Field systems (IV)

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)…

Chris Green


Hall, D. 2014. The Open Fields of England. Oxford: OUP.

Field system orientation (III)

Following on from my previous work on field system orientation (I)(II), I have now finished data gathering for a set of 40 field systems across England, mostly within our case study areas, using data provided by Historic England’s National Mapping Programme. These cover almost 6,000 hectares and represent in part all of our time periods of interest (albeit there is only one early medieval example and even that is reuse of part of a larger prehistoric system). They should provide a decent set of evidence within which we can search for spatial and temporal patterns in prehistoric and Roman field system morphology.

I have gathered a whole series of metrics on these field systems (including dating evidence, length of boundaries, count of boundaries, etc.), which will be drawn upon in our later analyses, but I have started by thinking through orientation further. The set of graphs in the image below show the approximate orientation of field boundaries within each of our 40 field systems. The graphs require a little explanation. They each vary from 0 to 179˚ on the OS National Grid: this means that any axis through the centre of the graph represents 90˚ not 180˚. The black line shows the total length of boundary lines for each degree (relative to the bearing of greatest total length). Each line has been smoothed in order to bring out trends rather than showing the full complexity of the field system.

As such, symmetry along any axis on the graph can be seen as representing a stronger degree of “coaxiality”, as 180˚ on the graph represents 90˚ on the ground. Tighter peaks (so long as there are only two and they fall opposite each other) also represent a stronger degree of “coaxiality”. This provides us with a simple visual aid for assessing how “coaxial” or “rectilinear” a field system is and how each compares to other field systems. In this case, by “coaxial” I mean field systems where the boundaries tend to be orientated along two alignments perpendicular to one another.


The variation seen remains to be analysed, to see if there are patterns across time and space, but some tentative initial conclusions can be drawn:

  • Many field systems show strong perpendicular symmetry. This is often also the case with those that did not appear particularly “coaxial” in plan form.
  • Some field systems show no favouritism towards particular alignments, although even these often avoid certain alignments.
  • Currently, there appears to be a bias across the dataset as a whole towards a particular coaxial alignment approximately targeted on NNE/ESE, although this needs further investigation to see if this represents a strong bias in one particular time period or spatial area.

However, we have yet to explore this dataset in its fullest detail, so further work is needed. I will try to report on any interesting patterns seen here in the future.

Chris Green

Field system orientation (II)

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

Layout of field system south of Doncaster.

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

Radial graph for south of Doncaster.

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

Chris Green

Field system orientation

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

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

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

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

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

Layout of field system at Orcheston.
Radial graph for Orcheston.
Layout of field system at Netheravon.
Radial graph for Netheravon.
Layout of field system at Milston Down.
Radial graph for Milston Down.
Layout of field system at Maddington.
Radial graph for Maddington.
Layout of field system at Longstreet.
Radial graph for Longstreet.
Layout of field system at Figheldean.
Radial graph for Figheldean.

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

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

Happy Xmas everyone!

Chris Green


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

NMP Projects Conference

Yesterday, Anwen, Vicky and myself all attended at EH’s 2013 NMP Projects conference in Swindon at the (excellent – you get to walk under a steam locomotive!) STEAM museum.  It was a great day and it felt very useful to get some insight into how professional aerial photograph interpretation works and where the NMP is heading.

I gave a short talk on how we are planning on using NMP data and just had a few additional thoughts to add regarding questions asked afterwards (apologies for the irrelevance of this to anyone who wasn’t at the conference):

  1. You can find my blog posts on vectorising raster NMP here: (1)(2)(3)
  2. It occurred to me during the day that I should have put in a slide containing the map I produced regarding soil suitability for showing cropmarks: (4)
  3. Regarding the vectorised raster NMP layers, I don’t feel that these should be thought of as replacing the original scanned data.  There is no reason why we cannot have both versions on our GIS map: the scans for study at large scales (1:10,000) and the vectorised data for study at broader landscape scales (>1:10,000) where they function better at showing the results.
  4. I am very happy with the vectorised results, but they would still require a lot of work to perfect: polygons for ridge and furrow need drawing (although this is equally true of the raw raster data); areas where removed grid marks covered drawn features need filling back in; I would also like to remove the small hand written notes that feature on some of the tiles (and the typed notes on the Dartmoor tiles), as these don’t feel “right” in the vectorised version.  However, this is mostly just polishing and does not unduly effect their overall functionality as an interpretative tool.

