Pondering regionality

I have recently been pondering the definition of regions, in the sense of carving England (or any country) up into contiguous zones of particular archaeological character. I would suppose that as a method of archaeological enquiry, this probably goes back at least as far as Fox’s division of Britain into “lowland” and “upland” zones along a dividing line running approximately from Dorset to Yorkshire. As a modern practice, I would suggest that recent interest in defining regions probably arises, at least in part, from the influential work of Roberts and Wrathmell (2000).

The reason why I have especially been thinking about this subject of late is due to the way in which two projects contemporary to our own have gone about structuring their reporting of their results. Their final report currently in press (Rippon et al. 2015), the Fields of Britannia (FoB) project divided the country (in this case being England and Wales) up into a series of regions (made up of groups of bio-geographical pays”). Similarly, the Roman Rural Settlement Project (RRSP) has also divided the country up into their own set of regions based upon the archaeological character of the excavated evidence found within each. Both of these projects based their regions around conglomerations of Natural England’s “Natural Areas“.

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Regions defined by other projects

If we compare these various regions on a map against the “Settlement Provinces” defined by Roberts and Wrathmell (R&W), we can see that there are broad similarities but also substantial local differences between the various regions (and provinces) defined. Herein lies the major problem with projects defining their own regions for analysis and reporting: it makes cross-comparison between different projects’ results difficult. For example, the Chilterns and the Berkshire Downs both fall within the south east regions of R&W and FoB, but within RRSP’s central zone: as such, can their respective “central” zones truly be compared? The simplest solution to this would be defining regions based upon modern political boundaries or, say, 100x100km grid squares. However, such an approach would result in regions that are archaeologically and bio-geographically irrelevant, which is very far from ideal (and so not recommended here!).

More fundamentally perhaps, I am also not convinced that archaeological remains (and thus, by implication, past human culture) truly lacks variety across such continuous areas of space and changes according to such sharp boundaries. I am sure that all of the researchers involved would agree with me on that and there is no doubt that defining regions helps in formulating ideas / arguments and in reporting results. However, I just wonder if there is a better way to structure our space? Some degree of structure is necessary, or all would be chaos and incomprehensible, but could alternative structures be preferrable?

2 HiLo model
Another experimental model (HiLo)

As an experiment, I constructed a regional model for England, but one that did not result in continuous regions, but rather fractured zones spread across the whole country. This model was based upon a mixed classification of elevation and terrain ruggedness and resulted in three new zones: a coastal zone (which largely seems to accord with former wetland areas), a lowland zone, and a highland zone (which seems to capture every important range of hills in England). These zones can exist in pockets within one another: they are not contiguous. Although not (by design at least) archaeologically relevant, these zones certainly have a degree of bio-geographic meaning. Furthermore, they would be reproducible by other scholars, assuming I publicised their construction method. As a Warwickshire man, I am particularly taken with the result that my county almost looks like a “natural” division of the country!

3 regions vs HiLo
Other regions against HiLo

If we compare these three “HiLo” zones (named for Oxford’s infamous Jamaican inn) against the regions of the other projects we can again see some similarities between the borders of my zones and those of the other projects, but again with substantial local differences. Obviously, if we were to use my HiLo regions for reporting on our project, we would just end up compounding the problem of difficulty of comparison, but the experiment remains of interest.

elev_RandW
Elevation: Roberts and Wrathmell
elev_FoB
Elevation: Fields of Britannia
elev_RRSP
Elevation: Roman Rural Settlement Project
elev_HiLo
Elevation: HiLo

I then tested each set of regions against a series of other datasets: elevation, terrain ruggedness, broad soil types, soil wetness, etc. The graphs above show just the elevation results, but the broad conclusions were similar for all comparisons. Essentially, the FoB and RRSP regions look far more distinct than the R&W provinces. This is hardly surprising as they are of smaller spatial extent: the smaller a sample area, the more distinct from the general “population”/pattern a variable ought to tend to be. This is clearly the case here. However, the HiLo model sits somewhere in between. It only has three zones, but they appear far more clearly differentiated than the R&W provinces. As such, we can conclude that they have greater geographic differentiation, due to their non-contiguous nature, despite being of similarly large extent.

