Spatial Data On The Web
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Spatial Data on the Web
Author: Alberto Belussi
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-08-15
Spatial data is essential in a wide range of application domains today. While geographical applications remain the key target area, spatial properties are required in other contexts such as computer-aided design, robotics and image processing. Associated with these is the constantly growing number of distributed processing architectures, based on, for example, grid systems, sensor data networks, and personalized smart devices. Spatial Data on the Web links these two research streams, focusing on the modeling and management of spatial data in distributed systems. Belussi and his coeditors have structured the contributions from internationally renowned researchers into four parts. Part I presents models for representing semistructured, multiresolution and multiscale data; Part II deals with the integration of spatial data sources; Part III describes approaches to spatial data protection; and, finally, Part IV reports innovative applications for mobile devices. The book offers researchers in academia and industry an excellent overview of the state of the art in modeling and management of spatial data in distributed environments, while it may also be the basis of specialized courses on Web-based geographical information systems.
Geocomputation with R
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/.
Collecting Spatial Data
The book is concerned with the statistical theory for locating spatial sensors. It bridges the gap between spatial statistics and optimum design theory. After introductions to those two fields the topics of exploratory designs and designs for spatial trend and variogram estimation are treated. A new methodology, so-called approximate information matrices, are employed to cope with the problem of correlated observations. A great number of relevant references are collected and put into a common perspective. The theoretical investigations are accompanied by a practical example, the redesign of an Upper-Austrian air pollution monitoring network. The reader will find respective theory and recommendations on how to efficiently plan a specific purpose spatial monitoring network.