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2013 | Buch

Hello World: Introducing Spatial Data Kapitel lesen Erstes Kapitel lesen

Applied Spatial Data Analysis with R

verfasst von: Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio

Verlag: Springer New York

Buchreihe : Use R!

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Hello World: Introducing Spatial Data
Abstract
Spatial and spatio-temporal data are everywhere. Besides those we collect ourselves (‘is it raining?’), they confront us on television, in newspapers, on route planners, on computer screens, on mobile devices, and on plain paper maps. Making a map that is suited to its purpose and does not distort the underlying data unnecessarily is however not easy. Beyond creating and viewing maps, spatial data analysis is concerned with questions not directly answered by looking at the data themselves. These questions refer to hypothetical processes that generate the observed data. Statistical inference for such spatial processes is often challenging, but is necessary when we try to draw conclusions about questions that interest us.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio

Handling Spatial Data in R

Frontmatter
Chapter 2. Classes for Spatial Data in R
Abstract
Many disciplines have influenced the representation of spatial data, both in analogue and digital forms. Surveyors, navigators, and military and civil engineers refined the fundamental concepts of mathematical geography, established often centuries ago by some of the founders of science, for example by al-Khwārizmı̄. Digital representations came into being for practical reasons in computational geometry, in computer graphics and hardware-supported gaming, and in computer-assisted design and virtual reality. The use of spatial data as a business vehicle has been spurred early in the present century by consumer wired and mobile broadband penetration and distributed server farms, with examples being Google EarthTM, Google MapsTM, and others. There are often interactions between the graphics hardware required and the services offered, in particular for the fast rendering of scene views.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Chapter 3. Visualising Spatial Data
Abstract
A major pleasure in working with spatial data is their visualisation. Maps are amongst the most compelling graphics, because the space they map is the space we think we live in, and maps may show things we cannot see otherwise. Although one can work with all R plotting functions on the raw data, for example extracted from Spatial classes by methods like coordinates or as.data.frame, this chapter introduces the plotting methods for objects inheriting from class Spatial that are provided by package sp.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Chapter 4. Spatial Data Import and Export
Abstract
Geographical information systems (GIS) and the types of spatial data they handle were introduced in Chap.1. We now show how spatial data can be moved between sp objects in Rand external formats, including the ones typically used by GIS. In this chapter, we first show how coordinate reference systems can be handled portably for import and export, going on to transfer vector and raster data, and finally consider ways of linking Rand GIS more closely.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Chapter 5. Further Methods for Handling Spatial Data
Abstract
This chapter is concerned with a more detailed explanation of some of the methods that are provided for working with the spatial classes described in Chap.​2. We first consider the question of the spatial support of observations, going on to cover the handling and combination of features using in particular the rgeos package. Next we consider map overlay, also known as spatial join operations, including aggregation, extract operations in the raster package, and spatial sampling.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Chapter 6. Spatio-Temporal Data
Abstract
Observations refer to properties or qualities at particular locations in space and moments in time. In many cases, locations and/or times are not taken into account explicitly, because they are not relevant. In other cases, they are. Most of this book addresses the case where spatial location matters, and temporal variation is not present or ignored. Texts on time series analysis mostly do the reverse. This chapter will address first steps in handling spatio-temporal data, and analysing them.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio

Analysing Spatial Data

Frontmatter
Chapter 7. Spatial Point Pattern Analysis
Abstract
The analysis of point patterns appears in many different areas of research. In ecology, for example, the interest may be focused on determining the spatial distribution (and its causes) of a tree species for which the locations have been obtained within a study area. Furthermore, if two or more species have been recorded, it may also be of interest to assess whether these species are equally distributed or competition exists between them. Other factors which force each species to spread in particular areas of the study region may be studied as well. In spatial epidemiology, a common problem is to determine whether the cases of a certain disease are clustered. This can be assessed by comparing the spatial distribution of the cases to the locations of a set of controls taken at random from the population.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Chapter 8. Interpolation and Geostatistics
Abstract
Geostatistical data are data that could in principle be measured anywhere, but that typically come as measurements at a limited number of observation locations: think of gold grades in an ore body or particulate matter in air samples. The pattern of observation locations is usually not of primary interest, as it often results from considerations ranging from economical and physical constraints to being ‘representative’ or random sampling varieties. The interest is usually in inference of aspects of the variable that have not been measured such as maps of the estimated values, exceedance probabilities or estimates of aggregates over given regions, or inference of the process that generated the data. Other problems include monitoring network optimisation: where should new observations be located or which observation locations should be removed such that the operational value of the monitoring network is maximised.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Chapter 9. Modelling Areal Data
Abstract
Spatial data are often observed on polygon entities with defined boundaries. The polygon boundaries are defined by the researcher in some fields of study, may be arbitrary in others and may be administrative boundaries created for very different purposes in others again. The observed data are frequently aggregations within the boundaries, such as population counts. The areal entities may themselves constitute the units of observation, for example when studying local government behaviour where decisions are taken at the level of the entity, for example setting local tax rates. By and large, though, areal entities are aggregates, bins, used to tally measurements, like voting results at polling stations. Very often, the areal entities are an exhaustive tessellation of the study area, leaving no part of the total area unassigned to an entity. Of course, areal entities may be made up of multiple geometrical entities, such as islands belonging to the same county; they may also surround other areal entities completely, and may contain holes, like lakes.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Chapter 10. Disease Mapping
Abstract
Spatial statistics have been widely applied in epidemiology to the study of the distribution of disease. As we have already shown in, displaying the spatial variation of the incidence of a disease can help us to detect areas where the disease is particularly prevalent, which may lead to the detection of previously unknown risk factors. As a result of the growing interest, Spatial Epidemiology (Elliott etal.,2000) has been established as a new multidisciplinary area of research in recent years.
Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Backmatter
Metadaten
Titel
Applied Spatial Data Analysis with R
verfasst von
Roger S. Bivand
Edzer Pebesma
Virgilio Gómez-Rubio
Copyright-Jahr
2013
Verlag
Springer New York
Electronic ISBN
978-1-4614-7618-4
Print ISBN
978-1-4614-7617-7
DOI
https://doi.org/10.1007/978-1-4614-7618-4

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