For anyone who has bought a copy of English Landscapes & Identities and wants a better look at some of the images, we have uploaded the pre-publication versions in a PDF here:
See the image below for a 30% discount code for the book.
For anyone who has bought a copy of English Landscapes & Identities and wants a better look at some of the images, we have uploaded the pre-publication versions in a PDF here:
See the image below for a 30% discount code for the book.
Our final publication has today been published, an atlas of art and archaeology:
The ebook is free to download, just click on the Download Full PDF button on the page linked above.
We are pleased to announce that our main project book is out today with Oxford University Press. See the image below for a 30% discount code.
We hope those of you who read it, find it illuminating! We also have plans to publish an atlas based on the project, more news on that soon.
Tyler and I combined our presented material into a chapter for the conference proceedings, which can now be read here:
And the full set of papers can be found here:
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 is the text and images from a book chapter that I have recently had published. The published version has had the images converted to black and white, so I thought I’d post the proper version here, as I still hold the copyright for the chapter and it works better with colour imagery. This is the bibliographic reference if you want to cite me or find the rest of the book, which is a very interesting read indeed (no claims made of the same for what follows!):
Green, C. 2019. “Cartography and Quantum Theory: in defence of distribution mapping.” In: M. Gillings, P. Hacigüzeller, G. Lock (eds.) Re-mapping Archaeology: Critical Perspectives, Alternative Mappings. Abingdon: Routledge, 281-299. ISBN: 978-1-138-57713-8
Endnotes have been included after the paragraph in which they are referenced, rather than at the end of the text.
13 Cartography and Quantum Theory
In Defence of Distribution Mapping
Distribution Mapping in Archaeology: a Perceived Problem
This paper was written whilst working on the English Landscapes and Identities Project (EngLaId) at the University of Oxford. During my time working on EngLaId, I have been confronted with two things: my own improvement as a cartographer (by which I mean a maker of maps) over a substantial period of my life (see below); and how to gestate years of experience into guidelines for the somewhat less practiced members of the project team, employed to work with GIS and maps on an almost daily basis but mostly with little formal cartographic training. Alongside the rest of the project team, I have also spent a great deal of time pondering how to present the extremely complex and characterful data of the project to academic and public audiences (Green 2013; Cooper & Green 2016): the main EngLaId database contains over 900,000 records of English archaeological remains from the Middle Bronze Age (c.1500BC) to the end of the early medieval period (c.AD1000), including an almost unquantifiable large element of duplication due to its origin in a multiplicity of source datasets. Turning this complexity into a series of comprehensible messages is a difficult task (Green et al. 2017).
If we ignore a childhood spent doodling maps of imagined lands, my first real experience of formal cartography came as an undergraduate archaeology student at the University of Durham. As I would imagine is the case with many archaeologists (and indeed many geographers), I first encountered GIS as a map making tool (obviously it is also much more, but this paper only considers the cartographic aspects of GIS) as part of a course on computers and computing in archaeology. I recall little formal teaching on good map design, and maps that I subsequently made for my dissertation, which was an attempt to construct a “mental geography” of Roman Britain, show a considerable cartographic naivety (Figure 13.1): amongst several errors, note the excessively large north arrow and the use of a fractional scale for a map which would inevitably be resized in the final document (thus making the scale incorrect, as it also is here).
Fig. 13.1: Map created by the author in 1999 as part of his undergraduate dissertation. The lines depict routes in the Antonine Itinerary (after Rivet & Smith 1979).
This, then, is the situation in which many archaeologists find themselves: forced to make maps as part of their projects or employment, but with little expertise in what we might call good cartographic practice and with little opportunity to learn other than through trial, error, and repeated effort, especially in the academic sector (more structured training and formal cartographic guidelines may be provided in the commercial sector – Victoria Donnelly, pers. comm.). One consequence of this is the mistaken orthodoxy of certain prescribed practices, such as “a map should always have a scale bar” (Wood 2003: 7): many commonly used map projections (including the still very widely used modern derivatives of Mercator) do not preserve distances and so adding a scale bar to a map of large areas of Earth created in such a projection is simply incorrect (as measured distance will vary across the map) (Figure 13.2).
