Franconi, T. & C. Green. 2019. Broad and Coarse: Modelling Demography, Subsistence and Transportation in Roman England. In: Verhagen P., Joyce J., Groenhuijzen M. (eds) Finding the Limits of the Limes. Computational Social Sciences. Cham: Springer, 61-75. DOI: 10.1007/978-3-030-04576-0_4
This post follows on in part from a post I wrote a couple of years ago on regionality. It will also begin with an apology: the maps presented here will be very difficult for colour blind readers to understand, for which I am sorry. Unfortunately, the technique involved is somewhat limited in terms of control of colour (as it requires three colour channels), so it is not possible (or at least very difficult) to improve the maps to make them more legible for colour blind readers. As such, I would not propose publishing these particular visualisations in any formal setting, but hopefully I can get away with it in a blog post!
Before we get to the maps themselves, I shall describe briefly the mapping technique involved, which is partly inspired by the work of a former colleague of mine at the University of Leicester, Martin Sterry (departmental webpage; academia.edu). Essentially, this method can be used to describe the relationship between three different spatial variables that can be mapped as density surfaces. First, we create density surfaces (KDE here) for each variable and then we combine them into an RGB image using the Composite Bands tool in ArcGIS, with the first layer forming the red channel, the second layer forming the green channel, and the third layer forming the blue channel. However, RGB images (so-called “additive colours”, which work from black by adding light in the red, green, and blue channels), can be rather dark / muddy, so I then converted the images (using “Invert” in Photoshop) to CMY images instead (so-called “subtractive colours” where one works from white by subtracting light in the cyan, magenta, and yellow channels: this is how colour printers work). To do so cleanly, one must set up one’s map document so that anything one wishes to be white in the final image is black in the map document and vice versa. The same applies to greys, which must be set to their inverse (e.g. a 30R 30G 30B grey as seen below for Wales / Scotland / Man should be set to 225R 225G 225B, being 255-30 in each case). This may sound somewhat complicated but the end result is as follows:
Cyan (turquoise) tones represent high values in Channel 1, e.g. “complex farmsteads” in the first example below.
Magenta tones represent high values in Channel 2, e.g. “enclosed farmsteads” in the first example below.
Yellow tones represent high values in Channel 3, e.g. “unenclosed farmsteads” in the first example below.
Blue tones represent high values in Channels 1 and 2.
Red tones represent high values in Channels 2 and 3.
Green tones represent high values in Channels 1 and 3.
Dark grey / black tones represent high values in all three Channels.
White or pale tones represent low values in all three Channels.
Here is a close up of the colour category zones for the first two examples below:
I began by examining the three main categories of Roman farmstead defined by the Roman Rural Settlement Project (RRSP) at Reading, using their excellent data that is available online (Allen et al. 2015). As they defined only three specific categories, this is an ideal dataset to map in this way. For a first attempt, I made three KDE layers using a 10km kernel (or search window) to structure the size of the clusters in the resulting output, then combined them as described above. When plotted against the regions defined based upon variation in their data by the RRSP team (Smith et al. 2016: Chapter 1), we can see that there is a degree of agreement between the regions and the clustering of particular colours:
However, there is also clearly considerably more complexity to the data than a simple regional classification might suggest (as the RRSP team would certainly acknowledge, so this is not intended as a criticism in any way). If we construct a new model using a wider kernel (in this case 50km), we can get a really nice sense of regional variation in the data without the need to draw lines on a map:
There is some interesting structure in this model. For example, one can see a focus on enclosed farmsteads in the north and west, so-called complex farmsteads in parts of the southern and eastern midlands (largely alongside enclosed farmsteads), with quite a different focus on enclosed and unenclosed farmsteads in the south east. The strong peak in enclosed farmsteads in south Yorkshire / the north midlands is also quite striking. Although it relies too much on good colour vision in a reader, I think this model and technique works quite well here, so I decided to apply it to another dataset: our own.
