Available with Geostatistical Analyst license.
Quantitative data exploration
After mapping the data, a second stage of data exploration should be performed using the Exploratory Spatial Data Analysis (ESDA) tools. These tools allow you to examine the data in more quantitative ways than mapping it and let you gain a deeper understanding of the phenomena you are investigating so that you can make more informed decisions on how the interpolation model should be constructed. The most common tasks you should perform to explore your data are the following:
- Examine the distribution of your data
- Look for global and local outliers
- Look for global trends
- Examine local variation
- Examine spatial autocorrelation
Not all these steps are necessary in all cases. For example, if you decide to use an interpolation method that does not require a measure of spatial autocorrelation (GPI, LPI, or RBF), then it is not necessary to explore spatial autocorrelation in the data. It may, however, be a good idea to explore it anyway, as a significant amount of spatial autocorrelation can lead to using a different interpolation method (kriging, for example) than the one you had originally intended to use.
The ESDA tools
To help you accomplish these tasks, the ESDA tools allow different views into the data. These views can be manipulated and explored, and all are interconnected among themselves and with the data displayed in ArcMap through brushing and linking.
The ESDA tools are:
Working with the ESDA tools: brushing and linking
The views in ESDA are interconnected by selecting (brushing) and highlighting the selected points on all maps and graphs (linking). Brushing is a graphic way to perform a selection in either the ArcMap data view or in an ESDA tool. Any selection that occurs in an ESDA view or in the ArcMap data view is selected in all the ESDA windows as well as in ArcMap, which is linking.
For the Histogram, Voronoi Map, QQ Plot, and Trend Analysis tools, the graph bars, points, or polygons that are selected in the tool view are linked to points in the ArcMap data view, which are also highlighted. For the Semivariogram/Covariance tools, points in the plots represent pairs of locations, and when some points are selected in the tool, the corresponding pairs of points are highlighted in the ArcMap data view, with a line connecting each pair. When pairs of points in the ArcMap data view are selected, the corresponding points are highlighted in the Semivariogram/Covariance plot.