Fig. 13.2: Map of the world rendered in the very commonly used “WGS84” projection setting in ArcMap. Thick black lines are of 1000km length illustrating the incorrectness of adding a scale bar to such a map (or at least just a single one). Contains data derived from the GADM Database of Administrative Areas (http://www.gadm.org/).
Another consequence of cartographic naivety is the widely heard verbal (albeit rare in print) complaint that mapping distributions is “just putting dots on maps”. Although it may often be correct to say that dots can be a poor way of representing extended areas of past settlement (Evans 2000: 3), that is essentially a question of the scale at which one works. Scale is, in fact, key herein and we shall return to the issue later. One form of distribution mapping for which complaints of being “just dots on maps” are somewhat justifiable are the very common phenomenon of “sites referred to in the text” (another mistaken orthodoxy), at least where no spatial context is given beyond a coastline and perhaps major rivers. Without sufficient context (an issue we shall return to below), such maps are largely without merit, as they will be of little practical use to readers who do not already possess a strong knowledge of the geography of the area mapped: a gazetteer with grid references would be far more useful if a reader wished to subject results to further data analysis. At the very least, a distribution of all sites of the same type should be included, rather than simply those chosen by the author to write about: without that, any conclusions drawn by a reader (or the author) regarding the distribution of the sites discussed will be entirely meaningless.
In part, criticism of distribution mapping arises out of the wider phenomenon of postmodern critique, specifically seen in archaeology in the tension between so-called phenomenological and Cartesian approaches (Sturt 2006: 121): maps are seen as a tool of positivism, providing a restricting and classificatory top-down perspective at odds with lived experience on the ground in the real world. Yet the recovery of lived experience of the past is an impossible dream. Crampton states that there is, to the postmodern eye, something unseemly about maps (2010: 6):
“These wretched unreconstructed things seem to work so unreasonably well! [original emphasis]”
Crampton accepts that maps enabled many abhorrent elements of the modern age, such as colonialism, racism, warfare, espionage, and that GIS is seen by some as a Trojan Horse for the return of positivism. Yet he states that this is not exclusively true of maps but also of many other things. Like the power of the atom, maps can be used for beneficial as well as unfortunate purposes (Crampton 2010: 6–7). Maps in and of themselves, then, are not part of the problem: how we use them and how we construct them are the real issues. Making a map involves an inevitable task of classification and simplification of real world phenomena, but this can as easily be approached as a subversive act as it can as an act designed to promote an imperialist (whether colonial or economic) agenda.
Map Making in a Digital World
Through the 20th and early 21st centuries, the practice of making maps (alongside GIS generally) became increasingly divorced from both geography and cartography as academic subjects, to the extent that people who never made maps (e.g. lecturers) could claim to be cartographers (Crampton 2010: 2). Conversely, it is now possible for any person with a computer and access to the internet to make a map: cartography as a practice has opened up to the public and escaped from its academic confines, aided by practices such as “map hacking” and “mash-ups” (Gartner 2009) and software such as Google Earth (Collins 2013). However, this democratisation is highly dependent upon access to computers and information technology expertise (Crampton & Krygier 2006: 12, 18–19; Field & Demaj 2012: 70). Today, the use of maps and spatial technologies has never been more common and more widespread (Crampton 2010: 11), immanent in the continually strengthening “geo-lifestyle” revolution (Field 2009: 59).
Some celebrate what they see as the death of cartography as an academic subject (see Wood 2003 for a wonderful polemic on the question), although as a practice it has never been healthier (Crampton 2010: 24). Academic journals on the subject and on GIS more widely are now largely dominated by technical issues rather than the aesthetic or the political (Crampton 2010: 5), whereas the people actually making maps have changed, now including data artists, journalists, and coders. Non-cartographers have always made fine maps from time to time (e.g. Harry Beck’s Tube map, Charles Minard’s map of the advance of Napoleon into Russia [Figure 13.3], or John Snow’s epidemiological map of cholera in London), but the world is now awash with maps made by untrained map makers (Field 2015: 93). This should be seen in a positive light, with cartography as a practice slipping from control of the elites who have dominated it for centuries (Crampton 2010: 40) in what Field drolly calls (2014: 1):
“…a cacophony of cartography… a harsh, often discordant mixture of the weird and the wonderful.”
