All those webmaps: that was a bit exhausting, eh? Click here, click there, compare with the screenshot, click on that, click on this… webmaps are not GIS, even though they can be related.
Spatial archaeology using GIS looks at the patterning of the remains of human activity in (usually geographic) space over time. It can encompass working with geographic information systems, and it can encompass web mapping (on which see ODATE 3.5). A geographic information system is a way of stacking a series of maps of the same region that contain different kinds of information. Perhaps one layer is a map of soils. Another might be information on slope (steepness) or aspect (direction the slope faces). Another layer might have information on the relative fertility of the soil. To this stack we might add distribution maps of human activity for a given period (perhaps scatters of pottery). The power of GIS lies in its ability to query the ways these different layers might intersect. How does human activity 3000 years ago in the area intersect with the fertility of the soil, and the aspect of the hills? Is there a preference for one kind of landscape rather than another? If we imagine people stood 1.8 m tall, and there’s no vegetation in the way, what might they have been able to see?
In your lab workbench folder, under week-3, are a series of computational notebooks. The first (linlithgow_spatial.ipynb) is a modified version of work created by Dr. Rachel Opitz that shows you how to retrieve archaeological spatial data from the Archaeology Data Service in the UK using Python. In this notebook, she’ll show you how to query and visualize the data concerning a medieval graveyard, to ask some questions about mortuary practice in that community.
Then, there is a computational notebook called r-for-gis.ipynb that represents a modified version of a tutorial that Dr. Daniel Contreras put together. You need to run the r_kernel_install.ipynb file FIRST before you explore Contreras’ materials. Run the cells, in order, in r_kernel_install.ipynb, and if everything reports back ok, close and then re-open your workbench. We’re doing this so that your workbench gets fitted out with the R kernel, which will allow it to understand the R code in Contreras’ notebook. This notebook grabs a digital elevation model and shows you how to derive some other geophysical elements from it, and then to map some point data against that to explore an archaeological hypothesis.
Finally, because maps are cool and LIDAR (’light detection and ranging’) is even more cool, there is a notebook that shows you how to visualize LIDAR data to create a digital elevation model that you might use in other analyses. LIDAR works by shooting beams of laser light from on high down towards the ground. The amount of time it takes for the light to bounce back to the emitter/receiver allows the computer to work out the distance to the object or the ground that the light bounced off. That is to say, we can get a pretty good 3d representation at fantastic levels of resolution (the wikipedia article is pretty good on this). LIDAR is also great at penetrating foilage and finding the ground between the leaves of trees and jungle canopy and so on, which is why it has been a fantastic tool for discovering large-scale structures like roads, canals, and entire cities. The notebook will load up with my results already showing, so you’ll know what to expect. The final cell converts the lidar data into a tif digital elevation model, which could then be used in a GIS for further analysis. The notebook will point you at Carleton’s Macodrum Library LIDAR datasets, although any .las or .laz files can be used - can you find, and visualize, archaeological lidar?