This is just a (very) short note to say that Chris Green and Letty ten Harkel will be at this year’s Landscape Archaeology Conference in Berlin from the 6th to the 9th June. So if you’re coming along to Germany, look out for us there!
Month: May 2012
A flood of data!
Many thanks to HEROs across England for their recent efforts to collate the Englaid data – we have now received data from almost half of the English HERs (see below)
. Particular thanks go to Graham Lee (North York Moors), Louisa Matthews (North Yorkshire), Rebecca Casa-Hatton and Sarah Botfield (Peterborough City), Rob Edwards (Cheshire), Jennifer Morrison (Tyne and Wear), (Exmoor), Stephen Coleman and Sam Mellonie (Luton & Central Bedfordshire), Vanessa Clarke (Bedford Borough), Dr Mike Hodder (Birmingham), Graham Tait (Devon and Dartmoor), Alison Bishop (Sandwell), Jason Dodds (West Yorkshire), Ingrid Peckham (Southampton), Alex Godden (Hampshire), Ian Scrivener-Lindley (Winchester), Jo Mackintosh (Cumbria), all of whom have sent through data in the last couple of weeks.
Following advice from several HER officers, the commissioning by the EngLaId team of an ExeGesIS query has, as hoped, greatly facilitated the process of extracting the EngLaId data for HER officers using HBSMR software (at least 75% of English HERs). Regarding this, we are greatly indebted to Keith Westcott (ExeGesIS) for facilitating both the creation and application of this query. Equally, we would like to say that we appreciate doubly the efforts of non-HBSMR-using HER officers who have produced the EngLaId data without the aid of a specifically written query.
Finally, we are extremely grateful to Dave Yates who has provided us with a database of Later Bronze Age sites in southern England (as of 2003) drawn from the results of developer-funded archaeology. These data provided the basis for his 2007 volume Land, Power and Prestige.
The EngLaId IT specialists are now busy uploading all these data into the project database. Meanwhile the EngLaId researchers are really looking forward to analysing them in detail!
Landscape morphology
(Apologies to any colour blind readers for the maps in this post, but these are variables that are quite hard to illustrate without using wide colour schemes.)
We have begun to think about some of the different variables that we will want to compare our distributions against once we reach the stage of analysing data for our national survey. One of these variables will probably be the morphology of the landscape itself. There is a fear in archaeology these days of being accused of “environmental determinism”, but this fear sometimes means that we ignore environmental variables that do have an impact on past human choices: Chris Gosden, our boss, suggested today that this was denying landscape its own agency. As such, we do believe that this is a legitimate set of variables to take into consideration when studying distributions of archaeological sites.
We can plot and derive various morphological variables when we have an elevation model of England to hand. Fortunately, again the OS OpenData can provide here: it includes a Digital Elevation Model (DEM) of the British Isles at a pixel resolution of 50 x 50 metres (interpolated, I believe, from contour mapping). This is more than sufficiently detailed for nationwide or regional studies (a higher resolution DEM would be preferable for more focused scales of study).
The DEM provides elevation data, which is the first characteristic to be studied. From the DEM, we can also derive two further morphological variables using standard tools within ArcGIS: aspect and slope. Aspect shows the predominant compass direction in which a cell is pointing. Slope shows the degree (or percent) of slope of each cell, as you might very well guess.
These three variables are all at a 50m pixel resolution, but for our national survey we will be studying distributions at a 2000m pixel resolution. Therefore, we need to consider: (a) whether there is any validity to studying these variables at this coarse resolution; and (b) how to generalise the data from 50m cells to 2000m cells.
Elevation is fairly non-controversial as elevation varies quite predictably across the landscape in most cases. Therefore, we can simply use the mean average elevation as an expression of the approximate elevation of each cell. Slope is more problematic, as slope can vary a great deal within a 2 by 2 km area. However, it does serve well as a type of proxy for the general “bumpiness” of a cell. It is important to consider this in addition to elevation, as it helps distinguish between more flat (i.e. plateau) and more “bumpy” (i.e. mountain) uplands, more on which below. Aspect is much more difficult to generalise, however: I will present the results below, but I am unconvinced that they have any great validity.