Before we get to the next stage, here is a close-up of the colour category zones for the next two maps (with RO = Roman; PR = Prehistoric; EM = early medieval):
Based on another technique which we published recently (Green et al. 2017), the following two maps are created from a measure of the “complexity” of our datasets: specifically the number of different types of site / monument (based upon our thesaurus of types; see Portal to the Past) per 1x1km square. This measure was calculated for each square for each time period in our database and then density surfaces created for each time period (using a 5km kernel in this instance). A shortcoming of the mapping technique comes into play here: it can only map three categories at once. As such, we had to combine the Bronze Age and Iron Age models into a composite model for later prehistory. The three time period based complexity models were then combined into a single image as previously:
There are various nice patterns in this dataset, including the clear strength of prehistory and the early medieval in the south western peninsula, the intense focus on major river valleys (partly due to the large gravel quarry excavations in those areas), and the appearance of Roman roads highlighted in magenta. The Roman period also looks quite dominant generally, with lots of pinks, blues, and reds visible on the map. There is also a very clear difference in intensity between eastern / southern England and northern / western England.
It is possible to lessen the effects of regional and period based variation, by constructing a series of larger kernel density surfaces and using these to “correct” for regional variation in the period based models. This produces a new model which reflects complexity on a more local scale. Essentially, the first model can be thought of as a model of “globally” scaled (by which I mean the whole of the dataset, not the whole of the planet) complexity and the new model can be thought of as a model of locally scaled complexity:
This model also shows some interesting patterns. It is much less dominated by single periods in particular regions, with Roman dominance mostly along the Roman roads and Hadrian’s Wall. There are also some nice dark areas, which show high levels of local complexity across all three time periods. These cluster mostly along rivers again or around the large Roman towns, along with a similar cluster in southern Yorkshire / the north Midlands to that seen in the RRSP data.
As with all models of English archaeology, the images presented here represent a very complex data history, being influenced by both where more (and more visible archaeologically) activity took place in the past and where more modern archaeological activity takes place in the present (largely driven by development). They also, as previously noted, come with considerable caveats in regards to legibility, due to the relatively large minority of people with restricted colour vision (c.8-10% of men, and maybe 1% of women). The technique is also restricted by its inability to map more than three variables, but more than three variables would probably overcomplicate matters even if it were possible. However, I hope that this post gives a sense of the variation and complexity in the English archaeological record, locally, regionally, and nationally.
EngLaId is now winding down, having officially ended at Christmas, so this will probably be the last substantive post on technique or data for a while. We will however announce here when any new publications come out, including our main books.
Allen, M., T. Brindle, A. Smith, J.D. Richards, T. Evans, N. Holbrook, M. Fulford, N. Blick. 2015. The Rural Settlement of Roman Britain: an online resource. York: Archaeology Data Service. https://doi.org/10.5284/1030449
Smith, A., M. Allen, T. Brindle & M. Fulford. 2016. New Visions of the Countryside of Roman Britain. Volume 1: the Rural Settlement of Britain. Britannia Monograph Series No. 29. London: Society for the Promotion of Roman Studies.
Ever since I was an undergraduate (and attempted to write a “mental geography” of Roman Britain for my dissertation), I have been interested in Claudius Ptolemy’s Geography. Ptolemy was an Alexandrian Greek and his Geography dates to the mid second century AD: it contains coordinates from which it is possible to make maps of the entire known world at that time, including data representing the earliest surviving reasonably accurate survey of the British Isles. For the purposes of the EngLaId Atlas, that I am currently working on, I decided to see if I could plot Ptolemy’s Britain (or Albion as he called it) over the modern OS map.
To do so, I copied out the coordinates for Ptolemy’s places (representing points along coastlines, islands, and major settlements) from Rivet & Smith 1979. I suspect that there may be one or two typos in their lists (as a couple of the points in the final maps are not quite in the same place as they are on Rivet & Smith’s map), but I am not too worried about that for now. The task was then to convert Ptolemy’s coordinates so that they could be plotted onto the OS National Grid.
The first job was to correct for Ptolemy’s underestimate of the circumference of the planet (it was this underestimate that caused Columbus to be so confident about being able to reach the Indies by sailing west, thus accidentally discovering the Americas): to do so, all of the coordinates were first multiplied by 0.798. I then needed to recentre the coordinates so that they related to modern latitude / longitude: I used London / Londinium as a fixed point in both Ptolemy and the modern world, on the assumption that the provincial capital of Britannia ought to be relatively precisely located in Ptolemy’s data. This involved adding 8.41 degrees to each latitude measure and subtracting 16.06 degrees from each longitude measure.