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Regions: archaeological variation (area normalised)
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Regions: archaeological variation (percentage)

As a final test, I then compared each set of regions against our archaeological data, using our coarsest level of thesaurus categories. I did this for each broad time period, but the results shown above are for all EngLaID time periods combined (unspecified prehistoric, Bronze Age, Iron Age, Roman, early medieval). The conclusions, interestingly but perhaps not surprisingly, are very similar to those seen when comparing against the “natural” factors described just above. FoB and RRSP regions look fairly distinct, R&W rather homogeneous (albeit with less dense data in the north west), and HiLo regions are more distinct than R&W but less so than the others. Again, the size of regions remains key (due to the MAUP).

Since undertaking these comparative experiments, I have been reading a recent report by Historic England’s Andrew Lowerre (2015). In the second half of the report, Lowerre uses a mixture of environmental variables alongside Roberts and Wrathmell’s data to define regions using automated clustering techniques. The regions that he produced (across a series of different models), much like my HiLo model, are non-contiguous and possess fuzzy borders. As such, to me at least, they seem much more representative of the data than regions defined manually. I wonder if this type of automated region creation is the way forward if we wish to define regions for our analysis and reporting?

Regions are undoubtedly a useful and intuitive way to divide up space that makes analysis and reporting of results within the context of a project relatively simple and straightforward, both in terms of how a team thinks about their data and in terms of how an audience may digest the same. However, the cross-comparison issue is distinctly problematic when one begins to think beyond the bounds of the results of a single project. We could potentially define a set of regions based on the natural environment that all projects should attempt to use, but we as archaeologists often seem to be naturally inclined to always do our own thing, so I am not sure that would be fruitful. Plus the set of regions defined might not be relevant across multiple time periods.

As such, I do wonder if we ought to avoid the idea of archaeological character regions altogether and just talk about variation in data across space. So long as that data is quantifiable and mappable as continuous fields, then cross-comparison becomes simple: map overlay is after all the most obvious application and strength of GIS, with whole suites of tools and methods dedicated to it.

This post is not intended as a criticism of the methods of other projects, which have undoubtedly proved fruitful and interesting in each case. I just wanted to express why I feel we (as EngLaID) ought to avoid regions in our reporting, especially as a project looking across traditional period boundaries. Others might disagree, but I do feel the cross-comparison issue of bespoke regions is a thorny problem, particularly for those interested in broad syntheses across time and space.

Chris Green

References

Lowerre, A. 2015. Rural Settlement in England: Analysing Environmental Factors and Regional Variation in Historic Rural Settlement Organisation Using Regression and Clustering Technique. Portsmouth: English Heritage. http://research.historicengland.org.uk/redirect.aspx?id=6288

Rippon, S., C. Smart and B. Pears. 2015 (in press). The Fields of Britannia: Continuity and Change in the Late Roman and Early Medieval Landscape. Oxford: Oxford University Press.

Roberts, B. and S. Wrathmell. 2000. An Atlas of Rural Settlement in England. London: English Heritage.

Mapping pottery

Following on from suggestions (primarily by Prof. Barry Cunliffe) at our Academic Advisory Board meeting last year, we started thinking about how we might map aceramic (or minimally ceramic-using) zones through our time period. Due their general commonness and generally diagnostic nature, ceramic finds are probably the most commonly used method for dating archaeological contexts and, thus, by extension sites as a whole. As such, in areas where ceramic objects were little used, it becomes more difficult (and probably more expensive) to date sites. This, in turn, is likely to result in sites in aceramic areas being less precisely dated. This could, therefore, bias the distribution of sites of a particular period in the archaeological record, as sites in aceramic zones within a particular period are less likely to be securely dated to that period.

