使用ArcGis进行空间统计分析入门教材，英文原版

Contents
1  Welcome to ArcGIS Geostatistical Analyst  1
    Exploratory spatial data analysis  2
    Semivariogram modeling  3
    Surface prediction and error modeling  4
    Threshold mapping  5
    Model validation and diagnostics   6
    Surface prediction using cokriging  7
    Tips on learning Geostatistical Analyst   8
2   Quick-start tutorial  11
    Introduction to the tutorial   12
    Exercise 1: Creating a surface using default parameters  14
    Exercise 2: Exploring your data   19
    Exercise 3: Mapping ozone concentration   26
    Exercise 4: Comparing models  38
    Exercise 5: Mapping the probability of ozone exceeding a critical threshold  39
    Exercise 6: Producing the final map  42
3   The principles of geostatistical analysis  49
    Understanding deterministic methods  50
    Understanding geostatistical methods   53
    Working through a problem  54
    Basic principles behind geostatistical methods   59
    Modeling a semivariogram  61
    Kriging  74
    A guide to the Geostatistical Analyst extension   78
4   Exploratory Spatial Data Analysis    81
    What is Exploratory Spatial Data Analysis`?  82
    Exploratory Spatial Data Analysis  83
    Exploratory Spatial Data Analysis tools  84
    Examining the distribution of the data  95
Examining the distribution of your data  98
Looking for global and local outliers  99
Identifying global and local outliers  101
Looking for global trends  103
Looking for global trends  105
Examining spatial autocorrelation and directional variation  106
Examining spatial structure and directional variation  108
Understanding covariation among multiple datasets   109
Understanding spatial covariation among multiple datasets   111
5   Deterministic methods for spatial interpolation
    How Inverse Distance Weighted interpolation works   114
    Creating a map using ID W   118
    How global polynomial interpolation works  120
    Creating a map using global polynomial interpolation   122
    How local polynomial interpolation works  123
    Creating a map using local polynomial interpolation  125
    How radial basis functions work  126
    Creating a map using RBFs  129
6   Creating a surface with geostatistical techniques
    What are geostatistical interpolation techniques?   132
    Understanding the different kriging models  133
    Understanding output surface types   135
    Creating a kriging map using defaults  136
    Understanding transformations and trends        137
    Understanding ordinary kriging  138
    Creating a map using ordinary kriging  139
    Understanding simple kriging  143
    Creating a map using simple kriging  144
    Understanding universal kriging  150
Creating a map using universal kriging  151
Understanding thresholds   153
Understanding indicator kriging  154
Creating a map using indicator kriging  155
Understanding probability kriging  156
Creating a map using probability kriging  157
Understanding disjunctive kriging  159
Creating a map using disjunctive kriging  160
Understanding cokriging  165
Creating a map using cokriging  166
7   Using analytical tools when generating surfaces   167
    Investigating spatial structure: variography   168
    Modeling semivariograms and covariance functions   175
    Determining the neighborhood search size  181
    Determining the neighborhood search size  185
    Performing cross-validation and validation   189
    Performing cross-validation to assess parameter selections   193
    Assessing decision protocol using validation   195
    Comparing one model with another  197
    Comparing one model with another  199
    Modeling distributions and determining transformations  200
    Using transformations (log, Box-Cox, and arcsine)  204
    Using the normal score transformation   205
    Checking for the bivariate normal distribution  206
    Checking for bivariate distribution  209
    linplementing declustering to a山ust for preferential sampling  211
    Declustering to a山ust for preferential sampling  214
    Removing trends from the data  216
    Removing global and local trends from the data: detrending  218
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