Fig. 13.3: Charles Minard’s 1869 map of Napoleon’s Russian campaign of 1812–3, showing the successive losses of soldiers along the route. This is a public domain image obtained via Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Minard.png).
The reaction within the academic fields of cartography and GIS to democratisation of cartographic practice has been less positive in many cases. Professions with power organise and force through legislation to protect their positions (e.g. law or medicine), whereas weaker professions create certification criteria and denigrate the output of non-professional rivals (Wood 2003: 5). Cartography and GIS fall into the latter category, as seen in the existence of the GIS Certification Institute in the USA (Crampton 2010: 36). This rather parallels the role of the Chartered Institute for Archaeologists (CIfA) in British archaeology: like archaeologists, any person can now claim to be a cartographer, which some find discomfiting. Systems of ethics (Dent et al. 2009) and guidelines (Southworth & Southworth 1982) for “good” map design exist (MacEachren 1995; Monmonier 1996; also Tufte 2001 for data visualisation more widely), but are largely ignored by untrained makers of maps, causing professional cartographers to call for a return to best practice (Field 2005; 2014; Kent 2005: 187).
 CIfA is a membership organisation which requires the provision of references and work samples to become an accredited member, and which involves members signing up to a code of conduct, etc. See: http://www.archaeologists.net/
To return to the example of my own career, from 2001 to 2002 I studied for an MSc in GIS within a geography department. There, I learned a great deal more about GIS and about making maps (including cartography taught as visualisation – Field 2005: 81). However, the great majority of my peers had been through the undergraduate geography degree and, as such, certain key concepts were not covered in a great deal of detail (particularly map projections). In 2005, I commenced a PhD looking at the representation of time in archaeological GIS (published as Green 2011). In the course of that piece of work, I made a great many more maps and my cartographic skills improved (Figure 13.4). However, I still made mistakes, albeit ones that might be less apparent at first glance: in Figure 13.4, note the division of the first four sections of the scale bar into 2.5km units (five 2km units would perhaps be more logical) and the spurious precision of the values in the legend. I also dislike the ArcMap default American spelling of “Kilometers”. Furthermore, it would have been better to extend the extent of the trend surface to the full bounds of the county so that there were no white areas within the boundary polygon.
Fig. 13.4: Map created by the author in 2008 for his PhD thesis (later published as Green 2011). The trend surface shows a model of pottery deposition in Northamptonshire between AD115 and 240. This image contains data derived from Jeremy Taylor’s unpublished PhD thesis, originally gathered by David Hall.
The point of this autobiographical example is that even with training and practice, those engaged in map production may still make mistakes, which in some cases might confuse the message being communicated. They may sometimes only be obvious to experienced cartographers, but the key message here is that no map can ever be perfected to the taste of all audiences. Those without formal training in cartography might make more mistakes or produce maps that fail to please the cartographic profession, but their ability to produce new insights should be embraced. It is clearly not possible, nor probably desirable, to provide cartographic training to every person who now wishes to make a map, but some form of guidance is needed if people are to produce comprehensible output. For that guidance, we can find inspiration in what is perhaps an unlikely arena: quantum theory.
The Uncertainty Principle
The Uncertainty Principle, formulated by Heisenberg (1927), forms one of the foundational elements of the field of quantum mechanics. Hawking described its effects eloquently (2011: 62-63):
“In order to predict the future position and velocity of a particle, one has to be able to measure its present position and velocity accurately. The obvious way to do this is to shine light on the particle. Some of the waves of light will be scattered by the particle and this will indicate its position. However, one will not be able to determine the position of the particle more accurately than the distance between the wave crests of light, so one needs to use light of a short wavelength in order to measure the position of the particle precisely. Now… one cannot use an arbitrarily small amount of light; one has to use at least one quantum. This quantum will disturb the particle and change its velocity in a way that cannot be predicted. Moreover, the more accurately one measures the position, the shorter the wavelength of light that one needs and hence the higher the energy of a single quantum. So the velocity of the particle will be disturbed by a larger amount. In other words, the more accurately you try to measure the position of the particle, the less accurately you can measure its speed, and vice versa. Heisenberg showed that the uncertainty in the position of the particle times the uncertainty in its velocity can never be smaller than a certain quantity, which is known as Planck’s constant. Moreover, this limit does not depend on the way in which one tries to measure the position or velocity of the particle, or on the type of particle: Heisenberg’s uncertainty principle is a fundamental, inescapable property of the world.” [my emphasis]
The useful part of this for our purposes here is the idea that the greater the precision one can discover about one aspect of a particle’s state, the lesser the precision that one is able to discover about another (related) aspect of said particle’s state. This appears to be a fundamental property of the universe that we exist within. Whilst I would not propose the existence of a Planck’s constant for cartography, the idea that precision of measurement on one variable is related to precision of measurement in another variable can form a very useful metaphor for the untrained cartographer for estimating the level of detail that can be comprehensibly placed upon a map, with the “particle” being equivalent to any element placed on a map.