However, actually mapping aceramic zones is not especially easy. To do so, one must first map areas where ceramics are used, and collating data on that scale for 2,500 years of human history would almost certainly be a research project in itself on a similar scale to EngLaID as a whole. Therefore, we had to try and obtain the results of previous attempts at pottery synthesis.

We began with prehistory. The only existing national database which we could find of later prehistoric (Later Bronze Age to the Roman conquest) pottery was that created by Earl et al. (2007), archived at the ADS. The data collection for that project took place in 1995-6, so it is almost twenty years out of date, but it was the only reasonably comprehensive data source available to us. We hope that the broad brush picture will have not changed substantially in the past twenty years (albeit see below for the early medieval period), but until another such project is undertaken it is impossible to be certain.

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Density of later prehistoric pottery records

Simply plotting the density of records in this database shows a distinct bias in the distribution of later prehistoric pottery towards the southern and eastern half of England (essentially, Cyril Fox’s “lowland” zone of Britain), with the exception of a notable lack of pottery in the Weald and on the South Downs, and small peaks of pottery in western Cornwall, East Yorkshire, and County Durham. North Devon and large swathes of the West Midlands and the north west show a distinct lack of ceramic usage (or at least recovery by archaeologists).

2 earl_pot_density_phased
Density of LBA to EIA vs MIA to Conquest period pottery records

We can nuance this picture slightly by looking at change over time.  Following discussion with the prehistoric experts on the team, I split the data temporally into two broad time periods: Late Bronze Age to Early Iron Age, and Middle Iron Age to the conquest. The pattern that results seems to show a movement (of the peak in density) away from Wessex and northwards into the East Midlands, which could be the result of any number of factors (population growth, environmental change, etc.).

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Density of unspecified prehistoric pottery records

However, there are also large numbers of unspecified later prehistoric records in the database (especially in East Anglia), so temporal patterns should not be too heavily emphasised.

4 earl_pot_sherdcount_ALL_overEI
Sherdcounts of prehistoric pottery over density of prehistoric records in EH’s Excavation Index

Many of the records in the database also record sherd counts of the assemblages recorded, which helps to nuance the picture further. In an attempt to see if the patterns produced when mapping the database records simply stemmed from where archaeological work takes place (which inevitably they must to some extent), I mapped the records against the density of later prehistoric events recorded in English Heritage’s Excavation Index. As the map above shows, there does appear to be a fairly strong correlation. However, there are low peaks in the density of events in the north west which are not represented in the pottery database, so the pattern is not entirely determined by modern archaeological practice.

5 earl_pot_sherdcount_ALL_over14Cprob
Sherdcounts of prehistoric pottery over modeled radiocarbon probability for same period

To take this further, I also mapped the sherd counts against a modeled surface of radiocarbon probabilities for the same period (see previous post). This seems to show that there are areas of relatively high radiocarbon probability in apparently aceramic zones, suggesting that activity was taking place in those areas at that time. This helps to suggest that our aceramic zones, although partially biased by patterns of modern archaeological practice, are reasonably likely to be real. For later prehistory, then, it does appear that there was less use of pottery in the north west, the West Midlands, and in north Devon.

Moving on to the Roman period, the best source of national level data which we could find is Paul Tyers’ excellent Potsherd website. Naturally, collating sherd count level data for the Roman period would be an immense task (due to the incredible amount of ceramics deposited on Roman sites): as such, Tyers maps pottery by ware type on a presence / absence basis (by 10x10km square). His maps are all dated 2004, so we assume that the data mapped is around ten years out of date. Again, it is assumed that broad brush patterns will not have changed immensely, although proving that would be difficult.

Tyers provides encyclopaedic detail on his website, but does not offer direct downloads of his data. Fortunately, his maps are all relatively high resolution and all constructed in the same way, so it is possible to perform various trickery on them in order to study them further in GIS. It then becomes feasible to sum Tyers’ maps together and produce a map of variability in pottery wares across Romano-British England. As such, this is not directly comparable with the later prehistoric maps discussed above, as we are mapping the number of different ceramic wares deposited across England for the Roman period, rather than the density of records (i.e. site assemblages) for prehistory.

