Building out of the context of Anwen’s recent work on her Isle of Wight case study, we have recently been playing around with sampling biases in the PAS. This is in very large part based upon the pioneering work of Katie Robbins, who did her PhD and is doing a postdoc on the subject (see references below: Katie’s thesis is available online).
Katie discussed many different relevant factors in her work, but three stood out to us as being particularly suitable for spatial modelling on a national scale: land cover, obscuration, and proximity to known monuments. Other factors, such as landowner permissions or proximity to detectorists’ houses, would be very difficult to map nationally without a great deal of work.
Land cover: Using a simple reclassification of LCM 2007 data (via Edina Digimap), around 69% of PAS findspots of our period fall upon arable land, c.21% on grassland, c.4% in suburban areas, just short of 3% in woodland, and c.1% in urban areas. Other land cover types each accounted for less than 1% of PAS findspots. The affordance surface constructed for this category was given a weighting of 1.0 for arable cells, with each other type given a weighting relative to this (e.g. grassland was given a rating of 0.2133/0.6914 or 0.31).
Obscuration: Various other factors should completely block out the possibility of finding artefacts through metal detecting (although other finding methods might still result in discovery, such as finding something sitting on a molehill whilst on a walk). Easily mappable elements that fall within this category are: scheduled monuments (via EH), Forestry Commission land (via the Forestry Commission), ancient woodland, country parks, local nature reserves, national parks, RAMSAR sites, SSSIs (all via Natural England), and built up areas (via OS OpenData). The affordance surface was constructed by combining shapefiles for all of these elements, calculating the percentage obscuration of 1 by 1km grid cells and then constructing a kriged surface from the centroids of that data with 100x100m cells. This was then reclassified so that 0.0 was high obscuration (i.e. low affordance) and 1.0 was low (i.e. high affordance). Incidentally, the South Downs National Park is the one National Park with a relatively high number of PAS finds, as this was only founded in 2011, but I decided not to correct for this at this time.
Proximity to monuments: I undertook a simple spatial concurrence test of 1 by 1km grid cells (via our latest synthesis iteration: see this post for discussion of methodology) of presence of finds against presence of “monuments” (in the broadest sense) of each broad monument class for each of our period categories (e.g. Roman finds vs Roman agriculture and subsistence). The major areas of concurrence between (broadly) contemporary finds and monuments were with Roman monuments of most types and early medieval monuments of a funerary nature. Centroids of grid cells containing Roman monuments of most types or early medieval funerary monuments were used to construct a kernel density estimate layer, which was then tested against the PAS distribution for our period. However, the relationship was not particularly strong, therefore this layer was reclassified so that any value above the first quantile of the surface was given an affordance value of 1.0, with values below that being classified relative to the first quantile.
The relationship between these three derived affordance surfaces and the relevant PAS data was then graphed to see how valid the model appeared. Each line produces something close to the expected pattern.
Combining the three input factors into a mean averaged model produces a very strong result in terms of spatial patterning. Looking at the black combined line on the graph, we can see that c.60% of PAS records have an affordance (‘bias on the axis title’) value of over 0.8 and that c.90% exceed 0.6. This is a strong pattern, showing that areas of high affordance on our map are much more likely to feature PAS finds than areas with low affordance.
Plotting individual findspots onto the map of this surface shows that most fall within high affordance areas. We can also see this quite clearly if we plot a kernel density estimate of PAS finds (Bronze Age to early medieval) over the affordance surface (red is low affordance, blue is high), although the interpolation does result in some false overlaps with small areas of low affordance (particularly in East Anglia):
Two things stand out from this map: (a) that finds cluster in areas of high affordance; and (b) that there are areas of high affordance with few finds. (a) is an excellent result as it shows that the model is teaching us something valid. (b) can be explained in several possible ways (most likely a combination of all): differences in detecting practice / differences in reporting practice / the presence of other biases feeding into affordance but not included in the model.
There are some areas of “double jeopardy” feeding into this model, particularly between the obscuration and land cover layers (e.g. buildings appear in both land cover as urban / suburban and in obscuration; most national parks are of an upland / wild character in land cover). However, as the pattern seems robust, I am not too worried about this for now. A more developed model might, instead of the mean average of the three surfaces, be the mean average of the land cover and monument surfaces multiplied by the obscuration surface. I will experiment with this later, perhaps.
As such, although our model is clearly not perfect (but then, no model ever will be), it does help us to understand something of the underlying affordances helping to shape the distribution of PAS data. The next stage in this analysis will be to use the affordance surface to try to smooth out variation caused by this factor in our PAS distributions.
Robbins, Katherine. 2013a. From past to present: understanding the impact of sampling bias on data recorded by the Portable Antiquities Scheme. University of Southampton, Archaeology, Doctoral Thesis.
Robbins, Katherine. 2013b. “Balancing the scales: exploring the variable effects of collection bias on data collected by the Portable Antiquities Scheme.” Landscapes 14(1), pp.54-72.