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Applied Spatial Data Analysis with R.

Av: Medverkande: Materialtyp: TextSerie: Utgivningsuppgift: New York, NY : Springer New York, 2013Datum för upphovsrätt: ©2013Utgåva: 2nd edBeskrivning: 1 online resource (414 pages)Innehållstyp:
  • text
Medietyp:
  • computer
Bärartyp:
  • online resource
ISBN:
  • 9781461476184
Ämnen: Genre/form: DDK-klassifikation:
  • 519.536
Onlineresurser:
Innehåll:
Intro -- Preface (Second Edition) -- Preface (First Edition) -- Contents -- Chapter 1 Hello World: Introducing Spatial Data -- 1.1 Applied Spatial Data Analysis -- 1.2 Why Do We Use R -- 1.2.1 … In General? -- 1.2.2 …for Spatial Data Analysis? -- 1.2.3 …and for Reproducible Research? -- 1.3 R and GIS -- 1.3.1 What Is GIS? -- 1.3.2 Service-Oriented Architectures -- 1.3.3 Further Reading on GIS -- 1.4 Types of Spatial Data -- 1.5 Storage and Display -- 1.6 Applied Spatial Data Analysis -- 1.7 R Spatial Resources -- 1.8 Layout of the Book -- Part I Handling Spatial Data in R -- Chapter 2 Classes for Spatial Data in R -- 2.1 Introduction -- 2.2 Classes and Methods in R -- 2.3 Spatial Objects -- 2.4 SpatialPoints -- 2.4.1 Methods -- 2.4.2 Data Frames for Spatial Point Data -- 2.5 SpatialLines -- 2.6 SpatialPolygons -- 2.6.1 SpatialPolygonsDataFrame Objects -- 2.6.2 Holes and Ring Direction -- 2.7 SpatialGrid and SpatialPixel Objects -- 2.8 Raster Objects and the raster Package -- Chapter 3 Visualising Spatial Data -- 3.1 The Traditional Plot System -- 3.1.1 Plotting Points, Lines, Polygons, and Grids -- 3.1.2 Axes and Layout Elements -- 3.1.3 Degrees in Axes Labels and Reference Grid -- 3.1.4 Plot Size, Plotting Area, Map Scale,and Multiple Plots -- 3.1.5 Plotting Attributes and Map Legends -- 3.2 Trellis/Lattice Plots with spplot -- 3.2.1 A Straight Trellis Example -- 3.2.2 Plotting Points, Lines, Polygons, and Grids -- 3.2.3 Adding Reference and Layout Elements to Plots -- 3.2.4 Arranging Panel Layout -- 3.3 Alternatives Routes: =45 ggplot, latticeExtra -- 3.4 Interactive Plots -- 3.4.1 Interacting with Base Graphics -- 3.4.2 Interacting with spplot and Lattice Plots -- 3.5 Colour Palettes and Class Intervals -- 3.5.1 Colour Palettes -- 3.5.2 Class Intervals -- Chapter 4 Spatial Data Import and Export -- 4.1 Coordinate Reference Systems.
