This past week, I’ve started the intimidating task of crunching the data produced over three field seasons with the Western Argolid Regional Project. While we’ve made a few efforts to make sense of the data over the past five years, our dataset has been varying degrees of provisional and more pressing matters in the field and in the storerooms often attracted our attention. With the field and storeroom over five thousand mile away and our data as clean as any project can reasonably expect, now is the time for number crunching!
In the past, we have tried to focus on a number of rather well defined publication projects: a preliminary report and various side projects that required some attention. This year, we wanted to shift our attention back to analysis and instead of producing fully formed publishable quality manuscripts, we wanted to produce some reports and moved the project forward without the pressure of polished final publications.
This summer, I elected to look at the variables that shaped artifact recovery in the field with the hope that this might inform how we analyze artifact patterns in the landscape. So far, I’ve just started but I can make a few observations (and these, if I recall correctly, largely follow observations that I made several years ago when analyzing a rougher version of the same data).
First, the most significant variable in artifact recover is surface visibility. Survey archaeologists have know this for years so it comes as no surprise. It appears that sherd density tracks pretty closely with density up to the highest visibility units (100%) where densities drop rather steeply (as does sample size!).
Tile densities track visibility a bit less regularly and follow a kind dromedary curve with a hump at 40% visibility and another peak at 90%. The reason for this is a bit unclear. It may be that tiles are generally a bit more visible in the plow zone so surface visibility doesn’t impact their recovery quite as dramatically. A good example of this is that many of the highest density units with tile are from the immediate vicinity of collapsing houses at Chelmis and Iliopouleika (6 of the top 10 and 13 of the top 20), and these units tend to have visibility below 50%. In these units, tiles are abundant and often fairly well preserved and this likely contributed to their relatively high recovery rates even from units with lower visibility.
Second, my old buddy David Pettegrew has been running similar analyses on the EKAS data (which is rapidly becoming available at Open Context). Of particular interest to him (and to us!) is the impact of background disturbance on artifact recovery rates. As we say in the WARP field manual: this category represents the degree to which a field walker’s ability to see artifacts on the ground is hindered or obscured. This is a distinct category from visibility since even a field with 100% visibility could still have heavy background disturbance. A useful rule of thumb is that when walkers are spending much of their time picking up rocks they think are pieces of pottery, the background disturbance is heavy.
There are any number of ways to measure background disturbance. For example, units with high background disturbance took about 2 minutes longer to walk than units with moderate or light background disturbance despite having an average visibility of 68.5% as compared to 47.3% and 57.0% for light and moderate background disturbance respectively. Units with high or moderate background disturbance had a tendency to produce more “Stone, Unworked” (which are really just rocks) than those with light or none (2.8 and 2.3 rocks from units with high and moderate background disturbance and 1.9 rocks from those with light and none).
On EKAS, there was a relationship between background disturbance and artifact recovery rates. In fact, David has proposed a metric that takes into account background disturbance and visibility to understand recovery rates in those units (and he has plans to unpack some of this in a future publication). That said, when we analyze the background disturbance from the Western Argolid, it doesn’t seem to have a particularly strong relationship with recovery rates at least as manifest in artifact densities.
For units with the heaviest background disturbance (n=672), in fact, artifact densities tracked more or less along with those from similar visibility units with the exception of two spikes at 40% (n=33) and 90% visibility (n=88) where units with heavy background disturbance produced higher densities than might be expected from visibility alone. In contrast, units with moderate and light background disturbances more or less followed the expected trajectory based on visibility alone. This suggests that background disturbance did not exert a predictable influence over artifact recovery.
We obviously recorded more variable than background disturbance and I have began to run quarries on our data that looks at these variable as well. So, if you’re a survey archaeology “method-head” you might want to stay tuned for more “exciting” methodological reflections in the coming week.
In the meantime, I also ran some queries based on artifact recovery and vegetation in our units. We had standardized recording terms for vegetation in each unit which ranged from “weeds,” “maquis,” and “phrygana,” to “citrus,” “olives,” “grain,” and “grain stubble.” It was possible to select multiple vegetation types for each field resulting in 27 combinations which appeared in at least 50 units. Various combinations produce artifact densities that under performed what one might expect from visibility alone.
The lowest visibility were typically flat units lower elevations (< 200 masl) with citrus or stone fruits (and not infrequently weeds). My guess is that these units were as likely to be shaped by their proximity to the Inachos River and its wandering course that deposit sediments carried toward the Argolidic Gulf. In contrast, units with higher slopes and elevations, often populated with olives, weeds, and (mostly volunteer) grains produced artifact densities that exceeded those predicted by visibility alone. This is as likely the result of historical phenomena as artifact recovery variables and shaped by the dense scatters associated with the fields around the acropolis of Orneai.
As you might guess, such hypotheses will have to be tested using our GIS data, but for now, I’m mostly just crunching numbers without too much attention to spatial concerns. Once again, this means more “method-head” goodness is likely to appear in these pages in the near future!