All in all, it was a very productive way to spend a day and we would like to thank the organisers for letting us come along.

Chris Green

Happy Christmas and Thank You

Before we all sign off for Christmas, let us take this opportunity to thank the following HERs for kindly providing us with data over the last few months: Leicester City, Canterbury, Yorkshire Dales, Lake District, Plymouth, Nottinghamshire and Portsmouth. Special thanks must also go to Keith Westcott of ExeGesIS for helping with running the query in a few instances. This makes our HER data for the entire country almost complete!

Thanks as well to Helen Saunders from Essex HER for providing us with NMP data, and to Simon Crutchley, Lindsay Jones and Poppy Starkie for completing our NMP / NRHE data supply, and to Simon Crutchley and Mandy Roberts for taking the effort to visit us in Oxford. Finally, many thanks to the organisers of the HLC conference in London earlier this month for inviting us.

Last but not least, the EngLaId team wishes everyone a VERY HAPPY CHRISTMAS, and a wonderful start of 2013!

Red wine at Xmas

Processing raster NMP tiles (part 3)

We are now in receipt of all the NMP data (and associated NRHE data) currently possessed by English Heritage, alongside a couple of regions which were kindly supplied directly by the local HERs (Norfolk and Essex), and we would like to extend our thanks to Simon Crutchley, Lindsay Jones and Poppy Starkie for their work in pulling together these datasets for us.

I have previously discussed methodologies for processing the scanned (raster) maps which represent the results of the earlier NMP surverys (1) (2).  I am reasonably satisfied with the polygon result, but one issue that I have discussed with Simon Crutchley is whether it is possible to convert areas of rig and furrow (drawn with a dotted outline) into polygons representing their extent (rather than individual polygons for each dot).  Here is an example of the raster NMP:

1 raster
Raster NMP example

And here is the same data converted into polygons (with grid marks removed):

2 vectorised
Vectorised polygons generated

The first stage in converting the dotted outlines into filled polygons is to generate the line version of the same raster input data:

3 lines
Vectorised lines (red) generated, overlaid on polygons (click to enlarge)

We then create a 5m buffer around these lines (i.e. total width 10m):

4 mask
5 metre buffers around lines (red)

And then use this buffer layer to delete most linear features from the polygon version of the data (using the Erase tool in ArcGIS):

5 erase
Buffered areas erased from polygons

Most of the remaining objects are associated with areas of rig and furrow.  However, we can further improve the result.  First, we recalculate the areas of each polygon and filter the layer down so that we are only dealing with polygons of between 3 and 30 square metres in extent:

6 selection 3-30m2
Erased result filtered down to polygons of 3 to 30 sq. metres

This removes a few remaining linear features that were not previously erased.  Next, we generate centroids for each polygon (using the Feature to Point tool), run the Near tool on the result to get the distance to each resulting point’s nearest neighbour and filter (red dots) out those that are above a certain distance from their nearest neighbour (blue dots).  In this instance, I chose 40 metres, but I think a smaller value would have been better (probably 30m):

7 centroids red near 40m
Centroids generated for each polygon, filtered down to those within 40 metres of another point (red); blue points are those eliminated

We now have a point layer which for the most part represents the vertices for creating our rig and furrow polygons.  We can run the Near tool again, this time asking it to give the spatial location of the nearest neighbouring point for each point, and use the Calculate Geometry tool to insert two fields into the layer giving the location of each origin point.  The XY to Line tool can then create lines between each point and its nearest neighbour:

8 generated lines
Lines generated from points to their nearest neighbour

This result is getting fairly close to what we desire, but has some considerable problems.  First, none of the lines perfectly enclose the polygons, making it currently impossible to immediately process this result into a polygon layer.  It might be possible to fill some of these gaps using the Extend Line tool, but that is a very computer intensive task and liable to still produce an imperfect result (I left it running for 24 hours on this relatively small dataset before I gave up and cancelled it).  Second, in some instances, two lines of parallel dots can be closer to each other than the dots are within each line.  In this instance, the generated lines are drawn as links between the two parallel lines rather than along each individual line.

