The analysis of data typically starts by plotting the data and calculating statistics that describe important characteristics of the sample. We perform such an exploratory analysis to:

1. familiarize ourselves with the data and

2. detect patterns of regularity.

Graphical methods are useful to portray the distribution of the observations and their spatial structure. Many graphical methods are available and even more can be found and tailored to a specific application. The modest objective of this chapter is to review common tools of frequency analysis as well as the experimental variogram. Exploratory analysis is really a precursor to statistical analysis.

Exploratory analysis scope

Before computers, hydrogeologists used to spend hours transcribing and plotting their data. Although time consuming, labor intensive, and subject to human errors, one cannot deny that this process enhanced familiarity with data to the extent that the analyst could often discern patterns or spot “peculiar” measurements. This intimacy with one's data might appear lost now, a casualty of the electronic transcription of data and the extensive use of statistical computer packages that perform the computations.

However, data analysis and interpretation cannot be completely automated, particularly when making crucial modeling choices. The analyst must use judgment and make decisions that require familiarity with the data, the site, and the questions that need to be answered. It takes effort to become familiar with data sets that are often voluminous and describe complex sites or processes. Instead of striving for blind automation, one should take advantage of available computers and computer graphics to organize and display data in ways unimaginable using manual methods (for review of basic ideas see, for example, [20]).