4.1.1 Using the EPSG List -- 4.1.2 PROJ.4 CRS Specification -- 4.1.3 Projection and Transformation -- 4.1.4 Degrees, Minutes, and Seconds -- 4.2 Vector File Formats -- 4.2.1 Using OGR Drivers in rgdal -- 4.2.2 Other Import/Export Functions -- 4.3 Raster File Formats -- 4.3.1 Using GDAL Drivers in rgdal -- 4.3.2 Other Import/Export Functions -- 4.4 Google Earth™, Google Maps™ and Other Formats -- 4.5 Geographical Resources Analysis Support System (GRASS) -- 4.5.1 Broad Street Cholera Data -- 4.6 Other Import/Export Interfaces -- 4.6.1 Analysis and Visualisation Applications -- 4.6.2 TerraLib and aRT -- 4.6.3 Other GIS Systems -- 4.7 Installing rgdal -- Chapter 5 Further Methods for Handling Spatial Data -- 5.1 Support -- 5.2 Handling and Combining Features -- 5.2.1 The rgeos Package -- 5.2.2 Using rgeos -- 5.3 Map Overlay or Spatial Join -- 5.3.1 Spatial Aggregation -- 5.3.2 Using the raster Package for Extract Operations -- 5.3.3 Spatial Sampling -- 5.4 Auxiliary Functions -- Chapter 6 Spatio-Temporal Data -- 6.1 Introduction -- 6.2 Types of Spatio-Temporal Data -- 6.2.1 Spatial Point or Area, Time Instance or Interval -- 6.2.2 Are Space and Time of Primary Interest? -- 6.2.3 Regularity of Space-Time Layouts -- 6.2.4 Do Objects Change Location? -- 6.3 Classes in spacetime -- 6.4 Handling Time Series Data with =45 xts -- 6.5 Construction of =45 STObjects -- 6.6 Selection, Addition, and Replacement of Attributes -- 6.7 Overlay and Aggregation -- 6.8 Visualisation -- 6.8.1 Multi-panel Plots -- 6.8.2 Space-Time Plots -- 6.8.3 Animated Plots -- 6.8.4 Time Series Plots -- 6.9 Further Packages -- 6.9.1 Handling Spatio-Temporal Data -- 6.9.2 Analysing Spatio-Temporal Data -- 6.10 Outlook -- Part II Analysing Spatial Data -- Chapter 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 -- 7.3.1 Complete Spatial Randomness -- 7.3.2 G Function: Distance to the Nearest Event -- 7.3.3 F Function: Distance from a Pointto the Nearest Event -- 7.4 Statistical Analysis of Spatial Point Processes -- 7.4.1 Homogeneous Poisson Processes -- 7.4.2 Inhomogeneous Poisson Processes -- 7.4.3 Estimation of the Intensity -- 7.4.4 Likelihood of an Inhomogeneous Poisson Process -- 7.4.5 Second-Order Properties -- 7.4.5.1 Inhomogeneous K-Function -- 7.5 Some Applications in Spatial Epidemiology -- 7.5.1 Case-Control Studies -- 7.5.1.1 Spatial Variation of the Relative Risk -- 7.5.2 Binary Regression Estimator -- 7.5.3 Binary Regression Using GeneralisedAdditive Models -- 7.5.4 Point Source Pollution -- 7.5.4.1 Assessment of General Spatial Clustering -- 7.5.5 Accounting for Confounding and Covariates -- 7.6 Further Methods for the Analysis of Point Patterns -- Chapter 8 Interpolation and Geostatistics -- 8.1 Introduction -- 8.2 Exploratory Data Analysis -- 8.3 Non-geostatistical Interpolation Methods -- 8.3.1 Inverse Distance Weighted Interpolation -- 8.3.2 Linear Regression -- 8.4 Estimating Spatial Correlation: The Variogram -- 8.4.1 Exploratory Variogram Analysis -- 8.4.2 Cutoff, Lag Width, Direction Dependence -- 8.4.3 Variogram Modelling -- 8.4.4 Anisotropy -- 8.4.5 Multivariable Variogram Modelling -- 8.4.6 Residual Variogram Modelling -- 8.5 Spatial Prediction -- 8.5.1 Universal, Ordinary, and Simple Kriging -- 8.5.2 Multivariable Prediction: Cokriging -- 8.5.3 Collocated Cokriging -- 8.5.4 Cokriging Contrasts -- 8.5.5 Kriging in a Local Neighbourhood -- 8.5.6 Change of Support: Block Kriging -- 8.5.7 Stratifying the Domain -- 8.5.8 Trend Functions and Their Coefficients -- 8.5.9 Non-linear Transforms of the Response Variable -- 8.5.10 Singular Matrix Errors -- 8.6 Kriging, Filtering, Smoothing.
