Digitizing the Roman Imperial road network

I originally posted this on the UrbNet blog.

The Romans built an expansive road network. Thousands of kilometres of very well-designed roads connected regions as far apart as present-day Britain, Morocco, Egypt and Turkey. This network shaped and structured European transport systems in ways that are clearly visible today. In the centuries that followed the Roman Empire, people and goods still very much followed Roman routes, and subsequent kingdoms and empires gradually elaborated and modified the Roman core of the transport system. But even today, many of the key transport routes throughout Europe still follow the ancient Roman transport system.

Many research topics in Roman Studies are dependent on a good knowledge of the Roman road system. How did the Roman military march from one frontier to another, and how were they supplied with the necessary subsistence goods? What routes did inland distribution of grain and oil follow to supply for the needs of urban populations? A good understanding of the Roman road system is even crucial for studies of movements of people and goods in later periods, because this system was so foundational for European infrastructure.

So Roman roads are important. Sadly, this importance is not reflected in the available resources needed for exploring these research topics. To be clear, Roman roads have received vast amounts of research attention, their tracks are very well documented for most parts of the Empire, as are associated objects like Roman milestones and waystations. The issue lies in the aggregation of this evidence and research. Detailed information derived from excavations of parts of Roman roads is often not systematically used to update regional road maps, if such regional aggregations even exist. This has led to a very patchy overall picture: for some regions which have seen a lot of research attention we have a pretty good and detailed picture of the Roman road system, such as Italy, France or Britain; but for other regions there has been very little aggregation of Roman road evidence. Few of these regional aggregations have been digitised and even fewer are openly accessible online.

A highly detailed digital version of the entire Empire that aggregates all known evidence of Roman roads simply does not exist. I find this incredible, given the importance of such a resource for Roman Studies and the sheer amount of attention Roman roads have received.

A few digital models for the entire empire do exist, but these are nowhere near representative of what we actually already know about Roman roads (nor do they claim to be). The roads of the Iberian Peninsula in figure 1 offer a striking example for comparing existing digital models. At the top of figure 1 we have theORBIS model, a very useful network representation of the Roman transport system. It was purposefully kept very abstract and low detail because it serves as a tool to study the overall shape of movement through the Roman world. In the middle of figure 1 we can see the much more detailed spatial tracks of Roman roads captured in the Empire-wide road network available from the Ancient World Mapping Centre. This is currently the most detailed empire-wide digital representation of Roman roads. These are digitisations of the canonical atlas of the ancient world (the Barrington Atlas), which maps the roads throughout the entire empire in what seems like very high detail. However, the discrepancy between this source and the amount of detail we get when aggregating published evidence of roads becomes clear from the bottom of figure 1. Not only do we see far more roads (most of them are minor roads), but we also notice that the actual spatial tracks of these roads are far more detailed.

fig1
Figure 1 © Tom Brughmans: two empire-wide digital models of Roman roads (top © ORBIS; middle © Ancient World Mapping Centre), but a highly detailed model representative of current knowledge is missing (bottom © MERCATOR-e)

This example at the bottom of figure 1 is the result Dr Pau de Soto’s work in his project MERCATOR-E, where he aggregated available evidence for the Iberian Peninsula. But this kind of work is possible for the entire Roman Empire. The challenge is not to perform the foundational research, it is to digitise and aggregate what is already known.

To support this process, Pau de Soto and I teamed up to develop project Itiner-e (supported by a grant from Pelagios). This is the first gazetteer of ancient roads: a framework where parts of roads can be digitally documented in full detail and uniquely cited, such that this data can be linked with other linked open data.

The work of developing a highly detailed model for the entire empire is underway. In the meantime, we can already road-test some of our research questions with the useful resources from ORBIS or the Ancient World Mapping Centre. For example: it is often said all roads lead to Rome, but which roads get you there faster?

In figure 2, I have used the ORBIS model to explore this question. Every dot is a city in the Roman Empire, and the lines indicate the ability to move from one city to another over Roman transport links. Grey lines are roads, green lines are navigable rivers, and red lines are sea connections.

The size and colour of the dots represents how close each city is to Rome over the transport system; the larger and darker, the further away from Rome. This is achieved by calculating the fastest route over this network from every city to Rome: a GPS or Google Maps function for the Roman route map.

This geographical representation of the transport network reveals some interesting features. We can see an obvious general trend that the closeness to Rome decreases with as-the-crow-flies distance, even though we used network distance to calculate these results. We notice that much of present-day Tunisia, the region of ancient Carthage, is relatively close to Rome thanks to efficient maritime links. We can also see that the farthest western cities on the British Isles are still closer in network distance than the farthest cities along the Nile, the Black Sea and in Mesopotamia.

fig2
Figure 2 © Tom Brughmans: Geographical representation of ORBIS transport model (© ORBIS). Node size and colour represent increasing physical distance over the network away from Rome. Edge colours represent edge type: red = sea, green = river, grey = road. Background © Openstreetmap.

Figure 3 represents this same network in a different way: we have thrown away all geographical locations and the map, and just positioned each dot based on how it is connected to all other dots (a so-called network topological layout). This alternative visualisation highlights different things. Notice how almost all lines at the centre of the picture are sea routes: this network representation reveals that the maritime connections draw all regions’ road networks together, and that they facilitate fast movement throughout the entire transport system.

This is just an abstract example, which highlights the kinds of general insights about Roman transport we can gain thanks to an Empire-wide model such as ORBIS. A more detailed model would allow us not only to derive such results with more accuracy, but also to better understand the role of particular regions’ road structures in giving rise to the Empire-wide patterns. Creating such a high-detail digital model involves a lot of work aggregating existing sources, but it is an entirely doable task. And clearly, such a valuable resource is worth pursuing, which I aim to do over the coming years at UrbNet.

fig3
Figure 3 © Tom Brughmans: Topological representation of ORBIS transport model (© ORBIS). See figure 2 for legend.