So, to begin with elevation, we can simply classify this into bands, convert the raster image into a point vector dataset, run the Identify tool in ArcGIS (which seems to be becoming my favourite) against our distribution of polygon grid squares (which we are using to plot our archaeological distributions), and then join the results to said grid square layer. In this way, it becomes straightforward to statistically test distributions against elevation band: by comparing the statistical profile of a distribution of a specific sub-set of site types (by period or generally) against the statistical profile of all sites, we can test whether any patterns seen are meaningful. Here is an example of a set of elevation bands to prove that 2km cells still show useful pattern:
Moving on to slope, we can work in exactly the same way, producing again a mean value for slope in each cell. As stated above, this result is less meaningful, but I still feel it has some useful validity in picking out the edges of major uplands and in differentiating between flat and “bumpy” areas of the landscape (the numbers themselves are not too important, more the variation between areas):
As stated, aspect is much more problematic for several reasons. Firstly, ArcGIS will derive an aspect for all but the most flat of cells, with the result that areas that would appear flat to the naked eye will acquire an interpretatively meaningless aspect value. However, we can construct a mask from the slope layer to reclassify the aspect of cells with less than a certain degree of slope (in this case, 3 degrees) as being flat. Secondly, because flat cells are classified as having a slope of -1, generalising using the mean value becomes impossible. We cannot reclassify these cells as NoData, as then they will be ignored. Therefore, we have to reclassify the aspect layer to a category of five (or nine if you including the intermediate directions) cardinal directions expressed numerically: flat (0), north (1), east (2), south (3), west (4). We can then generalise to the median direction to produce our 2km aspect map, which we then link to our 2km vector cells as before and convert to natural language terms (flat, north, east, south, west). Here is the result:
As should be apparent, this result is a rather messy and problematic one. The dominance of northerly and easterly aspects seems incorrect, and the overall pattern seems too incoherent to be convincing. As such, I don’t believe that there is any great feasibility of using aspect for this scale of survey. However, it may prove more fruitful when approached at the case study level during the latter part of this project.
As a final, more complex, example, I tried combining slope and elevation into a composite model. The idea was that in combination these two variables could help differentiate between relatively flat and relatively “bumpy” upland and lowland areas. The resulting map is quite hard to read, but I will explain it below:
Ignore the white cells around the edges of England on this map, that was my error in forgetting at which stage in the process I should clip the results to England. On this map, slope is represented by colour (purple/blue = flat; green = gentle; yellow = steep; red = severe) and elevation by saturation (i.e. the brighter the colour, the greater the elevation). This shows how you can use the HSV colour space to display two variables at once, albeit with slightly difficult results to read. However, I do think it is possible to derive certain conclusions from visual examination of this map: in particular, I like the way in which you can see a strong difference between landscapes that are truly mountainous (such as the Lake District and parts of the Pennines) and landscapes that are more plateau-like in character (such as Bodmin Moor and Dartmoor and other parts of the Pennines). Of course, whereas visual examination of this map is quite difficult, it would be simple to derive statistical measures from it.
In conclusion, then, I believe that there is strong potential for comparing archaeological distributions on the scale of England against certain aspects of landscape morphology. Certainly elevation, probably slope (especially in combination with elevation), but probably not aspect. I may continue to try to produce a more useful result for aspect, however, but I don’t think the prospects are particularly strong.
Chris Green
Devon field trip and Project Advisory Board meeting
It has been a busy week for the EngLaId project team. Over the Bank Holiday weekend, Letty and Miranda went to Dartmoor to explore this case study area and do some drawing. They were joined by the poet Alice Oswald (author of Dart) and her husband Peter Oswald, whose local knowledge of the area provided an immensely valuable contribution to the trip. Even the weather decided to hold out, which was wonderful, if unexpected!
Sites visited included Lydford, an Anglo-Saxon burh on the western edge of Dartmoor overlooking the moor. Lydford, now a small village, was a royal burh with spectacular natural defences on three sides, whilst the bank that defended the only easy access to the settlement is still visible as earthworks in the landscape today as well. During the medieval period, the parish of Lydford also included the entire area of Dartmoor’s upper moor.
