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Hierarchical Spatio Temporal Models For Ecological Processes
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Book Synopsis Hierarchical Spatio-temporal Models for Ecological Processes by : Mevin B. Hooten
Download or read book Hierarchical Spatio-temporal Models for Ecological Processes written by Mevin B. Hooten and published by . This book was released on 2006 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecosystems are composed of phenomena that propagate in time and space. Often, ecological processes underlying such phenomena are studied separably in various subdisciplines, while larger scale, interlinking mechanisms are overlooked or only speculated about. As grows the burden of global climate change and human disturbance of natural systems, so grows the need for rigorous statistical methods focused on characterizing and forecasting large-scale spatio-temporal environmental and ecological processes in the presence of limited data and multiple sources of uncertainty. Hierarchical models offer a powerful means with which to study complex phenomena in space and time. This dissertation develops and illustrates the utility of spatio-temporal hierarchical models for studying ecological phenomena.
Book Synopsis Hierarchical Spatio-temporal Models for Environmental Processes by : Ali Arab
Download or read book Hierarchical Spatio-temporal Models for Environmental Processes written by Ali Arab and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The processes governing environmental systems are often complex, involving different interacting scales of variability in space and time. The complexities and often high dimensionality of such spatio-temporal processes can be effectively addressed using a hierarchical modeling framework where a complex problem is decomposed into a series of simpler problems that are linked through rules of probability. In this dissertation, hierarchical spatio-temporal models are developed and utilized for environmental processes. The methods discussed in this dissertation include a wide scope of problems related to the modeling of spatio-temporal environmental processes. Specifically, methods are described for efficient modeling of spatio-temporal environmental processes using both discrete- and continuous valued data.
Book Synopsis Spatio-Temporal Methods in Environmental Epidemiology by : Gavin Shaddick
Download or read book Spatio-Temporal Methods in Environmental Epidemiology written by Gavin Shaddick and published by CRC Press. This book was released on 2015-06-17 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and
Book Synopsis Spatio-Temporal Models for Ecologists by : James Thorson
Download or read book Spatio-Temporal Models for Ecologists written by James Thorson and published by CRC Press. This book was released on 2024-02-27 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecological dynamics are tremendously complicated and are studied at a variety of spatial and temporal scales. Ecologists often simplify analysis by describing changes in density of individuals across a landscape, and statistical methods are advancing rapidly for studying spatio-temporal dynamics. However, spatio-temporal statistics is often presented using a set of principles that may seem very distant from ecological theory or practice. This book seeks to introduce a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics. We provide a step-by-step illustration of techniques that combine core spatial-analysis packages in R with low-level computation using Template Model Builder. Techniques are showcased using real-world data from varied ecological systems, providing a toolset for hierarchical modelling of spatio-temporal processes. Spatio-Temporal Models for Ecologists is meant for graduate level students, alongside applied and academic ecologists. Key Features: Foundational ecological principles and analyses Thoughtful and thorough ecological examples Analyses conducted using a minimal toolbox and fast computation Code using R and TMB included in the book and available online
Book Synopsis Spatio-temporal Models for Ecologists by : James T. Thorson
Download or read book Spatio-temporal Models for Ecologists written by James T. Thorson and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Ecological dynamics are tremendously complicated and are studied at a variety of spatial and temporal scales. Ecologists often simplify analysis by describing changes in density of individuals across a landscape, and statistical methods are advancing rapidly for studying spatio-temporal dynamics. However, spatio-temporal statistics is often presented using a set of principles that may seem very distant from ecological theory or practice. This book seeks to introduce a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics. We provide a step-by-step illustration of techniques that combine core spatial-analysis packages in R with low-level computation using Template Model Builder. Techniques are showcased using real-world data from varied ecological systems, providing a toolset for hierarchical modelling of spatio-temporal processes. Spatio-Temporal Models for Ecologists is meant for graduate level students, alongside applied and academic ecologists"--
Book Synopsis Hierarchical Modelling for the Environmental Sciences by : James Samuel Clark
Download or read book Hierarchical Modelling for the Environmental Sciences written by James Samuel Clark and published by Oxford University Press, USA. This book was released on 2006 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.
Book Synopsis Hierarchical Modeling and Analysis for Spatial Data by : Sudipto Banerjee
Download or read book Hierarchical Modeling and Analysis for Spatial Data written by Sudipto Banerjee and published by CRC Press. This book was released on 2003-12-17 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,
Book Synopsis Applying Graph Theory in Ecological Research by : Mark R.T. Dale
Download or read book Applying Graph Theory in Ecological Research written by Mark R.T. Dale and published by Cambridge University Press. This book was released on 2017-11-09 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book clearly describes the many applications of graph theory to ecological questions, providing instruction and encouragement to researchers.