 Incidentally, knowledge of Heisenberg has previously been suggested as a desirable trait for archaeologists, albeit as a metaphor for greater archaeological understanding of the natural sciences rather than anything specifically to do with Heisenberg’s work (Pollard 1995). One might also get a sense of the Uncertainty Principle in Olivier’s Cycles of Memory (2011: 190–194) and certainly in his uncertainty principle of the archaeological past, in which he states that it is impossible to know both the position of any moment of the past in time (i.e. its date) and its rate of transformation (i.e. its place in evolutionary history) (Olivier 2001: 69).
Using the Uncertainty Principle to Understand Archaeological Cartography
Time (usually as date or period), space (usually as place of discovery), and type (often as typological category) are fundamental to the nature of archaeological data. Time and type have been extensively theorised within archaeology, but space arguably less so, with most theoretical considerations of space in archaeology focusing on ontological questions (e.g. space as an a priori container) rather than epistemological ones. Key to the epistemological theorisation of space is spatial scale, with different past processes potentially discernable at different scales (Wheatley 2000: 123, 128; Lock & Molyneaux 2006b: 1; papers in Lock & Molyneaux 2006a, particularly Yarrow 2006).
 The terms “large” and “small” are problematic when applied to spatial scale due to the differing conceptions of cartographers (who use “large” to mean a large representative fraction, e.g. 1:10,000, and “small” to mean a small representative fraction, e.g. 1:250,000) and most other people (who would expect a “large” scale analysis to cover a large amount of space, and vice versa) (Lock & Molyneaux 2006b: 5). As such, herein I will use the terms “narrow” to refer to spatial scales that cover smaller amounts of space in higher detail and “broad” to refer to spatial scales that cover larger amounts of space at lower detail.
Spatial scale is one of the attributes of archaeological data that we can approach using map production methods inspired by the Uncertainty Principle. For spatial scale, its linked attribute is spatial resolution: the size of the objects used on a map to represent the archaeological data, whether they be dots or spatial bins (see Green 2013 for more discussion on the latter). When working at broad scales, dots placed on a map will often cover many square kilometres of space and, as such, spatial precision matters less. As one moves in to narrower spatial scales, spatial precision becomes more important and the spatial resolution of the objects should become finer. To give examples from within the context of EngLaId (Figure 13.5a–b), at our broadest spatial scales (all of England as a small printed image) the most appropriate forms for mapping distributions would be interpolated surfaces such as Kernel Density Estimates (KDE) (O’Sullivan & Unwin 2010: 68–71) or trend surfaces (O’Sullivan & Unwin 2010: 278–287), as these operate at coarse spatial resolutions where patterns remain visible that would be obscured or invisible if raw data was mapped (see Green et al. 2017). As our spatial scale narrows, we would move on to using various resolutions of aggregated spatial bins (Green 2013). Finally, at very narrow spatial scales, we would use the raw data directly exported from our database.
Fig. 13.5: Examples of maps from towards each extreme position; (a) broader scale, coarser resolution; (b) narrower scale, finer resolution; (c) coarser temporal precision, higher typological precision; (d) higher temporal precision, coarser typological precision.
