Available with Geostatistical Analyst license.
As mentioned in The geostatistical workflow, there are many stages involved in creating a surface. The first is to fully explore the data and identify important features that will be incorporated in the model. These features must be identified at the beginning of the process because a number of choices have to be made and parameter values have to be specified in each stage of building the model. Note that, in the Geostatistical Wizard, the choices you make determine the options that are available in the following steps of the process, so it is important to identify the main features of the model before starting to build it. While the Geostatistical Wizard provides reliable default values (some of which are calculated specifically for your data), it cannot interpret the context of your study or the objectives you have in creating the model. It is critical that you create and refine the model based on additional insights gained from prior knowledge of the phenomenon and data exploration in order to generate a more accurate surface.
The following topics provide more detail regarding data exploration and information on how to use the findings when building an interpolation model:
- Map the data—Covers the first step in data exploration: mapping the data using a classification scheme that shows the important features.
- Exploratory Spatial Data Analysis—Provides an overview of the Exploratory Spatial Data Analysis (ESDA) tools and their uses.
- Data distributions and transformations—Covers the Histogram, Normal QQ Plot, and General QQ Plot tools, as well as data transformations.
- Looking for global and local outliers—Presents techniques for identifying global and local outliers using the Histogram, Semivariogram/Covariance Cloud, and Voronoi Map tools.
- Trend analysis—Examines how to identify global trends in the data using the Trend Plot tool.
- Examining local variation—Indicates how to use the Voronoi Map tool to show whether the local mean and local standard deviation are relatively constant over the study area (a visualization of stationarity). The tool also provides other local factors (including clustering) that can be useful in identifying outliers.
- Examining spatial autocorrelation—Demonstrates how the semivariogram and covariance and cross-covariance clouds are built and how they are used to explore spatial autocorrelation and spatial cross-covariance in the data.