Problem/Question:

Traditionally, archaeologists classify projectile points based on a number of different morphological features, such as the presence or absence of distinct features, or the shape of the hafting element.  However, projectile points can vary widely in the shape, size, and construction technique. This means that their classification can often be a matter of opinion with little objective data to support it. With the ever increasing availability of 3D scanning and morphometric analysis, it is possible to add level of statistical confidence to these classifications. The goal of this project is to determine how well these new classification methods match traditional classification schemes.

Data set:

The data for this project consists of a number of projectile from the Pilcher Creek archaeological site in northeastern Oregon. In total, 44 complete projectile points were recovered from the site, which were originally classified into 3 separate categories, corner notched, lanceolate, and stemmed lanceolate. The majority of the points are made of fine grained basalt, with a small percentage made of obsidian. These points were originally dated to approximately 8,000 years BP, making them some of the oldest artifacts in Oregon.

Hypothesis/ Approach;

It is expected that the classification groups generated through this analysis will follow mostly with the original classification. and it may be possible to subdivide the points into more than the original 3 categories. The rough work flow for this project starts with creating a high resolution 3D of the artifacts using structured light scanning. These 3D scans are then run through as series of ArcGIS tools to generate a large set of descriptive data which can be used to compare the different points. This data for all the points can then be run through clustering analysis and principal component analysis in order to group artifacts together based on the similarity of their morphology.

Significance;

There are a number of benefits to performing this type of analysis. The first is that the 3D scans of the artifacts are much easier to share with the rest of the archaeological community than the actual physical artifacts. It also provides a way to classify these artifacts with calculable certainty, and without bias. It also lays the groundwork for future research and comparison. As more artifacts are 3D scanned and made publically available, it will be possible to create a comparative collection and classification system which will be greater in size and accuracy than anything previously.

Preparation:

I have a decent amount of experience experience with Arc and a little QGIS. I am comfortable with Python and ArcPy. I used to know a little R, but it has been awhile since I have used it.