Book Synopsis Land-use Regression and Spatio-temporal Hierarchical Models for Environmental Processes by : Sara Zapata-Marin
Download or read book Land-use Regression and Spatio-temporal Hierarchical Models for Environmental Processes written by Sara Zapata-Marin and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Land-use regression is a popular method used to describe the spatial variability of different environmental processes using local variables. However, there are situations in which there might be some complex spatio-temporal structure left after accounting for land-use variables.In this work, three different Bayesian hierarchical models are proposed to model the spatial and spatio-temporal dispersion of air pollutants and aeroallergens within cities. Bayesian inference can easily accommodate complex interactions while naturally accounting for uncertainties in the estimation of unknowns in the model when performing predictions.In the first study, a spatial hierarchical model is used to analyze the concentration of volatile organic compounds (VOCs) in Montreal, Canada. The data consists of concentration measurements of five VOCs measured over two-week periods for three monitoring campaigns between 2005 and 2006 over 130 locations in the city. The five VOCs of interest are: benzene, decane, ethylbenzene, hexane, and trimethylbenzene. Four different models are fitted to each of the five VOCs. These models extend land-use regression by accounting for any spatial structure left after including the covariates while also capturing the across campaign variation through an indicator variable or campaign-specific coefficients. Predicted surfaces are obtained for each campaign. For all VOCs higher levels are found during the December campaign, and the predicted areas with the highest levels correspond to multiple sections of major highways.For the second and third studies, we have available data on the daily and weekly measurements of pollen concentration in Toronto, Canada collected in 2018. The measurements consist of tree, weed, grass, and total pollen concentration at 18 monitoring sites and were obtained daily for eleven of these sites and weekly for the other seven sites.In the second study, the weekly concentration of each of the four pollen types is modeled. Instead of considering the temporal window that only has positive values, that is, removing the zeros, a hurdle model is proposed to account for the high number of measurements equal to zero. This structure allows for the estimation of the probability of the pollen concentration being equal to zero at any given week, which provides further information on temporal windows with positive concentrations of the different types of pollen. Additionally, a dynamic linear model is used to capture the weekly trend of pollen concentration in the city.In the third study, the daily concentration of total pollen is modeled. Rather than aggregating the data to the weekly scale, a temporal misalignment model is proposed to account for the difference in scale and to take advantage of the daily measurements. Using the properties of dynamic linear models and the multivariate normal distribution, a spatio-temporal model to account for temporal misalignment is proposed. This model allows to estimate the fine-scale measurements at locations where only coarse-scale observations were available. Additionally, the model is fitted to artificial data with different temporal structures, including trend and seasonality.The predicted surfaces obtained in these three studies will help inform future health-related studies. Furthermore, the methods proposed here are flexible, easily adaptable, and can improve our understanding of similar environmental processes. All codes are publicly available such that the implementation of the proposed approach in similar situations is easily achieved"--
Book Synopsis Hierarchical Modeling and Analysis for Spatial Data by : Sudipto Banerjee
Download or read book Hierarchical Modeling and Analysis for Spatial Data written by Sudipto Banerjee and published by CRC Press. This book was released on 2003-12-17 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,
Book Synopsis Spatio-temporal Models with Time-varying Spatial Model Error for Environmental Processes by : Daniel Gladish
Download or read book Spatio-temporal Models with Time-varying Spatial Model Error for Environmental Processes written by Daniel Gladish and published by . This book was released on 2013 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Environmental processes exhibit uncertainty in the spatial and temporal domains. Often, mechanistic forecast models, such as weather forecasting systems, may not necessarily match the observed data, resulting in the need for a stochastic error term. This is common in the context of data assimilation, where one seeks to blend observations and mechanistic (deterministic) models to create complete spatio-temporal fields and their uncertainty. The observation and state-process error covariances play important roles in the development and implementation of such data assimilation models. However, the mechanistic models in this framework depend on approximations that may fail under certain real-world conditions. These models may be inadequate for various reasons, such as the need for parameters to vary through time and/or space, incomplete knowledge of the process, improper assumptions, such as temporal stationarity, or incomplete data. As such, spatio-temporal structure may exist in the resulting misfit error process. Thus, the evolution of spatial covariances may be nonstationary in time. Because of the complexities involved in characterizing these error processes, Bayesian hierarchical models (BHMs) provide an appropriate framework for modeling time-varying covariances through appropriate decompositions of the spatial covariance matrix. In this dissertation, we develop models that account for the evolution of time-varying covariance matrices in dynamical spatio-temporal models.