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Roman pottery variability

The map above shows the overall variability in Roman pottery across England, based on Tyers’ data. Dark blue areas have no pottery (the aceramic zones we sought) and red areas have many different types of pottery. The results are quite interesting: the greatest variability in pottery wares is in a similar region to the greatest density of later prehistoric pottery records, i.e. in the south and east of England. However, the zone covered is significantly larger and there are also further significant peaks in otherwise “quiet” areas, particularly around the Roman cities and military sites.

7 tyers_ware_density_domestic_imported
Roman pottery variability: domestic vs imported

We can, however, take this further. Comparing variability in domestic and imported wares, we can see that the areas with greatest variety in imports were around the major settlements and, in particular, around the Thames estuary. By contrast, the greatest variability in domestic wares was more widespread.

8 tyers_ware_density_coarseware
Roman pottery variability: coarsewares
9 tyers_ware_density_fineware
Roman pottery variability: finewares
10 tyers_ware_density_terrasigilata
Roman pottery variability: terra sigilata / Samian ware
11 tyers_ware_density_mortaria
Roman pottery variability: mortaria
12 tyers_ware_density_amphorae
Roman pottery variability: amphorae

Further patterns emerge when looking at more specific groups of wares. Coarsewares are quite well spread; finewares largely restricted to the south; terra sigilata is very clustered; mortaria are well spread and possibly rural in character; amphorae are very tightly clustered into small areas.

13 tyers_ware_density_overtime
Roman pottery variability: over time (smaller version here for tablets etc.)

We can also look at change over time, which also shows some interesting patterns, with the peak of variability being most widespread (albeit largely southern) in the 3rd and 4th centuries. The strong 5th century peak in Cornwall is caused by imported wares from the eastern Mediterranean.

Overall, the patterns produced by mapping Tyers’ data in this way can potentially tell us interesting things about pottery supply in the Romano-British period, in particular in regard to economic factors (as availability of different ceramic wares must be linked to economic conditions / opportunity to some extent). Also, although we have mapped somewhat different things, certain comparisons can be made with the later prehistoric data: areas with less ceramics in the Romano-British period were less widespread than in later prehistory, but in generally the same places, especially if you mentally factor out the influence of military garrisons.

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9th century AD pottery industries (after Vince 1993; Blinkhorn and Dudd 2012)

Moving finally to the early medieval period, we struggled to find any datasets of anything like the degree of comprehensiveness of either the Earl et al. or Tyers data. The best source discovered was a fairly old article by Alan Vince (1993), which mapped the major pottery industries of the 9th century AD. However, it does appear that this map was now quite out of date, as his zone of Ipswich Ware (highlighted in red above) was much more restricted than the areas recorded recently by Blinkhorn (2012) (black dots and shaded in grey: it is assumed that the grey shading is record density by modern administrative region, but the map had no legend). This also only really covers the very end of our period, when wheel thrown pottery came back into production in England: we have no data for the mid-5th to 8th centuries. As such, it is hard to draw any conclusions at all about the early medieval picture.

In conclusion, largely aceramic zones probably existed in later prehistory in the north west, the West Midlands and the south west. These largely persisted into the Roman period, albeit with ceramic using areas around the military installations and larger settlements. In the early medieval period, we do not have enough data to reach even tentative conclusions, but we might assume that the same areas continued to use less pottery than in the south and east? Or that might be plain conjecture.

Chris Green

References:

Blinkhorn, P. 2012. The Ipswich Ware Project: Ceramics, Trade and Society in Middle Saxon England. Medieval Pottery Research Group Occasional papers.

Earl, G., E. Morris, S. Poppy, K. Westcott, T.C. Champion. 2007. Later Prehistoric Pottery Gazetteer. http://dx.doi.org/10.5284/1000013

Tyers, P.A. 2014. Potsherd. http://potsherd.net/atlas/potsherd

Vince, A. 1993. “Forms, Functions and Manufacturing Techniques of Late Ninth- and Tenth- Century Wheelthrown Pottery in England and their Origins.” In D. Piton (ed.), Travaux du Groupe de Recherches et D’Etudes sur la Céramique dans le Nord – Pas-de-Calais; Actes du Collque D’Outreau (10 -12 Avril 1992). Numéro hors-série de Nord-Ouest Archéologie, pp.151-64.

