Author : DICK J. BRUS
Publisher : Unknown
Page : 128 pages
File Size : 50,6 Mb
Release : 2022
Category : Electronic
ISBN : 1032193859
SPATIAL SAMPLING WITH R.
DICK J. BRUS
Spatial Sampling With R
Spatial Sampling With R Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Spatial Sampling With R book. This book definitely worth reading, it is an incredibly well-written.
Spatial Sampling with R
Dick J. Brus
Author : Dick J. Brus
Publisher : CRC Press
Page : 582 pages
File Size : 44,8 Mb
Release : 2022-09-26
Category : Mathematics
ISBN : 9781000600056
Spatial Sampling with R by Dick J. Brus Pdf
Scientific research often starts with data collection. However, many researchers pay insufficient attention to this first step in their research. The author, researcher at Wageningen University and Research, often had to conclude that the data collected by fellow researchers were suboptimal, or in some cases even unsuitable for their aim. One reason is that sampling is frequently overlooked in statistics courses. Another reason is the lack of practical textbooks on sampling. Numerous books have been published on the statistical analysis and modelling of data using R, but to date no book has been published in this series on how these data can best be collected. This book fills this gap. Spatial Sampling with R presents an overview of sampling designs for spatial sample survey and monitoring. It shows how to implement the sampling designs and how to estimate (sub)population- and space-time parameters in R. Key features Describes classical, basic sampling designs for spatial survey, as well as recently developed, advanced sampling designs and estimators Presents probability sampling designs for estimating parameters for a (sub)population, as well as non-probability sampling designs for mapping Gives comprehensive overview of model-assisted estimators Covers Bayesian approach to sampling design Illustrates sampling designs with surveys of soil organic carbon, above-ground biomass, air temperature, opium poppy Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data Data and R code available on github Exercises added making the book suitable as a textbook for students The target group of this book are researchers and practitioners of sample surveys, as well as students in environmental, ecological, agricultural science or any other science in which knowledge about a population of interest is collected through spatial sampling. This book helps to implement proper sampling designs, tailored to their problems at hand, so that valuable data are collected that can be used to answer the research questions.
Applied Spatial Data Analysis with R
Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio
Author : Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio
Publisher : Springer Science & Business Media
Page : 414 pages
File Size : 54,9 Mb
Release : 2013-06-21
Category : Medical
ISBN : 9781461476184
Applied Spatial Data Analysis with R by Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio Pdf
Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.
Sampling Spatial Units for Agricultural Surveys
Roberto Benedetti,Federica Piersimoni,Paolo Postiglione
Author : Roberto Benedetti,Federica Piersimoni,Paolo Postiglione
Publisher : Springer
Page : 340 pages
File Size : 51,8 Mb
Release : 2015-03-20
Category : Business & Economics
ISBN : 9783662460085
Sampling Spatial Units for Agricultural Surveys by Roberto Benedetti,Federica Piersimoni,Paolo Postiglione Pdf
The research and its outcomes presented here focus on spatial sampling of agricultural resources. The authors introduce sampling designs and methods for producing accurate estimates of crop production for harvests across different regions and countries. With the help of real and simulated examples performed with the open-source software R, readers will learn about the different phases of spatial data collection. The agricultural data analyzed in this book help policymakers and market stakeholders to monitor the production of agricultural goods and its effects on environment and food safety.
Spatial Analysis with R
Tonny J. Oyana
Author : Tonny J. Oyana
Publisher : CRC Press
Page : 355 pages
File Size : 42,9 Mb
Release : 2020-08-31
Category : Mathematics
ISBN : 9781000173451
Spatial Analysis with R by Tonny J. Oyana Pdf
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data mining This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
Spatial Data Analysis in Ecology and Agriculture Using R
Richard E. Plant
Author : Richard E. Plant
Publisher : CRC Press
Page : 637 pages
File Size : 55,8 Mb
Release : 2012-03-07
Category : Mathematics
ISBN : 9781439819142
Spatial Data Analysis in Ecology and Agriculture Using R by Richard E. Plant Pdf
Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology and agriculture. Written in terms of four data sets easily accessible online, this book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Based on the author’s spatial data analysis course at the University of California, Davis, the book is intended for classroom use or self-study by graduate students and researchers in ecology, geography, and agricultural science with an interest in the analysis of spatial data.
R-Trees: Theory and Applications
Yannis Manolopoulos,Alexandros Nanopoulos,Apostolos N. Papadopoulos,Yannis Theodoridis
Author : Yannis Manolopoulos,Alexandros Nanopoulos,Apostolos N. Papadopoulos,Yannis Theodoridis
Publisher : Springer Science & Business Media
Page : 194 pages
File Size : 41,5 Mb
Release : 2010-09-08
Category : Computers
ISBN : 9781846282935
R-Trees: Theory and Applications by Yannis Manolopoulos,Alexandros Nanopoulos,Apostolos N. Papadopoulos,Yannis Theodoridis Pdf
Space support in databases poses new challenges in every part of a database management system & the capability of spatial support in the physical layer is considered very important. This has led to the design of spatial access methods to enable the effective & efficient management of spatial objects. R-trees have a simplicity of structure & together with their resemblance to the B-tree, allow developers to incorporate them easily into existing database management systems for the support of spatial query processing. This book provides an extensive survey of the R-tree evolution, studying the applicability of the structure & its variations to efficient query processing, accurate proposed cost models, & implementation issues like concurrency control and parallelism. Written for database researchers, designers & programmers as well as graduate students, this comprehensive monograph will be a welcome addition to the field.