I then created a shapefile in ArcGIS from the coordinate list using the WGS84 projection settings and then reprojected the map into OSGB 1936, ArcGIS’s representation of the OS National Grid. The points were then filtered out into islands, settlements, and coastline vertices. I had given the coastline points an “order” field (based upon the order of coordinates in Ptolemy) and used the Points to Line tool in ArcGIS to convert them to a line. I then converted the line to a polygon using Feature to Polygon. Finally, a few extra vertices were added to the coastline polygon using the editing tools in order to ensure that the settlement points were all on dry land. Here is the result:
Several things jump out. The most noticeable (and long commented on) is Ptolemy’s rotation of Scotland. Why he did this has been the subject of much debate, possibly being due to him believing that a N-S Scotland would extend too far north or possibly being due to a lack of reliable data on travel times through those non-Imperial lands. The latter is rather key to understanding the Geography: whereas latitude was fairly straightforward to calculate in the past, without chronometers longitude was much more difficult and relied largely upon calculations made using travel time itineraries. We can see the results of this in the way that most of the settlements in England / Wales are reasonably precise in their latitude (N-S) but much more imprecise in their longitude (E-W): York forms a good example. Overall, considering the time when it was constructed, Ptolemy’s Geography contains an impressive representation of Britain (south of Scotland).
I then experimented with a couple of transformations to see if I could improve the plotting onto the National Grid. First, I tried rotating the data so that the north of England more closely aligned with the modern map (actually an affine transformation using London, York and Chester as fixed points, so the geometry is slightly deformed, especially for Scotland):
The result is not really all that great, as the south of England then becomes much less closely aligned with the modern map. I also tried a rubbersheet transformation, using London as a fixed point and moving Ptolemy’s York onto modern York:
This turns the map into a really quite close approximation of the modern English / Welsh coastline, with the exceptions of the immense length of the south west and the rather stunted East Anglia. However, as it disturbs the geometrical relationship between Ptolemy’s coordinates, I decided in the end that my first model was probably the best: after all, I could keep adding points to the transformation until everything mapped perfectly onto the modern geography, but what would be the point of that? I would just be recreating the OS map.
This was just a short experiment for the purposes of debate and making a nice map. It seems likely that I may have done something spatially naive in plotting the data using the WGS84 settings, but the end results are rather pleasing in any event.
Rivet, A.L.F. & C. Smith. 1979 The Place-names of Roman Britain. London: Batsford.
The maps contain Ordnance Survey data (OpenData). (C) Crown Copyright and Database Right 2016.
This is another post about field systems, following up on my previous work on the subject (I)(II)(III). As stated in my last post on the subject, I now have a dataset of 40 field systems that I have digitised (based upon NMP data) and subjected to various analyses. Some initial results will be discussed below.
They range in enclosed area from around 2ha to over 1,100ha and cover time periods from the Bronze Age to the Roman period (with a single small section of early medieval reuse in one case). When plotted by time period, the earlier field systems are largely in the south of the country, whereas the Iron Age / Roman ones feature a more comprehensive national spread (the blue “PR” dots represent unspecified prehistoric or possibly Roman field systems):
When classified into rough categories of “coaxial” (meaning essentially rectilinear and perpendicular in character) and “aggregate” (meaning more amorphous), both types of field system occur across the country:
All of these field systems have been subjected to analysis of their morphology, topology (to a limited extent) and landscape character. Probably unsurprisingly, but usefully, field systems with less orientation “peaks” tend towards perpendicularity (y-axis is the difference in degrees between the centres of the first and second peaks on the orientation graphs, the x-axis is the number of peaks on the orientation graphs [5 peaks = 5+ peaks]):
Interestingly, the degree of “coaxialty” of each field system appears to have very little to do with how “open” the landscapes are, which suggests that the layout of field systems (particularly in the Bronze Age) did tend towards “terrain blindness” (x-axis is how “coaxial” each field system was from 0 [not at all] to 1 [very]; y-axis is how visually open the enclosed area of each field system is from 0 [very restricted intervisibility] to 1 [high degree of intervisibility]):
One pattern that has emerged is a degree of bias in orientation towards a particular pair of approximate compass bearings around 100-120˚ and 10-30˚ (this graph shows the direction [and strength] of the two strongest orientation peaks from every field system):
As the graph makes clear, this is not the case across the board, but it is common enough to suggest that there is something going on here. The orientation data was also plotted against the orientation of the aspect of the local terrain, to see if the latter could affect the former (red lines show aspect, black lines field system banks/ditches):
As should hopefully be apparent, the aspect of the ground surface can influence the orientation of field systems (especially in the case of FS_id 25, which runs along the side of a fairly steep hill), but not in many cases.