Book Synopsis Statistics for Spatio-Temporal Data by : Noel Cressie
Download or read book Statistics for Spatio-Temporal Data written by Noel Cressie and published by John Wiley & Sons. This book was released on 2015-11-02 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winners of the 2011 PROSE Award in the Mathematics category, for the book “Statistics for Spatio-Temporal Data” (2011), published by John Wiley and Sons. (The PROSE awards, for Professional and Scholarly Excellence, are given by the Association of American Publishers, the national trade association of the US book publishing industry.) Statistics for Spatio-Temporal Data has now been reprinted with small corrections to the text and the bibliography. The overall content and pagination of the new printing remains the same; the difference comes in the form of corrections to typographical errors, editing of incomplete and missing references, and some updated spatio-temporal interpretations. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. Statistics for Spatio-Temporal Data presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models. Cressie and Wikle supply a unique presentation that incorporates ideas from the areas of time series and spatial statistics as well as stochastic processes. Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, including visualization, spectral analysis, empirical orthogonal function analysis, and LISAs Spatio-temporal covariance functions, spatio-temporal kriging, and time series of spatial processes Development of hierarchical dynamical spatio-temporal models (DSTMs), with discussion of linear and nonlinear DSTMs and computational algorithms for their implementation Quantifying and exploring spatio-temporal variability in scientific applications, including case studies based on real-world environmental data Throughout the book, interesting applications demonstrate the relevance of the presented concepts. Vivid, full-color graphics emphasize the visual nature of the topic, and a related FTP site contains supplementary material. Statistics for Spatio-Temporal Data is an excellent book for a graduate-level course on spatio-temporal statistics. It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
Book Synopsis Fundamentals of Ecological Modelling by : Sven Erik Jørgensen
Download or read book Fundamentals of Ecological Modelling written by Sven Erik Jørgensen and published by Elsevier. This book was released on 2001 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cover -- Contents -- Preface -- Acknowledgements -- Chapter 1. Introduction -- 1.1 Physical and Mathematical Models -- 1.2 Models as a Management Tool -- 1.3 Models as a Scientific Tool -- 1.4 Models and Holism -- 1.5 The Ecosystem as an Object for Research -- 1.6 Outline of the Book -- 1.7 The Development of Ecological and Environmental Models -- 1.8 State of the Art in the Application of Models -- Chapter 2. Concepts of Modelling -- 2.1 Introduction -- 2.2 Modelling Elements -- 2.3 The Modelling Procedure -- 2.4 Types of Model -- 2.5 Selection of Model Type -- 2.6 Selection of Model Complexity and Structure -- 2.7 Verification -- 2.8 Sensitivity Analysis -- 2.9 Parameter Estimation -- 2.10 Validation -- 2.11 Ecological Modelling and Quantum Theory -- 2.12 Modelling Constraints -- Problems -- Chapter 3. Ecological Processes -- 3A.1 Space and Time Resolution -- 3A.2 Mass Transport -- 3A.3 Mass Balance -- 3A.4 Energetic Factors -- 3A.5 Settling and Resuspension -- 3B.1 Chemical Reaction ...
Book Synopsis Models for Ecological Data by : James S. Clark
Download or read book Models for Ecological Data written by James S. Clark and published by Princeton University Press. This book was released on 2020-10-06 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Lab manual in R is available separately
Book Synopsis Joint Species Distribution Modelling by : Otso Ovaskainen
Download or read book Joint Species Distribution Modelling written by Otso Ovaskainen and published by Cambridge University Press. This book was released on 2020-06-11 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.
Book Synopsis Bringing Bayesian Models to Life by : Mevin B. Hooten
Download or read book Bringing Bayesian Models to Life written by Mevin B. Hooten and published by CRC Press. This book was released on 2019-05-15 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features: R code implementing algorithms to fit Bayesian models using real and simulated data examples. A comprehensive review of statistical models commonly used in ecological and environmental science. Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC. Derivations of the necessary components to construct statistical algorithms from scratch. Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.
Book Synopsis Hierarchical Modeling of Spatio-temporally Misaligned Data by : Li Zhu
Download or read book Hierarchical Modeling of Spatio-temporally Misaligned Data written by Li Zhu and published by . This book was released on 2000 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