Miranda spent an afternoon drawing from the earthworks; keep an eye out for the visual blog to see the results of that!
The following day, we explored the moor near Scorriton, walking part of the Two Moors Way and the Abbot’s way, which originally would have connected the abbey of Buckfast with those of Tavistock and Buckland on the other side.
Miranda found a wonderful spot inside a Bronze Age settlement enclosure from where she spent a few hours drawing the landscape surrounding the Avon valle, whilst Letty followed the Abbot’s Way a bit further, retracing the steps of countless travellers who must have walked here since at least the early medieval period.
After this truly wonderful weekend, it was time to go back to reality and prepare for the annual project Advisory Board meeting, where the team presented progress to a committee consisting of Prof. Sir Barry Cunliffe, Prof. Richard Bradley, Prof. Mark Pollard, Prof. Helena Hamerow, Dr Jeremy Taylor and Dr Roger Thomas. After an introduction by Chris Gosden, Laura Morley presented progress on data collection, followed by a joint presentation by Anwen Cooper and Chris Green on the potential and problems of the data from Somerset, one of the first regions for which we had collated a complete dataset (many thanks must go once more to the HERO from Somerset!).
The Advisory Board provided much helpful feedback, and after a short tea break the presentations continued, first with Letty ten Harkel presenting preliminary work on the Devon case study area, followed by Miranda Creswell who presented her work on recording the team’s working methods, her landscape drawings and other art and, finally, the public engagement side of things in relation to a pilot project she is developing … again, watch the visual blog for more details!
The day ended with a very useful and constructive discussion. The EngLaId team would therefore like to thank the Advisory Board for their time and effort, and looks forward to the next meeting in a year’s time!
ESF Workshop on Landscape
Last week, I attended a European Science Foundation (ESF) Exploratory Workshop on Conceptualising European Landscapes Across Languages, Cultures, and Disciplines. The workshop took place in the Spanish mountains west of Madrid in the small town of Las Navas del Marqués. The venue was the fascinating Castillo-Palacio de Magalia, which (if I understood the tour correctly) was built in the late 16th century and then extensively remodelled by Franco as a retreat or headquarters for the women’s arm of the Falange. The building itself was beautiful and we were also very well (over)fed!
The format of the workshop consisted of two days of papers (including a long siesta each afternoon), followed by two broader discussion sessions on the final morning. Papers were presented on a variety of subjects, with particular themes developing around the meaning of the term “landscape” and the linguistics of landscape terminology. There were a number of papers from members of the LACOLA project at Lund University, which is another landscape-themed ERC project that began shortly before our own, albeit focused on linguistics rather than archaeology.
One idea that was introduced (by Zsolt Molnar) that I think might have particular application to archaeological models / narratives was that herders mentally classify their landscape based upon a minimum unit size equivalent to the spatial extent of a herd / flock. It seems to me that this might be a useful way to approach the definition of the minimum spatial resolution for study of past pastoral practices.
My own paper discussed my past work (available as a BAR: S2234) on mapping the uncertainty of archaeological dates, and how we might bring an element of this into the synthesis methodology we are currently intending to use for this project. It is a weakness of our current model that it can only express the temporality of data based upon period classifications, but there is potential for building probabilistic calculations of date into this. In essence, this would consist of dividing our study period up into time slices and then calculating the percentage probability of each type of site falling within each grid square for each time slice.
The question then is how we assess probabilities where there is more than one of each site type within a particular square: as we are assuming that most multiplicity is in fact duplication, I would think that the most appropriate answer would be to take the highest probability as being the most valid, rather than adding or multiplying probabilities (which would speak of slightly different things). However, my ideas on this front are still very vague… I hope to develop them further soon.
Overall, the workshop was a worthwhile (and enjoyable) experience and we would hope as a team to maintain links with the people involved, as we progress through our own study of the landscape in the past.
Chris Green
English Landscape and Identities 