Spatial Data Analysis and R language practices

Space Data Analysis and R language practices
Basic Information
Original Title: Applied spatial data analysis with R
Author: pebesma, E. J.) Gemel-Rubio (Gómez-Rubio, V .)
Translator: Xu Aiping Shu Hong
Press: Tsinghua University Press
ISBN: 9787302302353
Mounting time:
Published on: February 1, January 2013
Start: 16
Page number: 1
Version: 1-1
Category: Computer> Software and programming> integration> advanced programming language design
Computer> database storage and management

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Introduction
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Space Data Analysis and R language practice comprehensively introduces the principles and methods of R used in spatial data analysis. Based on introduction to the spatial data class, method, spatial object, spatial point class, spatial line class, spatial surface class, and spatial grid in R, firstly, this paper introduces the visualization of spatial data, the import and export of spatial data, the processing of spatial data, and the methods for customizing multi-point data, hexagonal grids, time-space grids, and large grid data; then, it introduces spatial point pattern analysis, interpolation and geographic statistics analysis, surface data, spatial self-correlation analysis, and surface data modeling. Finally, it introduces the application of spatial data analysis in disease data plotting and analysis.
Space Data Analysis and R language practice are suitable for teaching materials for Space Information Processing and space information visualization, A large number of examples demonstrate the application fields and values of spatial analysis methods, and comprehensively demonstrate the achievements and prospects of R in spatial statistics and analysis. The website of this book includes all the examples in the book, the packages and datasets involved, which will be of great help for readers to learn and study.
Directory
Space Data Analysis and R language practices
Part 1 R Spatial Data Processing
Chapter 1 Introduction to spatial data 1
1.1 spatial data analysis 1
1.2 Why use R 2
1.2.1 Overview 2
1.2.2 why use R for spatial data analysis 3
1.3 R and GIS 5
1.3.1 what is GIS 5
1.3.2 Service-Oriented Architecture 5
1.3.3 learn more about GIS 5
1.4 Spatial Data Type 6
1.5 storage and display 10
1.6 Spatial Data Analysis Application 11
1.7 R Space Resource 13
1.7.1 online resource 13
1.7.2 structure of this book 14
Chapter 17 R spatial data class 17
2.1 Overview 17
2.2 classes and Methods 18 in R
2.3 spatial object 22
2.4 spatialpoints class 24
2.4.1 Method 25
2.4.2 data frame of spatial point data 27
2.5 spatiallines class 31
2.6 spatialpolygons class 35
2.6.1 spatialpolygons dataframe object 37
2.6.2 hole and ring direction 39
2.7 spatialgrid and spatialpixel objects 40
Chapter 2 spatial data visualization 47
3.1 Traditional drawing system 47
3.1.1 draw points, lines, polygon and grid 47
3.1.2 axis and layout element 50
3.1.3 axis labels and degrees 53 in the reference Mesh
3.1.4 drawing size, drawing area, map proportion, and multi-Chart Drawing 54
3.1.5 plot attributes and map legend 56
3.2 use spplot's trellis/lattice to plot 57
3.2.1 an intuitive trellis example 58
3.2.2 draw points, lines, faces, and grids 59
3.2.3 add reference objects and layout elements to the graph 61
3.2.4 layout of panel 63
3.3 drawing interaction 63
3.3.1 interaction of basic images 63
3.3.2 drawing interaction between spplot and lattice 65
3.4 color palette and class range 66
3.4.1 color palette 66
3.4.2 class range 66
Chapter 2 Import/Export spatial data 70
4.1 coordinate reference system 71
4.1.1 use epsg list 72
4.1.2 proj.4 CRS specification 72
4.1.3 projection and coordinate transformation 73
4.1.4 degrees, minutes, And seconds 75
4.2 Vector file format 76
4.2.1 use the OGR driver 77 in the rgdal package
4.2.2 other import/export functions 81
4.3 Grid File Format 81
4.3.1 use gdal driver 81 in rgdal package
4.3.2 compile a Google Earth image to cover 84
4.4 grass 86
Hundred old street, cholera data 91
4.5 other import/export interfaces 94
4.5.1 analysis and visualization application 94
4.5.2 terralib and art 95
4.5.3 other GIS and Web Map systems 96
4.6 install rgdal package 97
Chapter 2 advanced spatial data processing method 99
5.1 support 99
5.2 stacked 102
5.3 space sampling 104
5.4 topology check 106
5.4.1 polygon merge 108
5.4.2 check of hole status 109
5.5 combined spatial data 110
5.5.1 combined location data 110
5.5.2 combined attribute data 110
5.6 auxiliary functions 112
Chapter 2 custom spatial data types and Methods 6th
6.1 use classes and methods for programming 117
6.1.1 S3 type and method 118
6.1.2 S4 type and method 119