Nationally, these data have been collated by 100x100km OS grid square, alongside orientation data for ridge and furrow, and for unstudied field systems via automated extraction of boundaries. Both of the latter datasets were based purely on the more modern CAD-based NMP projects and processed using automated methods, so the results are based upon more data than my set of field systems, but data that has been less rigorously filtered (numbers record the number of line segments analysed in each square):
The ridge and furrow data shows a particularly interesting pattern here, with a very common bias towards perpendicular orientations just west of north and just north of east for areas north of the Humber. Hall has noticed this pattern before in Yorkshire, suggesting that it probably is the result of a planned reorganisation of the landscape on a large scale at some time before the C13th (2014:53), but my analysis suggests that this may have occurred over a very substantial area of northern England.
So, what we have here is people in prehistory and the Roman period constructing field systems that were sometimes very regular (“coaxial”) in character and sometimes less so, with the ground surface sometimes having an effect on the orientation and regularity of the field systems, but with field systems also often being laid out in a way that ignored the affordances provided by the ground surface. Often, these field systems were laid out on an orientation that pointed approximately towards a compass bearing of 100-120˚ (and at 180˚ to that, as these lines have no direction) and, to a lesser extent, towards approximately perpendicular alignments. When so-called “open field” systems were created from the later early medieval period, these also show an orientation bias (a different one), particularly north of the Humber.
I suppose that the natural inclination of archaeologists working in their respective time periods would be to find a more ritual explanation for the earlier phenomenon and a more pragmatic explanation for the later phenomenon. This in itself is problematic and one of the reasons why working across traditional time period boundaries (as we are) has the potential to produce new interpretations and understandings. For myself, I am not sure what I think (yet)…
Hall, D. 2014. The Open Fields of England. Oxford: OUP.
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.
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).
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.).
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.
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.
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.
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.
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.
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.
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.
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.
Blinkhorn, P. 2012. The Ipswich Ware Project: Ceramics, Trade and Society in Middle Saxon England. Medieval Pottery Research Group Occasional papers.
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:
Following on from a previous post (see which and also Green 2011 for more details on the methods discussed here), I have been experimenting more with the application of fuzzy probability modelling of our data. We decided to expand out the previous experiments, which had only been done using PAS data, to take in radiometric dates. Although our search was rather cursory, just taking in the CBA Index maintained by the ADS (and periodically updated by English Heritage) and a search of published dates from within the OxCal database (kindly conducted for us by Christopher Bronk Ramsey at RLAHA), we were able to create a database of over 5,000 radiocarbon (14C) dates that fell within (or partially overlapped) our time period of interest (for this exercise, being 1500BC to AD1050).
