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Hierarchical Spatio Temporal Models For Environmental Processes
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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 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 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 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 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 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 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 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 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:
Book Synopsis Statistical Analysis of Environmental Space-Time Processes by : Nhu D. Le
Download or read book Statistical Analysis of Environmental Space-Time Processes written by Nhu D. Le and published by Springer Science & Business Media. This book was released on 2006-09-13 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad introduction to the subject of environmental space-time processes, addressing the role of uncertainty. It covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. In particular, with members of their research group the authors developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development.
Book Synopsis Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height by : Erik Vanem
Download or read book Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height written by Erik Vanem and published by Springer Science & Business Media. This book was released on 2013-10-11 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence structures and uncertainties in the data. This monograph is a research book and it is partly cross-disciplinary. The methodology itself is firmly rooted in the statistical research tradition, based on probability theory and stochastic processes. However, that methodology has been applied to a problem in the field of physical oceanography, analyzing data for significant wave height, which is of crucial importance to ocean engineering disciplines. Indeed, the statistical properties of significant wave height are important for the design, construction and operation of ships and other marine and coastal structures. Furthermore, the book addresses the question of whether climate change has an effect of the ocean wave climate, and if so what that effect might be. Thus, this book is an important contribution to the ongoing debate on climate change, its implications and how to adapt to a changing climate, with a particular focus on the maritime industries and the marine environment. This book should be of value to anyone with an interest in the statistical modelling of environmental processes, and in particular to those with an interest in the ocean wave climate. It is written on a level that should be understandable to everyone with a basic background in statistics or elementary mathematics, and an introduction to some basic concepts is provided in the appendices for the uninitiated reader. The intended readership includes students and professionals involved in statistics, oceanography, ocean engineering, environmental research, climate sciences and risk assessment. Moreover, the book’s findings are relevant for various stakeholders in the maritime industries such as design offices, classification societies, ship owners, yards and operators, flag states and intergovernmental agencies such as the IMO.
Book Synopsis Hierarchical Modelling for the Environmental Sciences by : James S. Clark
Download or read book Hierarchical Modelling for the Environmental Sciences written by James S. Clark and published by OUP Oxford. This book was released on 2006-05-04 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 Spatio-temporal Modeling of Environmental and Health Processes by :
Download or read book Spatio-temporal Modeling of Environmental and Health Processes written by and published by . This book was released on 2008 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Spatio-Temporal Statistics with R by : Christopher K. Wikle
Download or read book Spatio-Temporal Statistics with R written by Christopher K. Wikle and published by CRC Press. This book was released on 2019-02-18 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.
Book Synopsis Multivariate Spatial-Temporal Modeling of Environmental-Health Processes by :
Download or read book Multivariate Spatial-Temporal Modeling of Environmental-Health Processes written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications in environmental sciences and epidemiology, data are often collected over space and time. In some cases, the spatial-temporal data of interest are multivariate, and these multivariate spatial-temporal processes often have a complicated dependency structure. Hence, multivariate spatial-temporal modeling is a very challenging task. In this study, we develop statistical models to effectively account for multivariate spatial-temporal dependency structures of air pollution concentrations and human health outcomes. Fine particulate matter (PM2.5) is an atmospheric pollutant that has been linked to serious health problems, including mortality. PM2.5 has five main components: sulfate, nitrate, total carbonaceous mass, ammonium, and crustal material. These components have complex spatial-temporal dependency and cross dependency structures. It is important to gain better understanding about the spatial-temporal distribution of each component of the total PM2.5 mass, and also to estimate how the composition of PM2.5 changes with space and time. We introduce a multivariate spatial-temporal model for speciated PM2.5. Our hierarchical framework combines different sources of data and accounts for potential bias. In addition, a spatiotemporal extension of the linear model of coregionalization is developed to account for spatial and temporal dependency structures for each component as well as the associations among the components. We apply our framework to speciated PM2.5 data in the United States for the year 2004. In addition, the chemical composition of PM2.5 varies across space and time so the association between PM2.5 and mortality could change with space and season. Thus, we develop and implement a multi-stage Bayesian framework that provides a very broad and flexible approach to studying the spatial-temporal associations between mortality and population exposure to daily PM2.5 mass, while accounting for different sources of uncertainty. In the first stage.
