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Applied Spatial Data Analysis with R
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 st...
Interfacing Geostatstics and GIS
The present volume contains contributions of internationally renowned authors to the theme of interfacing between geostatistics, geoinformation systems and spatial data base management systems. Although some progress has been made toward interfacing, there is still only little overlap between the different communities. The present volume is intended to provide a bridge between specialists working in these areas. The volume first surveys new methodological developments in geostatistics, and then reports on applications in traditional areas of geo-and environmental sciences and novel application.
Lattice
Written by the author of the lattice system, this book describes lattice in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to 150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.
Data Science
This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
Spatial Data Science
Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analy...
Principles of Soundscape Ecology
From a founding figure in the field, the definitive introduction to an exciting new science. What do the sounds of a chorus of tropical birds and frogs, a clap of thunder, and a cacophony of urban traffic have in common? They are all components of a soundscape, acoustic environments that have been identified by scientists as a combination of the biophony, geophony, and anthrophony, respectively, of all of Earth’s sound sources. As sound is a ubiquitous occurrence in nature, it is actively sensed by most animals and is an important way for them to understand how their environment is changing. For humans, environmental sound is a major factor in creating a psychological sense of place, and m...
Statistical Inference and Simulation for Spatial Point Processes
Spatial point processes play a fundamental role in spatial statistics and today they are an active area of research with many new applications. Although other published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and a thorough treatment of the theory and applications of simulation-based inference is difficult to find. Written by researchers at the top of the field, this book collects and unifies recent theoretical advances and examples of applications. The authors examine Markov chain Monte Carlo algorithms and explore one of the most important recent developments in MCMC: perfect simulation procedures.
Remote Sensing Image Analysis: Including the Spatial Domain
Remote Sensing image analysis is mostly done using only spectral information on a pixel by pixel basis. Information captured in neighbouring cells, or information about patterns surrounding the pixel of interest often provides useful supplementary information. This book presents a wide range of innovative and advanced image processing methods for including spatial information, captured by neighbouring pixels in remotely sensed images, to improve image interpretation or image classification. Presented methods include different types of variogram analysis, various methods for texture quantification, smart kernel operators, pattern recognition techniques, image segmentation methods, sub-pixel m...
Spatial Data Science
-Written by the authors of key spatial R packages -Makes spatial data analysis more robust -Integrates with the tidyverse and comparable approaches -Includes many easily reproducible examples