I rewrote my fuzzy probability calculation scripts to enable them to use the full detail of the radiocarbon probabilities output by OxCal and then ran them on a series of timeslices across this new dataset. Initially, I used the sub-periods defined in the previous experiment, but it became quickly apparent that the sub-periods chosen for the Late Iron Age and Roman period were too narrow to produce high enough probabilities of dates falling within them to be of interest. So I defined a different set of sub-periods, which resulted in a higher average probability for dates through the LIA-Roman period:
1500 to 1151BC
1150 to 801BC
800 to 401BC
400 to 151BC
150BC to AD49
AD50 to 199
AD200 to 410
AD411 to 649
AD650 to 849
AD850 to 1050
The results were collated in ArcGIS and could then be mapped for each time-slice as follows:
However, there is a problem with reading these maps due to the relatively clustered nature of the distribution which results in a lot of overlapping points. This results in some low probability dates obscuring higher probability dates within the same local area. To get around this, I collated the results using hexagonal bins, with the maximum probability of any date within a given bin being used to define the probability for that bin (maximum rather than summed values were used as 14C dates are not really discrete objects in the same way as finds and so multiple dates do not necessarily represent greater density of activity in the past):
I then reran the probability calculations for PAS and other dated finds in our database using the new sub-periods and summed the results by hexagonal bin (summing was used rather than the maximum here as finds very much are discrete objects and, as such, more finds does imply more past activity, with certain caveats [modern archaeological / metal detecting practice being the most obvious one]):
I then combined the two sets of results, using the maximum value across both datasets. As such, if the weighted finds probability within a cell was greater than 1.0, then it was preferred, but if less than 1.0 and less than the 14C probability within the cell, then the 14C probability was preferred. Although the finds dominate the results, the 14C does fill in some gaps and increase probabilities in some areas, especially in prehistory:
The results for each time-slice can be viewed in the following animation (click to enlarge):
What can we read into this? Well, firstly, it should be noted that this is just an experimental model and shouldn’t read too much into it. There is a possible element of duplication in some of the finds data, as some PAS records are present in both our PAS dataset and our HER dataset (dependent upon local HER practice). Secondly, the 14C dates only add something quite subtle to the finds dates, as we have far more finds dates than 14C dates in our possession, but the subtle addition is, I feel, an important one.
However, subject to these caveats and the further element of uncertainty introduced by the affordance factors at play in the background (see previous posts: PAS; monuments), there are certain tentative archaeological conclusions that we could draw. The picture I see in the animation is one of relatively widespread activity in earlier prehistory, which intensified in the south and east in the Late Iron Age and especially through the Roman period, with late Roman and especially early medieval activity being particularly focused on the central / southern / eastern area of England (essentially Cyril Fox’s lowland Britain). Whether this remains the case as we build in more sources of evidence, remains to be seen.
Green, C.T. 2011. Winding Dali’s clock: the construction of a fuzzy temporal-GIS for archaeology. BAR International Series 2234. Oxford: Archaeopress.
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:
As clusters of spot finds.
As documentary records (or place names, etc.).
Via excavation / other intrusive field evaluation (e.g. watching briefs, test pits, etc.).
Via geophysical survey.
Via aerial photography as crop marks.
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):
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.
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):
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.
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:
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)?
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.
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.
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.
Feedback on the conference was mostly very positive, with some minor complaints about lack of internet access for non-academic attendees.
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.
On the Friday evening, a beer reception was held in the Pitt Rivers Museum, which seemed to be thoroughly enjoyed by all who attended.
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.
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.
This week, in an attempt to avoid any substantive work, I have been playing around with the Ordnance Survey’s Digital Terrain Models (DTM) that are available for free as part of their OpenData archive to anybody who wishes to use them. The spur for this was the launch in July of a new DTM onto the OpenData site.
Previously (and still today), the OS made available a dataset known as PANORAMA. This was created using contour data surveyed in the 1970s. In order to turn this into a rasterised DTM, some interpolation algorithm (I don’t know which) was used to estimate elevation values between contours to result in a continuous field (50m by 50m pixels) of elevation values for all of the UK. The heights in PANORAMA are recorded as integers, i.e. to the nearest whole metre.
In July, the OS released a new product, known as Terrain 50. This DTM was created using LiDAR data surveyed from the air and then averaged out to 50m by 50m grid cells. A lot of data processing goes into turning raw LiDAR data into a terrain model, but this all takes place behind the scenes, so it is difficult to know exactly what has been done. The heights in Terrain 50 are recorded as floating point numbers, so apparently convey more precision than PANORAMA. However, due to the relatively coarse nature of the grid used (50m by 50m pixels), this does carry a degree of spurious accuracy (as we are inevitably dealing with averages).
This map shows both products for comparison (click to enlarge):
Certain things stand out when you compare these images, but more obviously when you look at the hillshade (click to enlarge):
The main things to note are:
The contour origin and whole number data model of PANORAMA produces a stepped plateau appearance, being especially apparent in areas of gradual change in elevation.
PANORAMA produces a substantially smoother picture of change in elevation over space.
Terrain 50 appears much more accurate, but also “noisy”.