The second set of linked attributes of archaeological data that can be conceptualised using the Uncertainty Principle as a metaphor are time and type. Time in archaeology can be represented at a range of precisions from the complex probabilities of scientific dates (e.g. probabilities output from software like OxCal; Bronk Ramsey 1994) through to very coarse broad time periods (e.g. Iron Age, Bronze Age, etc.). Equally, type of object (whether a site, an artefact, an ecofact, etc.) can be represented at a range of categorical precisions from the broad (e.g. defensive monument) through to the more specific (e.g. bivallate hillfort, or conceptually even a single specific site). Clearly, as a database representation, it is possible to store and manipulate all of this detail for every record in a dataset. However, to attempt to represent the full complexity of our objects cartographically would be impossible (or, at least, incomprehensible). As such, these two attributes can again be conceived as related within the bounds of the Uncertainty Principle: when making a map, the greater the temporal precision of mapped objects, the lesser the typological precision or complexity that should be applied. To give further examples from within the context of EngLaId (Figure 13.5c–d), if we were to map objects using precise temporal probabilities for time-slices, then only objects of a single type or of grouped types could be plotted on a map. Conversely, if we wished to create a map which showed the full gamut of site types in our database, then we could only map objects of a single time period (or all time periods grouped together). The latter case would still produce a map that was too complex to be understood by a reader, but the point is conceptually sound.
Bringing this all together (Figure 13.6), the two sets of related properties are also subject to a similar precision dependency. Broad spatial scale, coarse spatial resolution maps can only articulate relatively simple time / type relationships. Conversely, when working at narrower spatial scales and finer spatial resolutions, then more subtle time / type relationships can be more easily expressed. When working with this model in practice, an archaeological cartographer should begin by considering the research question which their proposed map is attempting to explore and who their anticipated audience is. Audience is key, as the complexity of a map can be at its greatest for internal analytical usage (as we can assume a researcher will be very familiar with their own data) and its lowest for public-facing usage (where we can assume no familiarity).
Fig. 13.6: Model depicting the application of the Uncertainty Principle to archaeological cartography.
Assuming a large dataset, if the research question requires very precise time and type, then it can only be answered at relatively narrow spatial scales and, as such, the researcher should start at the top of the model (in Figure 13.6) and work down. Conversely, if the research question requires working at broad spatial scales, then the researcher should start at the bottom of the model and work upwards. As a brief example, if one wished to make a five by five centimetre map of grave goods dating to between AD 50 and AD 100 in England, the resulting map would be best done as a density surface (due to the small image size) and could only feature the broad category of all grave goods, or alternatively one specific sub-category. In this way, when making a map of archaeological data, even an inexperienced cartographer ought to be able to ascertain a sense of what level of time / type complexity and what spatial scale / resolution they should be working at, through their metaphorical understanding of the Uncertainty Principle.
Providing Better Context for Distribution Mapping
As something of an aside and to move away from quantum theory, another key element of increasing the reputation of distribution mapping in archaeological circles lies in improving the contextual information provided to our maps’ audiences. It may be a cliché, but context is key to archaeological research, both on site and when zooming out to study broader areas of space. When mapping archaeological data regionally or nationally, it is important for cartographers to take account of the manner in which archaeological distributions are governed by patterns in fieldwork undertaken (Evans 2000: 3). This has become particularly important since the advent of developer funding of commercial archaeological fieldwork (in 1990 in Britain), as the location where the vast majority of investigations that take place since that time has been governed primarily by planning concerns rather than by archaeological research questions. This need not be conceived of as a problem to be corrected, but rather an element of the character of the data that should be studied and embraced (Cooper & Green 2016).
As such, it may be better to conceive of this relationship between fieldwork patterns and archaeological distributions using the concept of affordance. Introduced into archaeology via Ingold (1992), affordances are used within the discipline as a way of understanding the mutually constitutive relationship between people and their environment (Gillings 2007: 38–39). Essentially, the relationship between the archaeological record and patterns of development / fieldwork can be viewed in similar vein: practices in the modern world give rise to opportunities to analyse archaeological remains. Acquiring an understanding of this relationship is vital to understanding archaeological distributions today.
