3D archaeological data visualization

Radiocarbon date with features, profile, and dominant shell species. You can see everything except the shell in the link below. All those points are too much data to upload for a blog!

While videos of  a GIS 3D screen are the way we’ve shown the data  till now, it allows for no user control other than pausing and rewinding. Alyssa has found a way to present the data in an interactive way using ArcGIS CityEngine WebViewer. This is great for the researchers working with us at other institutions on things like faunal remains, but we can also make classes of data available to blog viewers. Check out the links here:

Its a fair bit of data, but should take less than a minute or so on a fast connection.  This won’t work for tablets, or Internet Explorer.  It should be OK in Google Chrome but your graphics card will need to be compatible with WebGL. Some computers (like the one I’m typing on) will sometimes work and sometimes tell me I’m WebGL non-compatible even though I am.  Try clicking the “reload” rather than the “ignore” button if you have problems.

Play with it!!  It will move and rotate similar to Google Earth. You can select layers, and data classes within layers. If you double click on a layer, it will zoom to view the extent of that layer and will use the centre to rotate (great for looking at the features). We’ve put a few of the features, some profiles, and radiocarbon dates for viewing and may add some artifact classes in the near future. Select the “i” information mode then click on one of the big coloured balls to see the attribute data including real-world coordinates and dating information.

We’re using this information to define our archaeological component boundaries (that is, grouping the layers that date to the same period of occupation so we can see what artifacts and other data is characteristic of each period, and how this changes through time).  In addition to what you can see in the link, we also use stratigraphic break points in lines and surfaces, and classified point cloud data.  The graphic above shows what you will be able to see, plus mussel-rich vs clam-dominant layering that seems to differentiate between two occupations.

 Let us know what you think!