8.7 Model Diagnostics -- 8.7.1 Cross Validation Residuals -- 8.7.2 Cross Validation z-Scores -- 8.7.3 Multivariable Cross Validation -- 8.7.4 Limitations to Cross Validation -- 8.8 Geostatistical Simulation -- 8.8.1 Sequential Simulation -- 8.8.2 Non-linear Spatial Aggregation and Block Averages -- 8.8.3 Multivariable and Indicator Simulation -- 8.9 Model-Based Geostatistics and Bayesian Approaches -- 8.10 Monitoring Network Optimisation -- 8.11 Other R Packages for Interpolation and Geostatistics -- 8.11.1 Non-geostatistical Interpolation -- 8.11.2 Spatial -- 8.11.3 RandomFields -- 8.11.4 geoR and geoRglm -- 8.11.5 Fields -- 8.11.6 spBayes -- 8.12 Spatio-Temporal Prediction -- Chapter 9 Modelling Areal Data -- 9.1 Introduction -- 9.2 Spatial Neighbours and Spatial Weights -- 9.2.1 Neighbour Objects -- 9.2.2 Spatial Weights Objects -- 9.2.3 Handling Spatial Weights Objects -- 9.2.4 Using Weights to Simulate Spatial Autocorrelation -- 9.3 Testing for Spatial Autocorrelation -- 9.3.1 Global Tests -- 9.3.2 Local Tests -- 9.4 Fitting Models of Areal Data -- 9.4.1 Spatial Statistics Approaches -- 9.4.1.1 Simultaneous Autoregressive Models -- 9.4.1.2 Conditional Autoregressive Models -- 9.4.1.3 Fitting Spatial Regression Models -- 9.4.2 Spatial Econometrics Approaches -- 9.4.3 Other Methods -- Chapter 10 Disease Mapping -- 10.1 Introduction -- 10.2 Statistical Models -- 10.2.1 Poisson-Gamma Model -- 10.2.2 Log-Normal Model -- 10.2.3 Marshall's Global EB Estimator -- 10.3 Spatially Structured Statistical Models -- 10.4 Bayesian Hierarchical Models -- 10.4.1 The Poisson-Gamma Model Revisited -- 10.4.2 Spatial Models -- 10.5 Geoadditive Models -- 10.6 Detection of Clusters of Disease -- 10.6.1 Testing the Homogeneity of the Relative Risks -- 10.6.2 Moran's I Test of Spatial Autocorrelation -- 10.6.3 Tango's Test of General Clustering.
10.6.4 Detection of the Location of a Cluster -- 10.6.5 Geographical Analysis Machine -- 10.6.6 Kulldorff's Statistic -- 10.6.7 Stone's Test for Localised Clusters -- 10.7 Spatio-Temporal Disease Mapping -- 10.7.1 Introduction -- 10.7.2 Spatio-Temporal Modelling of Disease -- 10.8 Other Topics in Disease Mapping -- Afterword -- R and Package Versions Used -- Data Sets Used -- References -- Subject Index -- Functions Index.
Sammanfattning: This book first presents R packages, functions, classes and methods for handling spatial data. It then showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics and disease mapping.
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Intro -- Preface (Second Edition) -- Preface (First Edition) -- Contents -- Chapter 1 Hello World: Introducing Spatial Data -- 1.1 Applied Spatial Data Analysis -- 1.2 Why Do We Use R -- 1.2.1 … In General? -- 1.2.2 …for Spatial Data Analysis? -- 1.2.3 …and for Reproducible Research? -- 1.3 R and GIS -- 1.3.1 What Is GIS? -- 1.3.2 Service-Oriented Architectures -- 1.3.3 Further Reading on GIS -- 1.4 Types of Spatial Data -- 1.5 Storage and Display -- 1.6 Applied Spatial Data Analysis -- 1.7 R Spatial Resources -- 1.8 Layout of the Book -- Part I Handling Spatial Data in R -- Chapter 2 Classes for Spatial Data in R -- 2.1 Introduction -- 2.2 Classes and Methods in R -- 2.3 Spatial Objects -- 2.4 SpatialPoints -- 2.4.1 Methods -- 2.4.2 Data Frames for Spatial Point Data -- 2.5 SpatialLines -- 2.6 SpatialPolygons -- 2.6.1 SpatialPolygonsDataFrame Objects -- 2.6.2 Holes and Ring Direction -- 2.7 SpatialGrid and SpatialPixel Objects -- 2.8 Raster Objects and the raster Package -- Chapter 3 Visualising Spatial Data -- 3.1 The Traditional Plot System -- 3.1.1 Plotting Points, Lines, Polygons, and Grids -- 3.1.2 Axes and Layout Elements -- 3.1.3 Degrees in Axes Labels and Reference Grid -- 3.1.4 Plot Size, Plotting Area, Map Scale,and Multiple Plots -- 3.1.5 Plotting Attributes and Map Legends -- 3.2 Trellis/Lattice Plots with spplot -- 3.2.1 A Straight Trellis Example -- 3.2.2 Plotting Points, Lines, Polygons, and Grids -- 3.2.3 Adding Reference and Layout Elements to Plots -- 3.2.4 Arranging Panel Layout -- 3.3 Alternatives Routes: =45 ggplot, latticeExtra -- 3.4 Interactive Plots -- 3.4.1 Interacting with Base Graphics -- 3.4.2 Interacting with spplot and Lattice Plots -- 3.5 Colour Palettes and Class Intervals -- 3.5.1 Colour Palettes -- 3.5.2 Class Intervals -- Chapter 4 Spatial Data Import and Export -- 4.1 Coordinate Reference Systems.

