Bivand Roger S. - Applied Spatial Data Analysis with R
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Preface 2nd edition -- Preface 1st edition -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Classes for spatio-temporal Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Modelling Areal Data -- Disease Mapping
Bivand Roger S.: author's other books
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Bivand Roger S.
Jorge Mateu
Martin Werner (editor)
Eric Pimpler
Brunsdon Chris
Chris Brunsdon
Mark R. T. Dale
Martin Thiering
Noel Cressie
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Roger S. Bivand , Edzer Pebesma and Virgilio Gmez-Rubio Use R! Applied Spatial Data Analysis with R 2nd ed. 2013 10.1007/978-1-4614-7618-4_1 Springer Science+Business Media New York 2013
1. Hello World : Introducing Spatial Data
Roger S. Bivand 1, Edzer Pebesma 2 and Virgilio Gmez-Rubio 3
(1)
Norwegian School of Economics, Bergen, Norway
(2)
Westflische Wilhelms-Universitt, Mnster, Germany
(3)
Department of Mathematics, Universidad de Castilla-La Mancha, Albacete, Spain
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.
1.1 Applied Spatial Data Analysis
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.
Possible questions that may arise include the following:
- Does the spatial patterning of disease incidences give rise to the conclusion that they are clustered, and if so, are the clusters found related to factors such as age, relative poverty, or pollution sources?
- Given a number of observed soil samples, which part of a study area is polluted?
- Given scattered air quality measurements, how many people are exposed to high levels of black smoke or particulate matter (e.g. PM10), and where do they live?
- Do governments tend to compare their policies with those of their neighbours, or do they behave independently?
In this book we will be concerned with applied spatial data analysis, meaning that we will deal with data sets, explain the problems they confront us with, and show how we can attempt to reach a conclusion. This book will refer to the theoretical background of methods and models for data analysis, but emphasise hands-on, do-it-yourself examples using R ; readers needing this background should consult the references. All data sets used in this book and all examples given are available, and interested readers will be able to reproduce them.
In this chapter we discuss the following:
(i)
Why we use R for analysing spatial data
(ii)
The relation between R and geographical information systems (GIS)
(iii)
What spatial data are, and the types of spatial data we distinguish
(iv)
The challenges posed by their storage and display
(v)
The analysis of observed spatial data in relation to processes thought to have generated them
(vi)
Sources of information about the use of R for spatial data analysis and the structure of the book.
1.2 Why Do We Use R
1.2.1 In General?
The R system). For data analysis, it can be highly efficient to use a special-purpose language like S , compared to using a general-purpose language.
For new R users without earlier scripting or programming experience, meeting a programming language may be unsettling, but the investment will quickly pay off. The user soon discovers how analysis components written or copied from examples can easily be stored, replayed, modified for another data set, or extended. R can be extended easily with new dedicated components, and can be used to develop and exchange data sets and data analysis approaches. It is often much harder to achieve this with programs that require long series of mouse clicks to operate.
R provides many standard and innovative statistical analysis methods. New users may find access to both well-tried and trusted methods, and speculative and novel approaches, worrying. This can, however, be a major strength, because if required, innovations can be tested in a robust environment against legacy techniques. Many methods for analysing spatial data are less frequently used than the most common statistical techniques, and thus benefit proportionally more from the nearness to both the data and the methods that R permits. R uses well-known libraries for numerical analysis, and can easily be extended by or linked to code written in S , C, C++, Fortran, or Java. Links to various relational data base systems and geographical information systems exist, many well-known data formats can be read and/or written.
The level of voluntary support and the development speed of R are high, and experience has shown R to be environment suitable for developing professional, mission-critical software applications, both for the public and the private sector. The S language can not only be used for low-level computation on numbers, vectors, or matrices but can also be easily extended with classes for new data types and analysis methods for these classes, such as methods for summarising, plotting, printing, performing tests, or model fitting (Chambers, ).
In addition to the core R software system, R is also a social movement, with many participants on a continuum from use R s just beginning to analyse data with R to develope R s contributing packages to the Comprehensive R Archive Network (CRAN) for others to download and employ.
Just as R itself benefits from the open source development model, contributed package authors benefit from a world-class infrastructure, allowing their work to be published and revised with improbable speed and reliability, including the publication of source packages and binary packages for many popular platforms. Contributed add-on packages are very much part of the R community, and most core developers also write and maintain contributed packages. A contributed package contains R functions, optional sample data sets, and documentation including examples of how to use the functions.
1.2.2 for Spatial Data Analysis?
For over 15 years, R has had an increasing number of contributed packages for handling and analysing spatial data. Up to 2003, these packages all used to make different assumptions about how spatial data were organised, and R itself had no capabilities for distinguishing coordinates from other numbers. In addition, methods for plotting spatial data and other tasks were scattered, made different assumptions on the organisation of the data, and were rudimentary. This was not unlike the situation for time series data at the time.
After some joint effort and wider discussion, a group). Classes specify a structure and define how spatial data are organised and stored. Methods are instances of functions specialised for a particular data class. For example, the summary method for all spatial data classes may tell the range spanned by the spatial coordinates, and show which coordinate reference system is used (such as degrees longitude/latitude, or the UTM zone). It may in addition show some more details for objects of a specific spatial class. A plot method may, for example create a map of the spatial data.
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