Addendum – 12/01/2015:

In an attempt to see if the aceramic zone in the north of England in later prehistory was genuine or an artefact of modern archaeological practice, we mapped hillfort excavations prior to 1997 recorded in English Heritage’s Excavation Index (mapped as green diamonds) against hillfort ceramic assemblages recorded by Earl et al. (up to 1996). The results do appear to show that, on the whole, hillfort excavations do produce pottery in the southern half of England, but largely do not in the northern half, with the notable exception of northern Northumberland. This suggests that this is likely to be a genuine aceramic zone:

15 earl_pot_sherdcount_Hillforts_overEI
Hillfort excavations recorded in EH’s Excavation Index (pre-1997) against hillfort ceramic assemblages

 

Affordances, sites and monuments

In a previous post, I looked at how we might model some of the modern factors that affect the distribution of PAS finds.  I termed these factors “affordances”, as they can be thought of as helping to govern the opportunity for metal detectorists (and others) to be able to discover archaeological material.  I then began thinking about the affordances that affect the opportunity / likelihood of sites and monuments being discovered (if they were there to begin with in the past, of course).

I would argue that the six primary routes by which sites and monuments are discovered in the present day are:

  1. As clusters of spot finds.
  2. As documentary records (or place names, etc.).
  3. Via excavation / other intrusive field evaluation (e.g. watching briefs, test pits, etc.).
  4. Via geophysical survey.
  5. Via aerial photography as crop marks.
  6. Via aerial photography as earthworks.

To these could be added other routeways, such as LiDAR prospection, but these account for quite low percentages of sites and monuments at present.  Of the affordances relating to these six possible discovery pathways, number 1 has been outlined in the post referred to above, number 2 is, I think, impossible to define / quantify, and number 4 is hard to define / quantify.  Therefore, I have focused on numbers 3, 5, and 6.

Excavation / other intrusive field evaluation:

The main affordance in this category is where the opportunity to undertake archaeological excavation (etc.) exists or has existed.  Ideally, this variable would be based on data not explicitly linked to archaeological investigation: i.e. planning decisions on major projects and small projects in sensitive areas (albeit sensitive areas brings in an archaeological element), but this data is very hard to discover and collate.  In particular, planning statistics are inconsistently archived on the UK Government websites and changes in planning authority boundaries over time make collation particularly problematic.  Although I feel that putting the effort into compiling this information (since 1990 ideally, i.e. post PPG16) would be worthwhile, it would be too great an amount of work to undertake for our current project and purposes.

Therefore, I decided to base this affordance variable on data for where excavations (etc.) have taken place.  The most complete source of national data on this is the Excavation Index maintained by English Heritage and hosted by the ADS.  I extracted relevant types (e.g. excavation, watching brief, etc.) for post-1990 events and constructed a KDE plot of the resulting distribution.  I included events resulting in discoveries of all periods in order to minimise any effects of period bias.  The results were very “peaky” in the sense that the density of events in certain cities (in particular London) dwarfed that of the rest of the country.  In order to create my affordance surface, I therefore capped off the variation at +3 standard deviations (c.1.45 events per sq. km.), and divided by this maximum value to produce an affordance surface that varied between 0 (low chance) and 1 (high chance):

EI affordance
Affordance for excavation etc.: white is low, yellow medium, and red is high.

The results look pretty convincing, with areas of well-known high amounts of fieldwork showing up in the reds (e.g. London, the Upper Thames, Peterborough, Cambridgeshire).  As such, I am pretty happy with this result.