4.1.1 Using the EPSG List -- 4.1.2 PROJ.4 CRS Specification -- 4.1.3 Projection and Transformation -- 4.1.4 Degrees, Minutes, and Seconds -- 4.2 Vector File Formats -- 4.2.1 Using OGR Drivers in rgdal -- 4.2.2 Other Import/Export Functions -- 4.3 Raster File Formats -- 4.3.1 Using GDAL Drivers in rgdal -- 4.3.2 Other Import/Export Functions -- 4.4 Google Earth™, Google Maps™ and Other Formats -- 4.5 Geographical Resources Analysis Support System (GRASS) -- 4.5.1 Broad Street Cholera Data -- 4.6 Other Import/Export Interfaces -- 4.6.1 Analysis and Visualisation Applications -- 4.6.2 TerraLib and aRT -- 4.6.3 Other GIS Systems -- 4.7 Installing rgdal -- Chapter 5 Further Methods for Handling Spatial Data -- 5.1 Support -- 5.2 Handling and Combining Features -- 5.2.1 The rgeos Package -- 5.2.2 Using rgeos -- 5.3 Map Overlay or Spatial Join -- 5.3.1 Spatial Aggregation -- 5.3.2 Using the raster Package for Extract Operations -- 5.3.3 Spatial Sampling -- 5.4 Auxiliary Functions -- Chapter 6 Spatio-Temporal Data -- 6.1 Introduction -- 6.2 Types of Spatio-Temporal Data -- 6.2.1 Spatial Point or Area, Time Instance or Interval -- 6.2.2 Are Space and Time of Primary Interest? -- 6.2.3 Regularity of Space-Time Layouts -- 6.2.4 Do Objects Change Location? -- 6.3 Classes in spacetime -- 6.4 Handling Time Series Data with =45 xts -- 6.5 Construction of =45 STObjects -- 6.6 Selection, Addition, and Replacement of Attributes -- 6.7 Overlay and Aggregation -- 6.8 Visualisation -- 6.8.1 Multi-panel Plots -- 6.8.2 Space-Time Plots -- 6.8.3 Animated Plots -- 6.8.4 Time Series Plots -- 6.9 Further Packages -- 6.9.1 Handling Spatio-Temporal Data -- 6.9.2 Analysing Spatio-Temporal Data -- 6.10 Outlook -- Part II Analysing Spatial Data -- Chapter 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 -- 7.3.1 Complete Spatial Randomness -- 7.3.2 G Function: Distance to the Nearest Event -- 7.3.3 F Function: Distance from a Pointto the Nearest Event -- 7.4 Statistical Analysis of Spatial Point Processes -- 7.4.1 Homogeneous Poisson Processes -- 7.4.2 Inhomogeneous Poisson Processes -- 7.4.3 Estimation of the Intensity -- 7.4.4 Likelihood of an Inhomogeneous Poisson Process -- 7.4.5 Second-Order Properties -- 7.4.5.1 Inhomogeneous K-Function -- 7.5 Some Applications in Spatial Epidemiology -- 7.5.1 Case-Control Studies -- 7.5.1.1 Spatial Variation of the Relative Risk -- 7.5.2 Binary Regression Estimator -- 7.5.3 Binary Regression Using GeneralisedAdditive Models -- 7.5.4 Point Source Pollution -- 7.5.4.1 Assessment of General Spatial Clustering -- 7.5.5 Accounting for Confounding and Covariates -- 7.6 Further Methods for the Analysis of Point Patterns -- Chapter 8 Interpolation and Geostatistics -- 8.1 Introduction -- 8.2 Exploratory Data Analysis -- 8.3 Non-geostatistical Interpolation Methods -- 8.3.1 Inverse Distance Weighted Interpolation -- 8.3.2 Linear Regression -- 8.4 Estimating Spatial Correlation: The Variogram -- 8.4.1 Exploratory Variogram Analysis -- 8.4.2 Cutoff, Lag Width, Direction Dependence -- 8.4.3 Variogram Modelling -- 8.4.4 Anisotropy -- 8.4.5 Multivariable Variogram Modelling -- 8.4.6 Residual Variogram Modelling -- 8.5 Spatial Prediction -- 8.5.1 Universal, Ordinary, and Simple Kriging -- 8.5.2 Multivariable Prediction: Cokriging -- 8.5.3 Collocated Cokriging -- 8.5.4 Cokriging Contrasts -- 8.5.5 Kriging in a Local Neighbourhood -- 8.5.6 Change of Support: Block Kriging -- 8.5.7 Stratifying the Domain -- 8.5.8 Trend Functions and Their Coefficients -- 8.5.9 Non-linear Transforms of the Response Variable -- 8.5.10 Singular Matrix Errors -- 8.6 Kriging, Filtering, Smoothing.