In order to enable quantitative analyses of this relationship, the EngLaId project constructed a model of modern affordances that have been affecting the structure of the archaeological record in England since 1990 (Figure 13.7) (for more on the structure of this model and the data built into it, see Green et al. 2017). This particular model reflects factors relating to excavation and aerial photographic survey, currently the two most common sources for information that has entered the English archaeological record (LiDAR and geophysical survey are still only just starting to result in significant numbers of new “sites” in England, being mostly employed to study sites that were already known). We can use the model to test distributions of archaeological sites in an attempt to assess the extent to which they are influenced by modern fieldwork patterns or by more genuine patterns of past practice. Contextualising our data in this way is vital if we wish to make our distribution mapping work better at communicating and explaining any patterns that we might discover, as the distributions we map are mostly no longer primarily structured by past practice (if they ever were). Modern affordances are just one example of better contextualisation, but they are a particularly key one. In combination with a more structured understanding (as discussed above) of the relationships between scale, resolution, time, and type, improving the contextual elements of archaeological maps can only make their messages more powerful.
Fig. 13.7: Model of modern affordances affecting the structure of archaeological distributions in England. This image contains modified data originally derived from: Historic England NRHE Excavation Index (hosted at: http://archaeologydataservice.ac.uk/archives/view/304/); Centre for Ecology & Hydrology’s Land Cover Map 2007 (hosted at: http://doi.org/10.5285/2ab0b6d8-6558-46cf-9cf0-1e46b3587f13); NSRI National Soil Map of England and Wales (hosted at: http://www.landis.org.uk/data/natmap.cfm).
 Planning data was impossible to collate, so excavation affordances were modeled using the density of previous excavations from 1990 to 2010, including excavations with negative results and those that found material of any time period (to minimize bias introduced by focusing on specific periods).
 Factors affecting aerial photographic survey were modern land use (arable representing crop mark potential; pasture representing earthwork or parch mark potential) and obscuration of the ground surface, whether by above ground structures / water bodies / woodland or below ground masking superficial geologies / soil types.
 For example, hillforts score low on this model, as they tend to be in areas where opportunities to find archaeological material are low but being large and obvious monuments they tend to be easy to discover despite that. A monument category like Anglo-Saxon sunken featured buildings (also known as Grubenhäuser) score much more highly on the model, as they are less obvious and thus require greater levels of opportunity to be discovered.
Conclusions: Making Better Archaeological Maps?
To return one final time to my autobiographical examples, since 2011 I have been employed on the EngLaId project as a postdoctoral researcher in GIS. As a result, I have spent many hours making many different maps of many different datasets. I have regularly made maps for my own purposes, for other project members, and for other people working within our department. As a result, my skills as a cartographer have improved immensely, to the extent that I often feel quite a strong sense of pride over the maps that I produce (e.g. Figure 13.8). Distilling that experience into a model of good cartographic practice for my own purposes and for the help of others has resulted in this paper.
Fig. 13.8: Map created by the author in 2016. Darker green shading depicts greater levels of woodland cover in the eleventh century AD (after Roberts & Wrathmell 2000: figure 24), collated by post-medieval parishes (Burton et al. 2002). This image contains data derived from Sturt et al. 2013 (the former sea levels around The Wash).
Generally speaking, in my work I start with the question of scale and move upwards through the model outlined above, considering resolution next to maximise the legibility of the map produced. For example, although there are statistical methods for determining the appropriate kernel size for a KDE surface (e.g. the Mean Integrated Squared Error; Marron & Wand 1992), making a choice based upon cartographic legibility seems more logical to me as that should always be a key concern when making any map. Visualization for the purposes of research (of which map making is but one strand amongst many) exists to save time during the processes of discovery and communication of information and knowledge (Chen et al. 2014), with “pleasantness” key to visual efficiency (Kent 2005: 184). As a specific technique, map design lies at the central point between art, science, and technology, with no single recipe for success (Field & Demaj 2012: 73–75). As such, cartographic concerns such as legibility (including taking account of colour blind audience members) and aesthetic appeal should be foremost in our minds when making maps. A metaphorical understanding of the Uncertainty Principle as outlined above can aid greatly in that task, providing guidance to aid the archaeological cartographer without dictating or attempting to control methods or outputs.