Relevant references:

Carreras, C. & P. De Soto. 2013. The roman transport network: A precedent for the integration of the European mobility Historical Methods 46: 117–33.

Scheidel, W. 2014. The Shape of the Roman World: modelling imperial connectivity Journal of Roman Archaeology 27: 7–32. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2242325.

Talbert, R.J.A. 2000. Barrington Atlas of the Greek and Roman World. Princeton: Princeton University Press.

Madness part 2: processes of emerging inter-visibility

The second post in the madness series, describing the run-up to my PhD submission! Last time I wrote about why visibility networks might be an interesting method in archaeology. There was a hidden agenda in that post however: I am not just interested in visualising a visibility network, that has been done before by many archaeologists. My main interest is in understanding the decisions that went into the establishment of lines of sight. That is, the processes that led to the visibility network I study. This might sound rather ambitious, since many factors influenced the selection of the settlement locations I study in my PhD, and visibility networks are merely one factor derived from our limited knowledge of past settlement patterns. However, I argue it is necessary to understand such processes. Mainly because when archaeologists formulate assumptions about how lines of sight affected past human behaviour, these assumptions imply a sequence of events rather than a static state. Therefore, a method is needed that allows one to test the assumed processes, and I have some ideas on how to go about this 🙂

Visibility network between  Iron Age and Roman settlement in Southern Spain
Visibility network between Iron Age and Roman settlements in Southern Spain

Network representations of archaeological data are often used as static snapshots conflating an ever-changing dynamic past. By performing an exploratory network analysis we get an idea of their structure during a given period of time. Such an approach can be considered a type of exploratory data analysis. However, archaeologists use these data networks as representations of past phenomena. It is these past phenomena that archaeologists are ultimately interested in understanding, and most of past phenomena were not static but involved change through time. It is entirely plausible that at an earlier or later stage in time a given network would have had a different structure.

A commonly used technique for archaeologists to overcome this problem is to formulate theoretical assumptions about how the emergence or disappearance of a relationship between pairs of nodes in their data networks affected the change of past networks over time (from here-on referred to as dependence assumptions). Such dependence assumptions are frequently accompanied by (explicitly formulated or implied) expectations of the kinds of network patterns they give rise to. In other words, archaeologists frequently make theoretical statements about dynamic processes that cause change in past phenomena, and how these are represented in networks of archaeological data. Nevertheless, we rarely evaluate whether processes guided by our dependence assumptions can actually give rise to the networks we study, nor do we consider the effect multiple dependence assumptions can have on each other in such processes. Instead, archaeological network analysts have relied on the identification of the expected patterns in an observed network’s static structure when discussing the social processes that caused a network to change from one state to another.

The study of visibility networks in archaeology serves as a particularly good example of this problem. Archaeologists have used visibility networks as a method for studying the role particular visibility network patterns could have in structuring past human behaviour, for example through communication networks using fire or smoke signalling, or the visual control settlements exercise over surrounding settlements. Formulating dependence assumptions for visibility networks implies a sequence of events where new lines of sight will be established as a reaction to pre-existing lines of sight. For example, if we observe that a settlement is positioned in a visually prominent location from where many other settlements can be seen then we might formulate the hypothesis that this location was intentionally selected to enhance communication with or visual control over neighbouring settlements. A further example: if an effective signalling network was considered during settlement location selection then settlement locations inter-visible with other settlements would have been preferred. However, archaeological network analysts have so far studied these processes exclusively through an analysis of static network representations. By pointing out the patterns of interest, an exploratory network analysis can only take us so far to evaluate our dependence assumptions, leaving hypotheses surrounding the intentional creation of visibility patterns untested. A good example of this is Tilley’s (1994) study of a network of inter-visibility between barrows on Cranborne Chase, in which an observed network pattern is interpreted as the intentionally established outcome of an untested process: “One explanation for this pattern might be that sites that were particularly important in the prehistoric landscape and highly visible ‘attracted’ other barrows through time, and sites built later elsewhere were deliberately sited so as to be intervisible with one or more other barrows. In this manner the construction of barrows on Cranborne Chase gradually created a series of visual pathways and nodal points in the landscape” (Tilley 1994, 159).

Very few visibility studies have explored hypotheses about such processes explicitly (see Swanson 2003 for a notable exception). In my case study, however, the decisions to establish certain patterns of visibility among urban settlements are the focus of attention. Most crucially, I will try to evaluate to what degree this changed through time. The approach taken here is experimental. It will initially focus exclusively on the patterns of inter-visibility between settlements, exploring their observed structure as a static snapshot, and then addressing the following hypothetical question: if the visibility patterning that we have observed was the only reason for selecting the locations of sites, what then would be the process that is most likely to have led to the observed patterning? This question will be evaluated through a statistical approach that models the creation of visibility patterns in abstract space (i.e. by simulating the creation of points and lines without taking the landscape’s topography into account as a constraint). Finally, the results of this exploratory network analysis and statistical simulation approach will be re-contextualised within a wider archaeological discussion to shed light on aspects of the changing interactions between urban settlements in the study area through time, as reflected through visibility patterns.

Next time I will introduce the archaeology of this study area and show you some actual results 🙂

As always, I very much welcome your comments. They are very valuable to me in these last stages of my PhD.

References:
Swanson, S. (2003). Documenting prehistoric communication networks: A case study in the Paquimé polity. American antiquity, 68(4), 753–767.

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