8.7 Model Diagnostics -- 8.7.1 Cross Validation Residuals -- 8.7.2 Cross Validation z-Scores -- 8.7.3 Multivariable Cross Validation -- 8.7.4 Limitations to Cross Validation -- 8.8 Geostatistical Simulation -- 8.8.1 Sequential Simulation -- 8.8.2 Non-linear Spatial Aggregation and Block Averages -- 8.8.3 Multivariable and Indicator Simulation -- 8.9 Model-Based Geostatistics and Bayesian Approaches -- 8.10 Monitoring Network Optimisation -- 8.11 Other R Packages for Interpolation and Geostatistics -- 8.11.1 Non-geostatistical Interpolation -- 8.11.2 Spatial -- 8.11.3 RandomFields -- 8.11.4 geoR and geoRglm -- 8.11.5 Fields -- 8.11.6 spBayes -- 8.12 Spatio-Temporal Prediction -- Chapter 9 Modelling Areal Data -- 9.1 Introduction -- 9.2 Spatial Neighbours and Spatial Weights -- 9.2.1 Neighbour Objects -- 9.2.2 Spatial Weights Objects -- 9.2.3 Handling Spatial Weights Objects -- 9.2.4 Using Weights to Simulate Spatial Autocorrelation -- 9.3 Testing for Spatial Autocorrelation -- 9.3.1 Global Tests -- 9.3.2 Local Tests -- 9.4 Fitting Models of Areal Data -- 9.4.1 Spatial Statistics Approaches -- 9.4.1.1 Simultaneous Autoregressive Models -- 9.4.1.2 Conditional Autoregressive Models -- 9.4.1.3 Fitting Spatial Regression Models -- 9.4.2 Spatial Econometrics Approaches -- 9.4.3 Other Methods -- Chapter 10 Disease Mapping -- 10.1 Introduction -- 10.2 Statistical Models -- 10.2.1 Poisson-Gamma Model -- 10.2.2 Log-Normal Model -- 10.2.3 Marshall's Global EB Estimator -- 10.3 Spatially Structured Statistical Models -- 10.4 Bayesian Hierarchical Models -- 10.4.1 The Poisson-Gamma Model Revisited -- 10.4.2 Spatial Models -- 10.5 Geoadditive Models -- 10.6 Detection of Clusters of Disease -- 10.6.1 Testing the Homogeneity of the Relative Risks -- 10.6.2 Moran's I Test of Spatial Autocorrelation -- 10.6.3 Tango's Test of General Clustering.

10.6.4 Detection of the Location of a Cluster -- 10.6.5 Geographical Analysis Machine -- 10.6.6 Kulldorff's Statistic -- 10.6.7 Stone's Test for Localised Clusters -- 10.7 Spatio-Temporal Disease Mapping -- 10.7.1 Introduction -- 10.7.2 Spatio-Temporal Modelling of Disease -- 10.8 Other Topics in Disease Mapping -- Afterword -- R and Package Versions Used -- Data Sets Used -- References -- Subject Index -- Functions Index.

This book first presents R packages, functions, classes and methods for handling spatial data. It then showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics and disease mapping.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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