6.2 animal footprint data in the trip package 120
6.2.1 General function and constructor 121
6.2.2 method of trip object 122
6.3 Multi-Point Data: More than 123 of space focuses
6.4 hexagonal mesh 125
6.5 hour-empty grid 128
6.6 Space Analysis of Monte Carlo simulation 132
6.7 processing of large grids 134
Part 2 Spatial Data Analysis
Chapter 2 spatial point mode analysis 7th
7.1 overview 136
7.2 spatial point mode analysis package 137
Initial analysis of the 7.3-point model 140
7.3.1 full space random mode 140
7.3.2G function: the nearest neighbor event distance is 141.
7.3.3 F function: the distance from a point to the nearest event is 143.
7.4 statistical analysis of spatial point process 144
7.4.1 homogeneous Poisson process 145
7.4.2 non-homogeneous Poisson process 145
7.4.3 estimated strength: 145
7.4.4 likelihood 149 of non-homogeneous Poisson Process
7.4.5 second-order feature 151
7.4.6 non-homogeneous K functions 152
7.5 some applications in spatial epidemiology 153
7.5.1 case control study 153
7.5.2 binary regression estimation 158
7.5.3 binary regression 159 using a generalized plus Model
7.5.4 point source pollution 161
7.5.5 space aggregation evaluation 163
7.5.6 interpretation of Mixed Variables and co-variables 165
Method for Analyzing the 7.6-Point Mode: 168
Chapter 2 interpolation and Geographic Statistics 8th
8.1 overview 170
8.2 exploratory data analysis 171
8.3 non-statistical interpolation method 172
8.3.1 back-distance Weighted Interpolation 172
8.3.2 linear regression 173
8.4 spatial correlation estimation: variant function 174
8.4.1 exploratory variant function analysis 175
8.4.2 intercept, interval width, and direction dependence: 178
8.4.3 variant function model 179
8.4.4 heterosexual 183
8.4.5 multi-variable variant function model 184
8.4.6 residual variant function model 186
8.5 Space Prediction 187
8.5.1 fankeri, General Kerry and simple Kerry kingfa 188
8.5.2 multi-variable prediction: collaboration with Kerry kingfa 189
8.5.3 same-digit collaboration with Kerry kingfa 190
8.5.4 comparison with the Kerry Law 191
8.5.5 Kerry kingfa 191
8.5.6 Kerry Law 192
8.5.7 division of regions 193
8.5.8 trend functions and their coefficients 194
8.5.9 nonlinear transformation of variables 195
8.5.10 Singular Matrix Error 197
8.6 Model Diagnosis 198
8.6.1 cross-validation residual error 199
8.6.2 cross-validation Z-score 201
8.6.3 multi-variable cross-validation 201
8.6.4 limitations of cross verification 202
8.7 local statistical simulation 203
8.7.1 sequential simulation 203
8.7.2 nonlinear spatial clustering and block mean 205
8.7.3 multi-variable and indication simulation 206
8.8 Model-Based Geographic Statistics and Bayesian methods 207
8.9 monitoring network optimization 207
8.10 other R language packs for interpolation and Geographic Statistics 209
8.10.1 non-statistical interpolation 209
8.10.2 spatial package 209
8.10.3 randomfields package 209
8.10.4 Geor and georglm packages 211
8.10.5 fields package 211
Chapter 4 data and space self-correlation 9th
9.1 overview 212
9.2 Spatial Neighborhood 214
9.2.1 neighbors 215
9.2.2 create a neighboring domain 217
9.2.3 create graph-based neighbor 219
9.2.4 distance-Based Nearest Neighbor 220
9.2.5 High-Order nearest neighbor 223
9.2.6 grid nearest neighbor 224
9.3 inter-null weights: 225
9.3.1 space weight mode 225
9.3.2 General Space weight 227
9.3.3 import, export, and convert of spatial closeness and weight 229
9.3.4 use weights to simulate spatial self-correlation 230
9.3.5 operation space weight 231
9.4 Space Self-correlation test 232
9.4.1 global test 234
9.4.2 local test 240
Chapter 4 Data Modeling 10th
10.1 overview 246
10.2 spatial statistics method 246
10.2.1 synchronous auto-regression (SAR) model 249
10.2.2 conditional auto-regression (CAR) model 253
10.2.3 spatial regression model fitting 255
10.3 Mixed Effect Model 257
10.4 spatial metered economics method 259
10.5 other methods 265
10.5.1 GAM, gee, glmM 265
10.5.2 Moran feature 269
10.5.3 geographic weighted regression 272
Chapter 1 disease chart 11th
11.1 Introduction 277
11.2 statistical model 278
11.2.1 Poisson-Gamma Model 280
11.2.2 log-normal model 282
11.2.3 Marshall, global EB Estimator, 283
11.3 spatial structure statistical model 285
11.4 Bayesian layered model 286
11.4.1 re-explore Poisson-Gamma Model 287
11.4.2 spatial model 291
11.5 disease aggregation detection 298
11.5.1 Homogeneous Test of relative risks 299
11.5.2 Space Self-related Moran's I test 301
11.5.3 Tango's test of general aggregation 301
11.5.4 clustered location detection 302
11.5.5 geographic analyzer 303
11.5.6 kulldorfft statistics 304
11.5.7 partial clustering stone test 305
11.6 other themes of disease maps 306
Conclusion 307:
References 311

Source of this book: China Interactive publishing network