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Displaying Time Series Spatial And Space Time Data With R

Author: Oscar Perpinan Lamigueiro
Publisher: CRC Press
ISBN: 1466565209
Size: 48.92 MB
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Code and Methods for Creating High-Quality Data Graphics A data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets. Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R presents methods and R code for producing high-quality graphics of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code. The book illustrates how to display a dataset starting with an easy and direct approach and progressively adding improvements that involve more complexity. Each of the book’s three parts is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics. Web Resource Along with the main graphics from the text, the author’s website offers access to the datasets used in the examples as well as the full R code. This combination of freely available code and data enables you to practice with the methods and modify the code to suit your own needs.

R Graphics Third Edition

Author: Paul Murrell
Publisher: CRC Press
ISBN: 0429749953
Size: 24.68 MB
Format: PDF, ePub
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This third edition of Paul Murrell’s classic book on using R for graphics represents a major update, with a complete overhaul in focus and scope. It focuses primarily on the two core graphics packages in R - graphics and grid - and has a new section on integrating graphics. This section includes three new chapters: importing external images in to R; integrating the graphics and grid systems; and advanced SVG graphics. The emphasis in this third edition is on having the ability to produce detailed and customised graphics in a wide variety of formats, on being able to share and reuse those graphics, and on being able to integrate graphics from multiple systems. This book is aimed at all levels of R users. For people who are new to R, this book provides an overview of the graphics facilities, which is useful for understanding what to expect from R's graphics functions and how to modify or add to the output they produce. For intermediate-level R users, this book provides all of the information necessary to perform sophisticated customizations of plots produced in R. For advanced R users, this book contains vital information for producing coherent, reusable, and extensible graphics functions.

Bayesian Disease Mapping

Author: Andrew B. Lawson
Publisher: CRC Press
ISBN: 146650482X
Size: 80.13 MB
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Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets. New to the Second Edition Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance Expanded material on case event modeling and spatiotemporal analysis New and updated examples Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.

Spatial Data Quality

Author: Rodolphe Devillers
Publisher: CRC
ISBN:
Size: 67.85 MB
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Producing maps that depict the real world accurately has been a major concern of cartographers for centuries. This is especially true today as escalating access to geospatial data and the subsequent increase in user-generated content provided by Web 2.0 have significantly altered the typical processes used to produce, distribute, and use geospatial data. Focusing on users and decisions as well as the data, Spatial Data Quality: From Process to Decisionsprovides an up-to-date overview of scientific progress in this core sub-discipline of the Geographic Information Sciences. Presenting results from a number of current research projects in spatial data quality (SDQ) ” from the assessment of data accuracy to legal aspects relating to the quality of geographic information ” this reference reflects the changes in practice in response to the rapid technological developments over the past decade. An impressive panel of internationally recognized expert contributors focuses on the relationship between the quality of geographic data and the quality of decisions based on such data. Structured for easy reference, the first section of the book discusses conceptual approaches to SDQ, the second presents a number of applications of spatial data quality methods, the third looks at SDQ issues for remote sensing data, and the final section presents papers that consider the interface between the law and SDQ. In addition to the main chapters presented in each section, a number of shorter notes present on-going and recent research projects investigating various aspects of spatial data quality.