pages|
Quantitative Analysis and Modeling of Earth and Environmental Data: Applications for Spatial and Temporal Variation offers a systematic, quantitative analysis of multi-sourced data, including the spatial distribution and temporal dynamics of natural attributes. It covers data handling techniques that may vary by space and/or time, and aims to improve understanding of physical laws of change underlying available numerical datasets, while also considering in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). Featuring real-world practical applications and practice exercises, this book is a comprehensive step-by-step tutorial of data-driven techniques that will help students and researchers master data analysis in earth and environmental sciences. The notions and methods presented in the book cover a wide range of data in various forms and sources, including hard measurements, soft observations, secondary information and auxiliary variables (ground-level measurements, satellite observations, scientific instruments and records, protocols and surveys, empirical models and charts). Addresses the analysis and processing data that varies spatially and/or temporally, which is the case with the majority of data in scientific and engineering disciplines Covers a wide range of data describing a variety of attributes characterizing physical phenomena and systems including earth, ocean and atmospheric variables, environmental and ecological parameters, population health states, disease indicators, and social and economic characteristics Includes case studies and practice exercises at the end of each chapter for both real-world applications and deeper understanding of the concepts presented
|Author||: Douglas G. Martinson|
|Publisher||: Cambridge University Press|
|Release Date||: 2018-09-30|
|ISBN 10||: 1107029767|
|Pages||: 598 pages|
Provides thorough and comprehensive coverage of new and important quantitative methods in data science, for graduate students and practitioners.
Master the fundamental math skills necessary to quantify andevaluate a broad range of environmental questions. Environmental issues are often quantitative--how much land, howmany people, what amount of pollution. Computer programs areuseful, but there is no substitute for being able to use a simplecalculation to slice through to the crux of the problem. Having agrasp of how the factors interact and whether the results makesense allows one to explain and argue a point of view forcefully todiverse audiences. With an engaging, down-to-earth style and practical problem-solvingapproach, Ecological Numeracy makes it easy to understand andmaster basic mathematical concepts and techniques that areapplicable to life-cycle assessment, energy consumption, land use,pollution generation, and a broad range of other environmentalissues. Robert Herendeen brings the numbers to life with dozens offascinating, often entertaining examples and problems. Requiring only a moderate quantitative background, EcologicalNumeracy is a superb introduction for advanced undergraduatestudents in environmental science, planning, geography, andphysical and natural sciences. It is also a valuable professionalresource for environmental managers, regulators, andadministrators.
|Author||: Hamid Reza Pourghasemi,Candan Gokceoglu|
|Release Date||: 2019-01-18|
|ISBN 10||: 0128156953|
|Pages||: 798 pages|
Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography Provides an overview, methods and case studies for each application Expresses concepts and methods at an appropriate level for both students and new users to learn by example
Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. Contents: Preface Time series analysis Chaos and dynamical systems Approximation Interpolation Statistical methods Numerical methods Optimization Data envelopment analysis Risk assessments Life cycle assessments Index
Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. Author's website: http://www.ldeo.columbia.edu/users/menke/edawm/index.htm Well written and outlines a clear learning path for researchers and students Uses real world environmental examples and case studies MatLab software for application in a readily-available software environment Homework problems help user follow up upon case studies with homework that expands them
|Author||: Adrian Burd|
|Publisher||: Cambridge University Press|
|Release Date||: 2019-04-30|
|ISBN 10||: 1107117488|
|Pages||: 550 pages|
An accessible introduction to the mathematical methods essential for understanding processes in the Earth and environmental sciences.
A comprehensive treatment of statistical applications for solvingreal-world environmental problems A host of complex problems face today's earth science community,such as evaluating the supply of remaining non-renewable energyresources, assessing the impact of people on the environment,understanding climate change, and managing the use of water. Propercollection and analysis of data using statistical techniquescontributes significantly toward the solution of these problems.Statistics for Earth and Environmental Scientists presentsimportant statistical concepts through data analytic tools andshows readers how to apply them to real-world problems. The authors present several different statistical approaches tothe environmental sciences, including Bayesian and nonparametricmethodologies. The book begins with an introduction to types ofdata, evaluation of data, modeling and estimation, randomvariation, and sampling—all of which are explored throughcase studies that use real data from earth science applications.Subsequent chapters focus on principles of modeling and the keymethods and techniques for analyzing scientific data,including: Interval estimation and Methods for analyzinghypothesis testingof means time series data Spatial statistics Multivariate analysis Discrete distributions Experimental design Most statistical models are introduced by concept andapplication, given as equations, and then accompanied by heuristicjustification rather than a formal proof. Data analysis, modelbuilding, and statistical inference are stressed throughout, andreaders are encouraged to collect their own data to incorporateinto the exercises at the end of each chapter. Most data sets,graphs, and analyses are computed using R, but can be worked withusing any statistical computing software. A related websitefeatures additional data sets, answers to selected exercises, and Rcode for the book's examples. Statistics for Earth and Environmental Scientists is anexcellent book for courses on quantitative methods in geology,geography, natural resources, and environmental sciences at theupper-undergraduate and graduate levels. It is also a valuablereference for earth scientists, geologists, hydrologists, andenvironmental statisticians who collect and analyze data in theireveryday work.
|Author||: Marina G Erechtchoukova,Peter A Khaiter,Paulina Golinska|
|Publisher||: Springer Science & Business Media|
|Release Date||: 2013-03-14|
|ISBN 10||: 3642320813|
|Pages||: 254 pages|
One of the most important issues in developing sustainable management strategies and incorporating ecodesigns in production, manufacturing and operations management is the assessment of the sustainability of business operations and organizations’ overall environmental performance. The book presents the results of recent studies on sustainability assessment. It provides a solid reference for researchers in academia and industrial practitioners on the state-of-the-art in sustainability appraisal including the development and application of sustainability indices, quantitative methods, models and frameworks for the evaluation of current and future welfare outcomes, recommendations on data collection and processing for the evaluation of organizations’ environmental performance, and eco-efficiency approaches leading to business process re-engineering.
|Author||: Y. Z. Ma|
|Release Date||: 2019-07-15|
|ISBN 10||: 3030178609|
|Pages||: 640 pages|
Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.
Environmental statistics is a rapidly growing field, supported by advances in digital computing power, automated data collection systems, and interactive, linkable Internet software. Concerns over public and ecological health and the continuing need to support environmental policy-making and regulation have driven a concurrent explosion in environmental data analysis. This textbook is designed to address the need for trained professionals in this area. The book is based on a course which the authors have taught for many years, and prepares students for careers in environmental analysis centered on statistics and allied quantitative methods of data evaluation. The text extends beyond the introductory level, allowing students and environmental science practitioners to develop the expertise to design and perform sophisticated environmental data analyses. In particular, it: Provides a coherent introduction to intermediate and advanced methods for modeling and analyzing environmental data. Takes a data-oriented approach to describing the various methods. Illustrates the methods with real-world examples Features extensive exercises, enabling use as a course text. Includes examples of SAS computer code for implementation of the statistical methods. Connects to a Web site featuring solutions to exercises, extra computer code, and additional material. Serves as an overview of methods for analyzing environmental data, enabling use as a reference text for environmental science professionals. Graduate students of statistics studying environmental data analysis will find this invaluable as will practicing data analysts and environmental scientists including specialists in atmospheric science, biology and biomedicine, chemistry, ecology, environmental health, geography, and geology.