Geocomputation with R
Robin Lovelace,Jakub Nowosad,Jannes Muenchow
Author : Robin Lovelace,Jakub Nowosad,Jannes Muenchow
Publisher : CRC Press
Page : 335 pages
File Size : 51,5 Mb
Release : 2019-03-22
Category : Mathematics
ISBN : 9781351396905
Geocomputation with R by Robin Lovelace,Jakub Nowosad,Jannes Muenchow Pdf
Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.
Spatial Econometric Methods in Agricultural Economics Using R
Paolo Postiglione,Roberto Benedetti,Federica Piersimoni
Author : Paolo Postiglione,Roberto Benedetti,Federica Piersimoni
Publisher : CRC Press
Page : 287 pages
File Size : 44,6 Mb
Release : 2021-12-22
Category : Technology & Engineering
ISBN : 9781498766838
Spatial Econometric Methods in Agricultural Economics Using R by Paolo Postiglione,Roberto Benedetti,Federica Piersimoni Pdf
Modern tools, such as GIS and remote sensing, are increasingly used in the monitoring of agricultural resources. The developments in GIS technology offer growing opportunities to agricultural economics analysts dealing with large and detailed spatial databases, allowing them to combine spatial information from different sources and to produce different models. The availability of these valuable sources of information makes the advanced models suggested in the spatial statistic and econometric literature applicable to agricultural economics. This book aims at supporting stakeholders to design spatial surveys for agricultural data and/or to analyse the geographically collected data. This book attempts to describe the main typology of agricultural data and the most appropriate methods for the analysis, together with a detailed description of the available data sources and their collection methods. Topics such as spatial interpolation, point patterns, spatial autocorrelation, survey data analysis, small area estimation, regional data modelling, and spatial econometrics techniques are covered jointly with issues arising from the integration of several data types. The theory of spatial methods is complemented by real and/or simulated examples implemented through the open-source software R.
Applied Spatial Statistics and Econometrics
Katarzyna Kopczewska
Author : Katarzyna Kopczewska
Publisher : Routledge
Page : 725 pages
File Size : 40,7 Mb
Release : 2020-11-25
Category : Business & Economics
ISBN : 9781000079784
Applied Spatial Statistics and Econometrics by Katarzyna Kopczewska Pdf
This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data.
Spatial Predictive Modeling with R
Jin Li
Author : Jin Li
Publisher : CRC Press
Page : 404 pages
File Size : 49,8 Mb
Release : 2022-02-22
Category : Mathematics
ISBN : 9781000542608
Spatial Predictive Modeling with R by Jin Li Pdf
Spatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant data, developing optimal predictive models based on point data, and then generating spatial predictions. This book aims to systematically introduce the entire process of SPM as a discipline. The process contains data acquisition, spatial predictive methods and variable selection, parameter optimization, accuracy assessment, and the generation and visualization of spatial predictions, where spatial predictive methods are from geostatistics, modern statistics, and machine learning. The key features of this book are: •Systematically introducing major components of SPM process. •Novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods. •Novel predictive accuracy-based variable selection techniques for spatial predictive methods. •Predictive accuracy-based parameter/model optimization. •Reproducible examples for SPM of various data types in R. This book provides guidelines, recommendations, and reproducible examples for developing optimal predictive models by considering various components and associated factors for quality-improved spatial predictions. It provides valuable tools for researchers, modelers, and university students not only in SPM field but also in other predictive modeling fields. Dr Li has produced over 100 various publications in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology, developed a number of hybrid methods for SPM, and published four R packages for variable selections as well as SPM.
Spatial Data Analysis in Ecology and Agriculture Using R
Richard E. Plant
Author : Richard E. Plant
Publisher : CRC Press
Page : 666 pages
File Size : 41,9 Mb
Release : 2018-12-07
Category : Science
ISBN : 9781351189903
Spatial Data Analysis in Ecology and Agriculture Using R by Richard E. Plant Pdf
Key features: Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data. Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods Updates its coverage of R software including newly introduced packages Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.
Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
Elias T. Krainski,Virgilio Gómez-Rubio,Haakon Bakka,Amanda Lenzi,Daniela Castro-Camilo,Daniel Simpson,Finn Lindgren,Håvard Rue
Author : Elias T. Krainski,Virgilio Gómez-Rubio,Haakon Bakka,Amanda Lenzi,Daniela Castro-Camilo,Daniel Simpson,Finn Lindgren,Håvard Rue
Publisher : CRC Press
Page : 284 pages
File Size : 41,9 Mb
Release : 2018-12-07
Category : Mathematics
ISBN : 9780429629853
Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA by Elias T. Krainski,Virgilio Gómez-Rubio,Haakon Bakka,Amanda Lenzi,Daniela Castro-Camilo,Daniel Simpson,Finn Lindgren,Håvard Rue Pdf
Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.
Resampling Methods for Dependent Data
S. N. Lahiri
Author : S. N. Lahiri
Publisher : Springer Science & Business Media
Page : 382 pages
File Size : 44,9 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475738032
Resampling Methods for Dependent Data by S. N. Lahiri Pdf
By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical examples throughout. The book fills a gap in the literature covering research on re-sampling methods for dependent data that has witnessed vigorous growth over the last two decades but remains scattered in various statistics and econometrics journals. It can be used as a graduate level text and also as a research monograph for statisticians and econometricians.
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
Chester Ismay,Albert Y. Kim
Author : Chester Ismay,Albert Y. Kim
Publisher : CRC Press
Page : 461 pages
File Size : 52,9 Mb
Release : 2019-12-23
Category : Mathematics
ISBN : 9781000763461
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse by Chester Ismay,Albert Y. Kim Pdf
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.