Interpolation Of Spatial Data

A summary of past work and a description of new approaches to thinking about kriging, commonly used in the prediction of a random field based on observations at some set of locations in mining, hydrology, atmospheric sciences, and geography.

This title gives an authoritative look at the use of Geographical Information Systems (GIS) in climatology and meterology. GIS provides a range of strategies, from traditional methods, such as those for hydromet database analysis and management, to new developing methods. As such, this book will provide a useful reference tool in this important aspect of climatology and meterology study.

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.

Spatial statistics has been widely used in many environmental studies. This book is a collection of recent studies on applying spatial statistics in subjects such as demography, transportation, precision agriculture and ecology. Different subjects require different aspects of spatial statistics. In addition to quantitative statements from statistics and tests, visualization in forms of maps, drawings, and images are provided to illustrate the relationship between data and locations. This book will be valuable to researchers who are interested in applying statistics to spatial data, as well as graduate students who know statistics and want to explore how it can be applied to spatial data. With the processing part being simplified to several mouse clicks by commercial software, one should pay more attention to justification of using spatial statistics, as well as interpretation and assessment of the results. GIScience proves to be a useful tool in visualization of spatial data, and such useful technology should be utilized, as part, for the interpretation and assessment of the results.

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.

"Ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice" - Professor Tao Cheng, University College London Focusing specifically on spatial statistics and including components for ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all relevant programs and software. It explains and demonstrates techniques in: spatial sampling spatial autocorrelation local statistics spatial interpolation in two-dimensions advanced topics including Bayesian methods, Monte Carlo simulation, error and uncertainty. It is a systematic overview of the fundamental spatial statistical methods used by applied researchers in geography, environmental science, health and epidemiology, population and demography, and planning. A companion website includes digital R code for implementing the analyses in specific chapters and relevant data sets to run the R codes.

Offers New Insight on Uncertainty Modelling Focused on major research relative to spatial information, Uncertainty Modelling and Quality Control for Spatial Data introduces methods for managing uncertainties—such as data of questionable quality—in geographic information science (GIS) applications. By using original research, current advancement, and emerging developments in the field, the authors compile various aspects of spatial data quality control. From multidimensional and multi-scale data integration to uncertainties in spatial data mining, this book launches into areas that are rarely addressed. Topics covered include: New developments of uncertainty modelling, quality control of spatial data, and related research issues in spatial analysis Spatial statistical solutions in spatial data quality Eliminating systematic error in the analytical results of GIS applications A data quality perspective for GIS function workflow design Data quality in multi-dimensional integration Research challenges on data quality in the integration and analysis of data from multiple sources A new approach for imprecision management in the qualitative data warehouse A multi-dimensional quality assessment of photogrammetric and LiDAR datasets based on a vector approach An analysis on the uncertainty of multi-scale representation for street-block settlement Uncertainty Modelling and Quality Control for Spatial Data serves university students, researchers and professionals in GIS, and investigates the uncertainty modelling and quality control in multi-dimensional data integration, multi-scale data representation, national or regional spatial data products, and new spatial data mining methods.