On 13 June, the EngLaId project hosted a one-day workshop to explore the relationship between landscape and scale at Keble College in Oxford, a perfect venue for such a lovely and sunny day!
A range of papers was presented on a number of different topics – see below for the programme – and we are currently exploring various options to publish proceedings. Most importantly, however, the symposium laid the foundations for a loose network of British scholars and professionals engaging with landscape-based issues.
It is hoped that these events will become annual affairs. For 2013, a number of themes were suggested, with the majority of attendees preferring a more explicitly theoretical angle for next year’s event. If you would like to get involved, contact the organisers (email@example.com, firstname.lastname@example.org or email@example.com) and we can keep you posted about our plans for next year!
Last but not least, a special thank you must go to the Royal Historical Society, who kindly granted us £200 to assist with reimbursing travel costs for early career researchers.
Programme ‘Landscape and Scale’
‘Issues of Scale and the English Landscape and Identities Project’ (Chris Gosden, University of Oxford).
‘The Fields of Britannia: continuity and discontinuity in the pays, regions and province of Roman Britain’ (Stephen Rippon, Christopher Smart and Fiona Fleming).
‘Recalibrating through ‘landscape’’ (Graham Fairclough, University of Newcastle).
‘Making the most of PAS data: macro and micro-level studies of Romano-British settlement’ (Tom Brindle, PAS Finds Liaison Officer, Staffordshire and West Midlands).
‘Identifying amateur collection bias at different scales of analysis, using the data collected by the Portable Antiquities Scheme’ (Katie Robbins, British Museum).
‘Local, regional and beyond: inter-tidal zone archaeology in the Greater London Area’ (Elliot Wragg and Nathalie Cohen, Thames Discovery Programme).
‘Bridging the gap? Scale and development-led archaeology in England today’ (Roger Thomas, EH).
‘Something fishy about scales? Tensions between macro and micro levels of analysis and interpretation in the study of later prehistoric and Romano-British field systems’ (Adrian Chadwick, affiliation?)
Over the last month, members of the Englaid team have been involved in a couple more regional HER meetings and have engaged with the broader research community at a workshop in Oxford on Landscape and Scale (more to come on this in a separate posting …).
Firstly, we would like to thank those who hosted to the south west and eastern regional HER meetings – in particular Chris Webster in Somerset and Vanessa Clarke in Bedford Borough – for having us along to these forums. We are also extremely grateful to all who attended these meetings for providing such thoughtful and constructive feedback on the project’s work so far as well as on its broader aims – your collective input is very much appreciated and as the following discussion hopefully shows we are considering your suggestions very carefully.
One outcome of our broader consultation with HER officers and the wider research community has been that we have begun to review the project’s original case studies (those set out in the original project design). The form and location of the project case studies has become a topic of considerable interest and we have now received a wealth of good suggestions as to how to reshape the case studies in order to satisfy our key aims of:
1) providing a sense of regional landscape variability across England
2) foregrounding data from under-exposed research areas
3) showcasing particularly rich combinations of data from the various sources consulted
4) linking into regional research agendas
The provisional results are illustrated below (the original case study areas are outlined in pale yellow).
The main outcome of this broader consultation is that many of those we have talked to felt that it was important for our case studies to traverse different landscape zones rather than focusing on areas defined either geographically (such as the Yorkshire Wolds) or by modern political boundaries (counties such as Suffolk or Norfolk). One particularly bold consequence of this is the new case study area which traverses the middle of England from east to west (a suggestion which came from the East Midlands regional HER meeting – thanks in particular to Ken Smith of the Peak District National Park Authority). We have also included a greater number of river valleys (for instance the Alne valley in Northumbria, the Lea Valley in Hertfordshire, and the Humber estuary), and a distinct island – the Isle of Wight – in part, to balance out a previous emphasis on upland areas. Two more interpretatively-led additions are a case study revisiting Hoskins’ Lincolnshire, rendered so evocatively in his seminal work The Making of the English Landscape, together with a strip of land traversing Hadrian’s Wall in order to explore the affects of this substantial piece of landscape architecture. It is also worth emphasising that the revised case studies are not fixed. Other than the case studies which we have already begun work on – the Dartmoor and Tamar Valley, Devon, and the Mendip Hills, Somerset – we would continue to welcome further ideas regarding the areas we have (re)defined. However from this point onwards, most revisions we make to the case study areas are likely to be exercises in fine-tuning rather than substantially reworking.