Aerial photography:

For (both types of) aerial photographic prospection, I based the affordance on two (overlapping) factors: modern land use and obscuration of the ground surface.  As with the PAS affordance, the land use was taken from LCM 2007 data and the obscuration based upon my earlier EngLaID work using OS and BGS data (1)(2)(3).  To keep it (relatively) simple, “cropmark” affordance was built from arable land with areas obscured by human (buildings, etc.), environmental (water, woodland, etc.) and soil factors (see previous posts) removed, and “earthwork” affordance was built from grazing land with areas obscured by human and environmental factors removed (but not the soil factor).  The results can be plotted onto the same map, as the areas of affordance are mutually exclusive (green = earthwork affordance present, orange = cropmark affordance present):

AP affordance
Affordance for aerial photography: green is good for earthworks, orange for crop marks.

Although there are some issues with this model, in particular the fact that cropmarks can appear on grazing land in dry years, the results look fairly robust.

In combination:

As a final step in the process, I then combined these three affordance patterns based upon the relative percentages of records in our database recording each discovery method as an evidence type.  This results in a composite affordance map for sites and monuments, albeit only based on the types of evidence that it is possible for me to map affordances for:

combined affordance
Combined archaeological affordance for sites and momuments

Although the model is clearly imperfect, the results do look intuitively correct, with the more archaeological dense areas of the country generally showing up in yellow and with clusters of high affordance around historic towns / cities.  The lower affordance levels in the north west, around the Wash and across the Weald are genuinely (relatively) low areas of site / momument density in our database.  Some areas of lower affordance can have higher densities of sites / monuments, which is particularly true of Bronze Age activity in upland areas (especially Dartmoor and the Peak District), but I do not think that this represents a problem.  There will inevitably exist areas with low affordance but high densities of sites (and vice versa) and, in some ways, these are perhaps the areas of most interest (as they are the ones where we can say that there are definitely past high activity levels coming through rather than simply being due to intensity of investigation)?

Chris Green

CAA UK 2014

Members of the EngLaID team helped to organise the UK Chapter Meeting of Computer Applications and Quantitative Methods in Archaeology (CAA) this year.  The conference took place in the Pitt Rivers Museum, Oxford, on 21-22 March 2014.  The full conference programme can be found here.

1_GaryLock
Gary Lock welcomes attendees

Following a welcome by Emeritus Professor Gary Lock of Oxford University, who is the current Chairman of CAA International, speakers were heard hailing from a good selection of UK universities and other institutions, including English Heritage and the British Museum.

2_Vicky
EngLaID team member, Vicky Donnelly, presents her research

Amongst this varied and excellent selection of talks, EngLaID DPhil student, Vicky Donnelly, spoke about her research into the role of grey literature in archaeology and what it can enlighten us on.

3_crowd

Feedback on the conference was mostly very positive, with some minor complaints about lack of internet access for non-academic attendees.

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Mark Gillings presents the keynote lecture

Particularly inspiring was our keynote speaker, Dr Mark Gillings.  Mark is Reader in Archaeology at the University of Leicester and a well known figure in the field of archaeological computing.  He gave an excellent lecture on what he terms “Geosophical Information Systems”, which is (I believe) an attempt to reframe archaeological GIS as a more exploratory technique.  Particular resonant with me were his ideas about “shallow but juicy” GIS experiments.

5_crowd

On the Friday evening, a beer reception was held in the Pitt Rivers Museum, which seemed to be thoroughly enjoyed by all who attended.

8_reception 7_reception 6_reception

Amazingly, despite the presence of a large number of archaeologists for two hours, only two-thirds of the beer provided was drunk!  But we made a very good effort.

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Conference beer ration!

The conference twitter feed can be found here: #caauk14 or storify

Chris Green

PAS ‘affordances’

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.

biases_graph
Comparison of different PAS affordances, inc. mean of three coloured lines.

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):

PAS_affordance
Main distribution of PAS finds of our period (Bronze Age to early medieval) over PAS affordances surface.

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.

Chris Green

References:

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.

