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Applied spatial data analysis with R / R.S. Bivand (2008)

Public Titre : Applied spatial data analysis with R Type de document : Monographie Auteurs : R.S. Bivand, Auteur ; Edzer J. Pebesma, Auteur ; V. Gómez-Rubio, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2008 Collection : Use R! Importance : 374 p. Format : 15 x 23 cm ISBN/ISSN/EAN : 978-0-387-78170-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] données localisées [Termes IGN] géostatistique [Termes IGN] R (langage) [Termes IGN] système d'information géographique Résumé : (Editeur) This book 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. 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. 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 systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. Note de contenu : 1. Hello World: Introducing Spatial Data 1.1 Applied Spatial Data Analysis 1.2 Why Do We Use R 1.3 R and GIS 1.4 Types of Spatial Data 1.5 Storage and Display 1.6 Applied Spatial Data Analysis 1.7 R Spatial Resources Part 1 - Handling Spatial Data in R 2. Classes for Spatial Data in R 2.1 Introduction 2.2 Classes and Methods in R 2.3 Spatial Objects 2.4 SpatialPoints 2.5 SpatialLines 2.6 SpatialPolygons 2.7 SpatialGrid and SpatialPixel Objects 3. Visualising Spatial Data 3.1 The Traditional Plot System 3.2 Trellis/Lattice Plots with spplot 3.3 Interacting with Plots 3.4 Colour Palettes and Class Intervals 4. Spatial Data Import and Export 4.1 Coordinate Reference Systems 4.2 Vector File Formats 4.3 Raster File Formats 4.4 Grass 4.5 Other Import/Export Interfaces 4.6 Installing rgdal 5. Further Methods for Handling Spatial Data 5.1 Support 5.2 Overlay 5.3 Spatial Sampling 5.4 Checking Topologies 5.5 Combining Spatial Data 5.6 Auxiliary Functions 6. Customising Spatial Data Classes and Methods 6.1 Programming with Classes and Methods 6.2 Animal Track Data in Package Trip 6.3 Multi-Point Data: SpatialMultiPoints 6.4 Hexagonal Grids 6.5 Spatio-Temporal Grids 6.6 Analysing Spatial Monte Carlo Simulations 6.7 Processing Massive Grids Part 2 - Analysing Spatial Data 7. Spatial Point Pattern Analysis 7.1 Introduction 7.2 Packages for the Analysis of Spatial Point Patterns 7.3 Preliminary Analysis of a Point Pattern 7.4 Statistical Analysis of Spatial Point Processes 7.5 Some Applications in Spatial Epidemiology 7.6 Further Methods for the Analysis of Point Patterns 8. Interpolation and Geostatistics 8.1 Introduction 8.2 Exploratory Data Analysis 8.3 Non-Geostatistical Interpolation Methods 8.4 Estimating Spatial Correlation: The Variogram 8.5 Spatial Prediction 8.6 Model Diagnostics 8.7 Geostatistical Simulation 8.8 Model-Based Geostatistics and Bayesian Approaches 8.9 Monitoring Network Optimization 8.10 Other R Packages for Interpolation and Geostatistics 9. Areal Data and Spatial Autocorrelation 9.1 Introduction 9.2 Spatial Neighbours 9.3 Spatial Weights 9.4 Spatial Autocorrelation: Tests 10. Modelling Areal Data 10.1 Introduction 10.2 Spatial Statistics Approaches 10.3 Mixed-Effects Models 10.4 Spatial Econometrics Approaches 10.5 Other Methods 11. Disease Mapping 11.1 Introduction 11.2 Statistical Models 11.3 Spatially Structured Statistical Models 11.4 Bayesian Hierarchical Models 11.5 Detection of Clusters of Disease 11.6 Other Topics in Disease Mapping Numéro de notice : 20853 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Monographie Accessibilité hors numérique : Non accessible via le SUDOC Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=63150