Human impacts on the landscape (e.g. quarrying) show up much more obviously in Terrain 50.
On the face of it, Terrain 50 looks a much more accurate representation of the terrain of the UK and, as such, would likely be most peoples’ first choice when choosing between these two DTMs.
As I have so far been working with the PANORAMA DTM, I wanted to test how different it was from Terrain 50 in order to see if I should go back and rerun some of my analyses with the newer product. The simplest way to do this is to compare the elevation values recorded in each product for the same piece of terrain, i.e. subtract one grid from the other in the Raster Calculator in ArcGIS and then calculate some basis statistics on the result.
However, this is complicated somewhat by the fact that the two grids are not aligned directly on top of each other: the origin of a pixel in one is in the middle of a pixel in the other, i.e. they are offset by 25m east / west and 25m north / south. To enable a direct comparison to be made, I reprocessed the PANORAMA DTM to split each cell into four and then aggregated sets of four cells (using the mean) on the same alignment as Terrain 50. This will have resulted in some smoothing of the resulting surface, I expect, but hopefully not to the extent of making the comparison invalid (as PANORAMA already possessed a relatively smooth surface).
The results can be seen on this map (click to enlarge):
White cells show little difference. Yellow cells are slightly higher elevation in Terrain 50 and red cells are significantly higher. Cyan cells are slightly higher elevation in PANORAMA and blue cells are significantly higher. Certain things stand out on this map:
Differences between the two DTMs are greatest in upland areas. This will at least partly be due to the need to draw contours legibly forcing cartographers to underplay the steepness of very steep slopes.
The sea tiles are quite interesting in the way they vary. This seems to be due to PANORAMA using a single value for sea cells across the whole dataset, whereas Terrain 50 seems to use a single value for sea cells on each 10km by 10km tile, but different values between tiles.
We can also see some differences being much greater on one side or other of the division between tiles aligning with 1000m divisions on the OS grid. This must be due to Terrain 50 data being processed on a tile by tile basis, more on which later.
Overall, however, the differences between the two DTMs are not great. If we remove the negative sign from the difference layer (by squaring, then square rooting the result) and clip out sea cells, we can plot a histogram of the difference in elevation (across all 92 million cells):
From this graph, we can see that although there are cells with differences of up to nearly 230m, the vast majority of cells are within 5m of elevation of their counterpart. The mean difference is 1.91m and the standard deviation 2.26m; 75% of all values are within 2.5m of their counterpart. As such, PANORAMA and Terrain 50 are actually very similar in elevations recorded.
We can also plot this difference layer on a map, with some interesting results:
Black cells on this map show no difference or minimal difference, shading up through grey to white for cells of relatively high difference in elevation between the two DTMs. Certain features stand out, some of which I have annotated onto this map:
The motorway is clearly a feature that appears in Terrain 50 but not PANORAMA. The contour lines are clearly an artifact of the origins of PANORAMA. The reservoir is presumably a similar issue to the sea level variation. The variation on the Mendips is presumably due to the “noisier” more precise nature of Terrain 50 contrasting against the smoothed appearance of PANORAMA.
The appearance of the grid lines worries me somewhat though. They were not apparent (to my eye) when looking at the raw data or hillshade layers for either dataset, so presumably they are the result of quite a subtle effect. My assumption (as mentioned above) is that these arise from the LiDAR data behind Terrain 50 being processed as a series of tiles rather than as a single dataset: this is of course inevitable as a continuous high resolution LiDAR dataset for all of the UK would be mind bogglingly immense. My fear is that any sensitive analyses of terrain using Terrain 50 might show up these grid edges in their results. However, this is even more true of the 1m contour “cliff edges” that appear in PANORAMA. At least grid lines will be obvious to the human eye if they do cause strange effects.
So, what does this all mean? Well, I would argue that the generally minimal difference between elevations recorded for the same place in the two datasets means that previous analyses (especially coarse analyses) undertaken using PANORAMA should not be considered invalidated by the (presumably) more accurate new Terrain 50 DTM. Also, the “noisy” nature of Terrain 50 and the presence therein of more features of human origin might mean that the smoother PANORAMA could still be the best choice of DTM for certain applications (especially in archaeology, where features like the M5 would not generally be a useful inclusion).