Field system orientation (II)

Further to my previous post, I have now made a test of my field system orientation analysis method in another part of England.  This time, I decided to take a chunk of the extensive late prehistoric / Roman field systems of South Yorkshire / Nottinghamshire.  The section is question was south of Doncaster on the Yorks / Lincs border, plotted by Alison Deegan as part of the Magnesian Limestone ALSF NMP.

SDoncaster_layout
Layout of field system south of Doncaster.

Here is the resulting bearing / distance graph, as previously constructed for the SPTA:

SDoncaster_graph
Radial graph for south of Doncaster.

Hopefully, it should be apparent that this field system is much less strongly aligned on a pair of perpendicular bearings.  I think that there is some north-south / east-west bias, but the pattern is much less strong than that seen in the SPTA results.

Chris Green

Field system orientation

Firstly, EngLaID is now on Twitter!  Our handle is @englaid_oxford if you want to follow us.

Now onto business.  I’ve been recently starting to think about how we might analyse field systems of our period in GIS, initially in order to test if patterns seen by previous research projects look true and then to try extending methodologies developed to fresh areas.  In order to undertake this, I am using NMP data, as that is the best source we have for widespread transcriptions of field system layouts.

We decided to start with the Salisbury Plain Training Area (SPTA) as that was the subject of an excellent and very detailed study by a team at English Heritage (and RCHME?) (McOmish et al. 2002).  In six specific areas, they discovered areas of co-axial field system aligned (in five of the six cases) in an approximately SW – NE direction (the exception, Maddington, was aligned on 62˚ east of north) (McOmish et al. 2002: Fig 3.4; 54).

In order to test these conclusions and, if validated, to see if I could come up with a methodology for analysing further field systems outside of the SPTA, I redrew these six areas of field system in ArcMap and then calculated the length and bearing (from 0˚ to 180˚ as there is no direction of travel implied) of each section of line.  Using a mixture of Python and R, I then produced a series of radial graphs, showing the density of line bearing / length for each field system against the supposed dominant axis.

Below, you will see a plan of each field system with the redrawn features in red (green / purple / black features are the original NMP layers, apologies to the colour blind!).  Then each will be followed by a radial graph of the line lengths / bearings.  Each black line is a single section of field boundary.  The blue line shows the apparent orientation defined by McOmish et al.  Then the red shading shows the density of of the black lines in a series of 15˚ / 100 metre bands: the more saturated the red, the more lines present.  If the orientations previously defined were correct, then we would expect to see the red sections of the graph clustering around the blue line and at 90˚ perpendicular to the blue line.

Orcheston_layout
Layout of field system at Orcheston.
Orcheston_graph
Radial graph for Orcheston.
Netheravon_layout
Layout of field system at Netheravon.
Netheravon_graph
Radial graph for Netheravon.
MilstonDown_layout
Layout of field system at Milston Down.
MilstonDown_graph
Radial graph for Milston Down.
Maddington_layout
Layout of field system at Maddington.
Maddington_graph
Radial graph for Maddington.
Longstreet_layout
Layout of field system at Longstreet.
Longstreet_graph
Radial graph for Longstreet.
Figheldean_layout
Layout of field system at Figheldean.
Figheldean_graph
Radial graph for Figheldean.

So, does this tell us anything?  I feel that there is strong support for the previously identified alignment at Orcheston, Milston Down and Figheldean and reasonable support at all of the other three in addition.  Netheravon has a weaker appearance, but this is not surprising due to the section in the south east of the area that does appear to be on a different alignment.  Interestingly, Maddington shows some bias towards its previously defined alignment, but less bias towards the 90˚ perpendicular alignment.

After histogramming the Riley’s Terrain Ruggedness Index for these areas, I could see that all of them are relatively flat, so it would have been easier for people in the past to ignore local topography when laying out their fields.  I now want to apply this methodology to other areas (as I think it works!) and see if any different results come out, especially in areas with more rugged topography.  I will do this in the New Year.

Happy Xmas everyone!

Chris Green

References:

McOmish, D.; Field, D.; Brown, G.  2002.  The field archaeology of the Salisbury Plain Training Area.  Swindon: English Heritage.