As a result, currently, if we were to try to convert this result into polygons, we would first need to fill in all of the gaps manually using editing tools and possible also delete all of the small sections of line that have no association with rig and furrow (albeit we could ignore most of these as they will not produce closed polygons in the result or, if they did, they are likely to be small in area and thus possible to easily filter out).  I do not think it is possible with out-of-the-box tools in ArcGIS to improve upon the line generation result, although it might be possible to develop a new tool to do so (perhaps with a directional bias towards its nearest neighbour to encourage linearity?).

I shall keep thinking about how to improve this process.

Chris Green

Nationwide survey – what would you be interested in seeing?

As our databases are nearing completion, we will shortly be in a position to begin looking at patterns in our data on the scale of all of England.  Our basic methodology for beginning to do this has been described previously (1) (2).  Obviously, this will just be one element of what we will be undertaking as part of this nationwide study, and we will also be looking in more detail at the form of features in the rural landscape (using NMP data primarily).

During our discussions with archaeological researchers across England over the last 8 months or so, it has become clear to us that many people have quite particular ideas about what they would like to see out of our England-wide survey, for instance the mapping together of particular sets of evidence.  Obviously because of the scale of our dataset we are also aware that we won’t be able to pursue all potential avenues of research.  Consequently, and since, as a project, we wish to be as open to outside ideas as we can, we thought this might be a good time to give blog-readers the opportunity to mention any groups of evidence that they would particularly like to see mapped and analysed on a nationwide scale?  All thoughts are welcome!

If you are interested in making any suggestions to us, please feel free to do so, either by commenting on this post or via email.

Processing raster NMP tiles (part 2)

In my previous post on vectorising raster NMP tiles, I concluded that for certain purposes it would be more analytically useful to create a version that consisted of lines rather than polygons (in order to attempt to look at the topology of field systems in particular).  I said that I would look into using the Thin algorithm in GRASS GIS, but I then noticed that ArcGIS also has a Thin tool, so I decided to play with that instead!

After some experimentation, I came up with a process for performing this conversion, implemented as three iterative models in ArcGIS.  The first stage reclassifies the raster tiles so that white areas have a value of ‘NoData’ and then runs the Thin tool on each tile (to, as you might guess, thin all of the lines):

Model stage 1

The second stage then iterates through the results of the first, performs the same reclassification (as the Thin tool seems to output a binary result with values of 0 and 1), then converts each raster tile into line shapefiles:

Model stage 2

For the final stage, we then make sure the projection is defined correctly, trim any small lines that are likely to be artifacts of the process rather than real features (less than 3m in length), and then run the Smooth Line tool to try to improve the aesthetic quality of the result:

Model stage 3

The results of this process look quite good on first appearance, and removing grid marks / lines worked even better than with the polygon results (using the same methodology as previously described):

brickwork fields
Line version of vectorised raster NMP (brickwork fields in Nottinghamshire).

However, the results are significantly more problematic than when converting to polygons.  As an example, areas of rig and furrow are represented in the raster NMP drawings as dotted outlines with arrows showing the direction of the furrows.  These show up fine in the polygon version, but the dotted outlines disappear in the line version.*  Yet the arrows remain.  Therefore, if studying the line version in isolation, there is no way to tell that the lines representing the arrows were once arrows and are not drawings of archaeological features.  Something similar happens with text on the maps.

Comparison of line (red) and polygon (grey / black) vectorised versions of raster NMP: note the disappearance in the line version of the dotted outlines of rig and furrow areas.

As such, on balance, this line version is less useful than the polygon version.  However, for areas of field systems it will be simpler to use this version for the study of topological relationships.  In order to do so, however, it is necessary to extract the field systems from other features and fill small gaps in the drawings of what are clearly continuous features in reality (which presumably exist due to gaps in the crop marks / earthworks).  The former could only be done manually, but I did make an attempt to extend the lines automatically to fill gaps.  The tool took 15 hours to run for a small section of Nottinghamshire and, although successful in some cases, on the whole produced a very messy (and useless!) result:

The rather messy result of trying to automate filling gaps in lines (originals in black, extensions in red).

As such, if I am to use this data to study the topology of field systems, I will have to make all these edits manually: to eliminate small gaps in continuous features and to remove features that are not themselves part of the field systems.  I will experiment with this and report again at a later date.

Chris Green

*  Incidentally, although this result is problematic for most purposes, it may provide a route into converting these areas defined by dotted outlines into polygons covering the extent of the enclosed area, by creating buffers around the line result and erasing the buffered areas from the polygon result (and then processing this somehow into filled polygons: I’m not sure how!), but that is something that will require a lot more thought / experimentation.

Aerial photography and ground obscuration (part 3)

I have previously written twice about factors affecting the possibility of discovering archaeological features using aerial photography (1) (2).  The original version of this took into account human factors (see post 1), based upon OS OpenData, specifically buildings, roads, railways, and woodlands / rivers / lakes (I count woodland as a human factor, as many of them are plantations and others have been left / managed by humans, rivers / lakes are obviously less human influenced).  Here is a reworked example, showing the percentage of the ground surface obscured by such factors:

Ground obscuration taking into account woodland and human structures. Relevant to aerial photography generally.

To add to this previous work, a few months back at our Project Advisory Board meeting, Jeremy Taylor of the University of Leicester told us about a paper which classified the different soil types of England and Wales according to their prospects for showing buried features (whether geological or archaeological) as crop marks (Evans 1990).  This paper grouped the different soil types defined by the 1983 Soil Survey of England and Wales into five categories:

  • Soils that show extensive crop marks;
  • Soils that show extensive crop marks in dry conditions;
  • Soils that show frequent crop marks over small areas;
  • Soils that rarely show crop marks;
  • Soils that never show crop marks.

Usefully, the 1983 soil survey maps have been digitised and are made available via the National Soil Resources Institute (NSRI) at Cranfield University.  As a bona fide researcher, this is available on payment of a processing fee (i.e. without paying royalties), so we obtained this data to try to create a map of Evans 1990 classification.  On receipt, I found that there were five soil types (924a, 924b, 952, 961, 962) in the NSRI data that did not show up in the Evans 1990 classification.  However, these were all types of industrial spoil heap or reconstructed ground surface, so I assumed that they would fall within the category of soils that never show crop marks.  As a result, I was able to reclassify the NSRI soils data into Evans’ five types:

Soil types of England and Wales graded according to possibility of showing crop marks. After Evans 1990.

In respect of ground obscuration, I decided to only take the “never” category as being a factor.  I therefore combined this with the “human” factors and the peat / alluvial sub-soils from the British Geological Survey data (see post 2) to create a new map of ground obscuration:

Ground obscuration taking into account soil type, some sub-surface geology (peat / alluvium), woodland, and human structures. Relevant to crop marks.

This latest map is only really relevant to crop marks, as presumably earthworks would still show even in the more unsuitable soils (under the right lighting / weather conditions).  We can see, however, that large parts of England are not at all suitable for aerial prospection for crop marks.  I am not sure that the large peaty areas seen in the east would necessarily mask all crop marks, but then all models are imperfect.  It might be useful to include the “rare” crop mark soils in this map too, perhaps with a lower weighting to reflect their partial effect, but I decided to work for now on a stricter basis.

The point of all this is to assess the effect of ground obscuration on the patterning of archaeological discoveries.  As an example, we can compare the distribution of NMP projects against this map of ground obscuration:

NMP projects as of 2012 compared against ground obscuration (re. crop marks).

This shows a reasonably good correlation between areas in which NMP projects have been (or are being) undertaken and areas suitable for aerial prospection (for crop mark features).  Where the NMP projects cover areas unsuitable for crop mark discovery, these tend to be upland areas where thin soils ought to mean earthworks are fairly prominent (I think).  It also shows that although the NMP covers around 50% of England, much of the remaining 50% is not necessarily suitable for NMP type projects (as they are reliant on aerial photography), with the particular exception of the rural West Midlands and parts of East Anglia.  However, many of these less suitable areas might be suitable for LiDAR survey.

Further down the line, I will be comparing distributions of archaeological sites against these obscuration maps to see if I can discover any intrinsic biases towards areas suitable for aerial prospection within the data.

Chris Green


Evans, R.  1990.  “Crop patterns recorded on aerial photographs of England and Wales: their type, extent and agricultural implications.”  Journal of Agricultural Science, Cambridge 115, 369-382.