We make maps and use GIS in an attempt to make sense of the geographical world: they are as artistic and as political (Crampton 2010: 3, 12) as the world around us. They imperfectly reflect (or refract?) reality and should be embraced as such. Thus, we should not be anxious about cartography in and of itself, only anxious about approaching it uncritically (Crampton 2010: 184). Maps are no longer only useful for the increasingly moribund hypothesis testing / pattern confirmation practices of the old scientific method, but can be vital to modern exploratory data analysis / pattern seeking methodologies (Crampton & Krygier 2006: 24) as part of hybrid approaches to understanding the world / archaeology (Hacıgüzeller 2012: 257) through suggestion, not definition, of spatial patterning (Sturt 2006: 131).
Distribution mapping of archaeological material remains vital to the understanding and communication of the results of fieldwork and other investigations, giving a flavour of space and presenting an idea of the relationship between archaeological remains and local / regional topography (Sturt 2006: 131). Careful cartographic choices (e.g. using models such as that outlined above) and an understanding of context (including modern affordance patterns) are vital for achieving critical comprehension of our material in all of its wonderful character. Archaeologists are very rarely trained cartographers, but this should not be seen as a problem, more as an opportunity. Hopefully, the ideas outlined in this chapter will help guide those wishing to make maps of archaeological data down fruitful avenues, whereby effective communication is enabled through an understanding of the vital inherent relationships between scale / resolution and time / type. Finally, I will leave it up the reader to decide if I have improved as a cartographer since I made my first maps in the late 1990s, but I am certain that my creation of the model presented here has aided in whatever improvement I have achieved.
This paper was written whilst working on the English Landscapes and Identities Project (EngLaId) at the University of Oxford. EngLaId has been funded by the European Research Council (Grant Number 269797). My thanks go to the other members of the EngLaId team for providing a stimulating research environment without which this work would not have come to fruition. In particular, the ideas presented in this paper arose out of fruitful discussions with Anwen Cooper on the capacities of our evidence and how we can present it to the world. I would also thank Anwen Cooper, Miranda Creswell, Victoria Donnelly, Michaela Ecker, Letty ten Harkel, and Sarah Mallet for helpful feedback on draft versions of this paper, as well as the editors of this volume for the further useful advice.
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Some readers may have noticed that the URL for our blog has reverted to englaid.wordpress.com – any other ‘englaid’ related domains are not affiliated to our project, other than englaid.arch.ox.ac.uk
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:
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
Green, C., C. Gosden, A. Cooper, T. Franconi, L. Ten Harkel, Z. Kamash & A. Lowerre. 2017. Understanding the spatial patterning of English archaeology: modelling mass data from England, 1500 BC to AD 1086. Archaeological Journal 174(1): p.244–280. http://www.tandfonline.com/doi/full/10.1080/00665983.2016.1230436
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.
EDIT: Since writing this blog post, Martin Sterry has published a paper on his visualisation techniques, which can be found here: https://doi.org/10.11141/ia.50.15
We have compiled a playlist of a few EngLaId talks that are available to watch on Youtube. There are a couple of videos of Chris Green giving talks on EngLaId topics and a couple of videos of Chris Gosden giving longer lectures on the project. There is also a video of Miranda Creswell drawing at Danebury and a short video of reactions to a talk given by Chris Gosden.
This is just a short post to announce the publication of our new paper on performing analysis of PAS data. It is open access and has been published by Internet Archaeology:
This study tackles fundamental archaeological questions using large, complex digital datasets, building on recent discussions about how to deal with archaeology’s emerging ‘data deluge’ (Bevan 2015). At a broad level, it draws on the unprecedented volume of legacy data gathered from many different sources – almost one million records in total – for the English Landscape and Identities project (Oxford, UK). More specifically, the paper focuses in detail on artefact evidence – material derived primarily from surface surveys, stray finds and metal detecting. Novel computational models are developed that extend and connect ideas from usually distinct research realms (different arenas of artefact research, digital archaeology, etc.). Major interpretative issues are addressed including how to approach background factors that shape the archaeological record, and how to understand spatial and temporal patterning at various scales. Overall, we suggest, interpreting large complex datasets sparks different ways of working, and raises new theoretical concerns.
Here is a short video demonstrating the usage of the Portal to the Past website:
The website can be found at: http://englaid.arch.ox.ac.uk
EDIT: Here is a second longer video with audio commentary: