Data Visualization with GIS - Part One

While archaeologists traditionally rely on survey techniques such as shovel testing in order to locate sites, the decision about where to excavate blocks and trenches of test units is not a wild guess. In fact, the ongoing investigation at Arcadia benefits considerably from powerful site-prediction tools and methods designed to get the most out of the data we recover while simultaneously streamlining the interpretive process.

Remember when Indiana Jones said that "We don't follow maps to buried treasure, and X never, ever marks the spot?"
Never, ever listen to what this guy has to say about Archaeology.

The lab at the Archaeology Institute has been sorting and cataloging the artifacts recovered from shovel testing since the first field survey at Arcadia and has built a database that can be queried according to various "typologies." In order to say anything meaningful about the people who lived and worked at the village, it is important that we clearly establish the criteria for sorting and grouping the artifacts that we recover beforehand, so our database is capable of a variety of different approaches. One useful typology is Stanley South's artifact group model, which organizes artifacts according to their basic functions, such as "Kitchen," Personal," or "Architecture." Armed with this data, we can present it in useful ways through Geographic Information Systems software such as ESRI's ArcMap and ArcScene in a technique known as data visualization.

This is a three-dimensional representation of the results of a preliminary statistical model for artifact distribution. The sample data consists of the combined count of all cultural material recovered from Area A shovel tests in 2009, as shown by the triangles. By applying a technique called "interpolation," the software uses a mathematical equation that predicts the artifact counts at any given point in the sample area; Basically, the computer is told to predict what might be in a shovel test before we dig it. This is represented in the diagram both in color (Red means higher counts) and in height.

With the help of maps created with the help of GIS software, deciding where to excavate test units is literally as simple as looking up the locations that contain high predicted counts, cross-checking it with remote sensing anomalies (In both images, they are the red and blue lines,) and laying them out with the total station. Indy never had it this good.