(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
English Landscape and Identities 
it is always a bit strange commenting on a blog with people you haven’t personally met or know, but I wonder how important it might be to go back what is it, nearly four thousand years, when looking at map data with this level of abstraction, granularity, scalability? And even now, the tides vary by 6 metres I am told in Eastbourne. Incidently as I fill in below, WordPress of course recognises me from WordPress Space, so I have to make a bit of a change.
Sea level change is something we are planning on building into our models once we have access to data in that regard. Ideally, we would also want to model post-glacial rebound, but I’m not sure that is easily done or has had sufficient effect to be relevant on this scale. Away from the coasts, I would expect modern terrain to not be too dissimilar from terrain in the middle Bronze Age (our starting point), probably. These are all things that need consideration.