I have just got back from the 2013 Computer Applications in Archaeology (CAA) conference in Perth, Australia. The conference was held in the University Club at the University of Western Australia:
UWA is in western Perth, close to the estuary of the aptly named Swan River:
The conference overall was a fun one, with particularly interesting presentations by Oxford’s own John Pouncett and by his boyhood mentor, Dominic Powlesland. I presented a paper in John and Gary Lock’s session on spatial scale, about how different scales inter-operate in the context of Englaid data. I will summarise it on here at some point in the future, once we’ve thought through our ideas a bit more.
After the conference, I explored some distinctly non-English landscapes:
Moving on from my holiday snaps, I have been thinking a little more about temporal fuzziness with regards to PAS data (see previous post). This time, I built in the data contained in the early medieval coin corpus at the Fitzwilliam Museum, Cambridge (EMC), to provide extra detail for the post-Roman period.
Using the “standard” time brackets discussed in the previous post, I then divided the data up according to (some of) our broad object type categories. These are what we call “soft” categories, so that certain types of object can appear in more than one category (e.g. axes are categorised as both weapons and tools). We can then produce graphs of the summed probabilities for each type, showing change in their deposition over time (x-axis is time, y-axis is summed probability):
Obvious things to note are the peaks in coinage deposition in the late Iron Age and the 4th century and the peaks in personal decorative items in the early Roman and during the earliest early medieval. However, because of the vastly different amounts of objects found in each category and in each time period bracket, it is hard to pick out subtler patterning. To do so, we can calculate the mean value and standard deviation for each category and then express the values in variation from the mean (in standard deviations) for each category (x-axis is again time, y-axis is summed probability in plus or minus standard deviations [0 is the mean, +1 is +1 st. dev., -1 is -1 st. dev., etc.]):
This graph then shows the same patterns we could draw out from the previous graph, but brings out various other details. Most obvious is the huge peak in weapons and tools in the Bronze Age (especially later), but other patterns also come out (relatively high amount of tools during the Roman period; relatively high amount of weapons [i.e. average] in the earliest early medieval; etc.).
Similar graphs could be produced for regions of the country, rather than types, or for types within a region of the country. These ideas still need further exploration, but I think they begin to show the power of using a fuzzy probability approach to the analysis of the temporality of our data.