Finally, we have received another wave of datasets over the past month and would like to express a huge thanks to those involved in producing them. We are extremely grateful to Victoria Brown (Humber), Stuart Cakebread and Mel Bell (Greater London), Nick Crank (Milton Keynes), Sally Croft (Cambridgeshire) Ben Croxford (Kent), Lucie Dingwall & Melissa Seddon (Herefordshire), David Evans (S Gloucestershire), Jonathan Goodwin (Stoke), Teresa Hocking (Berkshire), Beccy Loader (Isle of Wight), Sarah Orr (W Berkshire) John Oxley (City of York), Sheena Payne-Lunn (Worcester), Colin Pendleton and Sarah Poppy (Suffolk), Rachel Salter (W Sussex), Mike Shaw (Black Country), Isobel Thompson (Hertfordshire), Liz Williams (Northumberland) and Julia Wise (Buckinghamshire) for sending through the HER datasets for these areas. Thanks, once again, to Keith Wescott at ExeGesIS for his continued support with processing the Englaid query for HBSMR-users.
The receipt of a new tranche of NMP data from English Heritage for the East Midlands region is much appreciated – we have now collated the NMP data for roughly half of England and are very much enjoying exploring its potential. One other more specific dataset we have kindly been given is Janice Kinory’s database of (mainly later prehistoric) salt-working sites, produced as part of her doctoral research. As the attendees of recent regional HER meetings have seen, we are now in a position to start interrogating in further detail the correspondence between various datasets and also their particular qualities.
During a small symposium we held last week on the subject of “scale”, I spoke briefly to Andrew Lowerre of English Heritage about my previous post on landscape morphology. He pointed me towards some of the more complex measures of landscape “bumpiness” than simply using average slope. Investigating further, I came across an apparent description of Riley’s Terrain Ruggedness Index (or TRI), which seemed like a good, relatively simple measure. This is fairly simple to calculate in ArcGIS if you were to follow the instructions taken from the webpage just linked. For England, using the OS OpenData 50m DEM, the result should look something like this:
If you compare the values on this TRI map (between 0 and 18.9 metres) against the classification given on the webpage linked above, you would end up classifying all of England as “level”, which seems fairly ridiculous, but this is down to a few obvious factors. First, a 50m cell size is quite small (Riley et al. used 1000 by 1000 m cells), so changes in terrain are going to be relatively small as well: changes in elevation of greater than 900 vertical metres across 50 horizontal metres are presumably only seen in landscapes like the Alps or Yosemite. Second, the DEM was interpolated by the OS from their contour dataset, which inevitably will have resulted in some smoothing of landscape features. Third (and most importantly), I have investigated further since and discovered that the maths / process are not actually correct for Riley’s TRI: when I found the original article of Riley et al. (1999), they are in fact calculating their TRI differently (theirs being the sum of the absolute [i.e. removing any negative sign] difference between a cell and all of its eight neighbours, whereas this result is the square root of the difference between the lowest and highest neighbouring cell value). This is a good lesson to learn (that I should have learnt already) in not trusting random websites found as the result of a Google search!
However, visual examination suggests the result is somewhat robust on its own terms (when compared against the results from the implementation of TRI in GDAL [which looks somewhat similar on the map, but has a range of 0 to 155m], and which uses the mean difference between a central pixel and its surrounding cells based upon Wilson et al. 2007, i.e. Riley’s sum but divided by 8 to produce the mean). In other words, even if the numbers are wrong, the relationship between the value in two cells should still be approximately correct, e.g. a cell with a TRI of “8m” ought to be more rugged than a cell with a TRI of “4m” even if the value of their TRIs ought to be much higher. I am not at all satisfied with this long term, but it will suffice for the purposes of the rest of this exercise. The Wilson et al. / GDAL implementation of TRI seems the most sensible to use in the long run, probably:
I have also been experimenting more with our synthesis methodology. This is based around the idea of applying a thesaurus to each of our input datasets to simplify their terminologies and then collating results by grid square. We have been working at length on defining a thesaurus that strikes the right balance between simplicity and complexity, so that it is interpretively useful but also computationally feasible. Our current thesaurus, based in part on groupings in the EH NMR thesaurus, is as follows:
01 – Agriculture and subsistence
A – Coaxial field system
B – Linear field system
C – Aggregate field system
D – Strip field
E – Unspecified field system
F – Linear earthwork
G – Pit alignment
H – Waterhole
I – Corn drying oven
J – Granary
K – Cairn
L – Fishpond 02 – Religious, Ritual and Funerary
A – Inhumation burial
B – Cremation burial
C – Inhumation cemetery
D – Cremation cemetery
E – Barrow
F – Cairn
G – Temple
H – Shrine / sanctuary
I – Church
J – Abbey / Monastery / Minster
K – Standing stone
L – Stone circle / cove 03 – Domestic and Civil
A – Town / Small town
B – Burh
C – Civitas Capital / Colonia
D – Hamlet / Village
E – Vicus
F – Canabae Legionis
G – Oppidum
H – Hillfort
I – Unenclosed settlement
J – Enclosed settlement
K – Linear settlement
L – Palisaded settlement
M – Riverside settlement
N – Dispersed settlement
O – Nucleated settlement
P – Road-side settlement 04 – Domestic architectural forms
A – Villa
B – Mansio
C – Roundhouse
D – Longhouse
E – Farmstead
F – Ringwork
G – D-shaped enclosure
H – Sub-rectangular enclosure
I – Banjo enclosure
J – Aisled building
K – Other rectilinear building
L – Burnt mound
M – Grubenhaus 05 – Industrial
A – Metal working site
B – Bronze working site
C – Iron working site
D – Mineral extraction site
E – Quarry
F – Pottery manufacturing site
G – Tile works
H – Lime kiln
I – Salt production site
J – Mint 06 – Communication and Transport
A – Road
B – Trackway
C – Hollow Way / Ridgeway
D – Drove road
E – Quay / Jetty / Harbour
F – Bridge
G – Canal
H – Aqueduct
I – Causeway 07 – Defensive
A – Hillfort
B – Fort (castellum)
C – Fortress (castrum)
D – Fortlet
E – Burh
F – Ringwork
G – Dyke 08 – Other
A – Mound
B – Ditch
C – Pit
D – Find
E – Hoard
F – Metalwork deposit
G – Watercraft
This is not a fixed list and we will undoubtedly continue to refine it over the coming months. The result of applying the thesaurus to a dataset is a new field containing a string of values based on the list above. The values are codes defining a particular period / type of site (sites can have multiple periods / types), e.g. RO04A would be a Roman villa site or IA07A would be an Iron Age hillfort. When we then apply a tessellation of grid squares across our input datasets, we can collate results using these codes to remove duplication, resulting in a map showing the presence of each type of site by grid square across England.
I have also been looking at different tessellations of grid squares. Until now, I had been using squares of 2 by 2 km dimension, as that seemed a sensible resolution for looking at patterns on the scale of England, but when applying this methodology in my latest tests, I have come to the decision that the resolution is too coarse. This is particularly compounded by the issue that when a data point falls on the intersection between four grid squares, it is registered as falling within all four squares, with the result that some sites have undue prominence in the synthesised dataset. I tried to find a way around this by using two overlapping tessellations with the origin of each cell in set B being at the central point of each cell in set A (as suggested to me at CAA by an audience member), but soon came to realise that merging these two datasets (accepting the presence of a site type only if it was in both overlapping cells) would simply replicate the result of applying a 1 by 1 km grid square tessellation layer.
Therefore, to decrease the coarseness of my grid square layer, I have now started using a tessellation of 1 by 1 km grid squares. The results are much more aesthetically pleasing. As a large number of our points will fall on the origins of these squares (where data locations are only known to the nearest kilometre square), I have also offset this new 1 by 1 km grid square layer by 500m east and 500m north. Therefore, these points that fall on the 1000m intervals will only be counted in a single grid square. This does mean that they might be slightly misplaced (by up to 500m east and 500m north) if they fell somewhere towards the northeastern corner of their kilometre square in actuality, but this is a very minor spatial misrepresentation on the scale of all of England.
As a test, I have run this synthesis methodology on the data received to date from English Heritage. This is the National Record of the Historic Environment (NRHE), which contains the former NMR records and records of sites found through the National Mapping Programme. Once processed, it consists of point, line and polygon shapefiles with associated attribute data. We currently have data for EH’s South West, South East, Eastern, and East Midlands regions. Here are some example results, with filled squares showing the presence of each period / type of site, bearing in mind that the empty swathes of the country to the north and west for each distribution are due to the lack of data for now (click on an image to zoom in):
Once the NRHE dataset is complete and once we also bring in the HER data that we are currently gathering for all of England, the distributions plotted will be much more complete, but I think that the result is beginning to show some promise.
Taking this further, we can then compare these distributions statistically against the various measures of landscape morphology calculated for each grid square, being the mean elevation and the mean TRI (i.e. the incorrect version originally calculated, see above). Elevation has a maximum value of 903.8m and the TRI a maximum value of 9.95m (when aggregated out from 50m cells to 1000m cells). Here are the results for each of the distributions shown above (bear in mind these are the results for 1km by 1km squares recording the presence of one or more of each of these types):
Bronze Age stone circles (150 results): mean elevation 235.9m ± 116.5m (1σ) (range 0.0 – 471.0m); mean TRI 3.2m ± 0.9m (1σ) (range 0.1 – 5.3m).
Iron Age hillforts (764 results): mean elevation 116.3m ± 69.5m (1σ) (range 0.0 – 439.6m); mean TRI 3.2m ± 1.1m (1σ) (range 0.0 – 7.6m).
Roman villas (2495 results): mean elevation 77.1m ± 53.8m (1σ) (range 0.0 – 360.5m); mean TRI 2.0m ± 0.9m (1σ) (range 0.0 – 6.5m).
Early medieval inhumation cemeteries (760 results): mean elevation 60.7m ± 53.8m (1σ) (range 0.0 – 234.0m); mean TRI 1.9m ± 1.0m (1σ) (range 0.1 – 5.4m).
As these distributions are incomplete, we should not read too much into these results, but some patterns do seem obvious. Hillforts (and stone circles) tend to be at higher elevations (logically) and on more rugged terrain, but have outliers right down to 0m elevation (these tend to fall along the coasts which are, thus, more likely to be edge effects than genuine 0m OSD hillforts, e.g. where a monument polygon overlaps a grid square with almost nothing but sea in it [I can partly correct for this by clipping my DEM to the coastline at a later date], but one is in northern Cambridgeshire [Stonea Camp]: should such a site really be defined as a “hill” fort?). By contrast, villas and early medieval cemeteries tend towards lower, flatter landscapes.
These are generally logical and fairly obvious results that we might expect to see without calculating any statistical measures, but it is still a useful exercise to run these analyses to try to confirm our intuitive assumptions and to attempt to discover any unusual cases that do not match what we might have expected (such as “hillforts” at sea level). When applied more extensively to more complete datasets for each of the thesaurus types defined above and for each period (where types exist in more than one period), we might discover some interesting patterns.
Obviously, this methodology remains a work in progress and I will continue to refine it over the coming months as more data comes in. This includes revising our thesaurus as research questions and the nature of our datasets becomes clearer and deciding on a better measure of terrain ruggedness (probably being the GDAL version).
Chris Green and Letty ten Harkel have just returned from the second Landscape Archaeology Conference (LAC) at the Freie Universität in Berlin. The Conference was a great success and overall was very enjoyable.
Letty presented a paper on her initial thoughts about her Dartmoor / Tamar case study, bringing in the concept of “hauntology” to try to think about how the early medieval inhabitants of Devon might have been haunted by the presence of previous dwellers, immanent in the landscape through their ruins and monuments, as seen in the monstrous inhabitants of ancient barrows so vividly captured in Beowulf.
Chris produced a poster on the methodological challenges confronted so far by the project, focusing in particular on his grid square synthesis system. Unfortunately, both Chris and Letty were too old to be eligible for any of the awards handed out, but both presentations of the EngLaId project seemed to be well-received.
Other papers of particular interest attended by Chris or Letty included: Kenneth Brophy / Gordon Noble on the immense Neolithic palisaded megastructures at Strathearn; Stefania Merlo on the short (in height) but extensive (in area) stone-walled megastructures of southeastern Africa; Jason Ur on the continuum between imposed (top down) and emergent (bottom up) ancient landscapes in ancient Mesopotamia; and Daniel Lawrence (and others) on dealing with uncertain chronologies using GIS, which had a lot in common with Chris’s particular research interests.
Overall, it was a very enjoyable few days in sunny Berlin and we hope to build on the contacts built in the future. LAC 2014 will take place in Rome, but there is a possibility that we may host LAC 2016 here in Oxford in coordination with the international landscape conference planned for the final year of the EngLaId project.