Digital Medievalist (2008). ISSN: 1715-0736.
© Leif Isaksen, 2008. Creative Commons Attribution-NonCommercial licence

The application of network analysis to ancient transport geography: A case study of Roman Baetica

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Peer-Reviewed Article

Accepting Editor: Arianna Ciula, King's College London.
Recommending Reader: Andrew Bevan, University College London.
Received: March 3, 2007
Revised: October 5, 2007
Published: March 21, 2008

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Abstract

In many ways the Roman province of Baetica is an ideal subject for exploring new approaches to historic transport geography. This is not due to the completeness of its record (for it is not), but because it provides a remarkable breadth of pertinent data. This paper, loosely based on a seminar hosted by the Digital Classicist at King’s College London, will briefly discuss the results of applying some as-yet relatively uncommon techniques to the archaeology and documentary record of transport in the area. It will then go on to tackle some more general issues in creating maps of movement in the past, concluding that there is still much theoretical work to be done, but that the potential for discovering new patterns in old data is great, and indeed, ever growing. The main concept that will be explored is that of a Node Network, an abstract model of the interactions between spatially separate locations. This paper demonstrates the potential of a standard relational database, coupled with a GIS and Network Analysis software package, to make a spatial argument about the relative importance of key towns within a transport network and expose the constituent elements of that argument in a formal, visual manner.

Keywords: Network Analysis; Transport Geography; Topography; Roman Baetica.


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Introduction

§ 1    In many ways the Roman province of Baetica is an ideal subject for exploring new approaches to historic transport geography. This is not due to the completeness of its record (for it is not), but because it provides a remarkable breadth of pertinent data (Sillières 1990, 9-16). This paper, loosely based on a seminar hosted by the Digital Classicist at King’s College London, will briefly discuss the results of applying some as-yet relatively uncommon techniques to the archaeology and documentary record of transport in the area. It will then go on to tackle some more general issues in creating maps of movement in the past, concluding that there is still much theoretical work to be done, but that the potential for discovering new patterns in old data is great, and indeed, ever growing.

§ 2    The main concept that will be explored is that of a Node Network, an abstract model of the interactions between spatially separate locations. This paper demonstrates the potential of a standard relational database, coupled with a GIS and Network Analysis software package, to make a spatial argument about the relative importance of key towns within a transport network and expose the constituent elements of that argument in a formal, visual manner. Before describing the results, however, it will be worthwhile to discuss here some contextual issues that have determined the techniques and technologies adopted.

§ 3    First, it is important to distinguish between transport networks and day-to-day travel. The great majority of human motion takes place in an extremely complex and essentially unpredictable fashion. Modern anthropological studies, however, suggest that across virtually all sedentary cultures it is a) limited to approximately one hour a day, and b) there is tendency to return to a place of residence each day (Ausubel & Marchetti 2001, 20-22). This places a hypothetical ‘upper limit’ on the distance most individuals will travel which we might loosely describe as intra-site (by which is meant the ‘home’ location and its immediate environs). Indeed, it is one of the restraining factors on the growth of ancient cities, which, despite occasionally being able to marshal vast resources and being densely populated, never grew more than a few kilometres in diameter (Ausubel & Marchetti 2001, 20-22). Based on these facts we shall here postulate that most inter-urban transport takes place only under special circumstances that require it (Turton & Black 1998, 159-60). It is the routes used in this type of long-distance transport, and the specific conditions that gave rise to them in Roman Baetica, that will form the main subject of this report.

§ 4    The primary source of information that we can draw upon in identifying such routes are the Roman itineraries. Looking beyond the raw lists of data that these documents provide (which we shall address fully later), we should remark upon two further aspects of them. First of all, they imply intentionality. In each case, their authors are suggesting that under rationale x, it is preferable to travel from A to B by way of C. If we are able to understand this rationale then we can begin to make sense of the way that people moved around the ancient world. That said, in the majority of cases we can currently only hypothesize as to just what x might be. Secondly, they are also vectors, having a beginning and an end. We might very reasonably surmise that the journeys described could just as well be undertaken in the opposite direction, but this asymmetry is not something we should entirely lose sight of. As we shall see, it may give us a crucial clue into the manner in which these ancient travel guides were created, and hence their strengths and weaknesses as descriptions of the network.

§ 5    The chronological development of the system is a further area in which our evidence is sparse. It is very difficult to distinguish, from a transport perspective, between the Tartessian, Phoenician, Punic, republican and imperial periods, yet surprisingly this is not as problematic as might be thought. The sources upon which this study was based are entirely Roman (or post-Roman) so they reflect the reality of the classical era (though sadly shed little light on pre-Roman development). We should nonetheless expect, as with most transport infrastructure throughout history, that it was a response to, and often a reinforcement of, the systems of communication which had hitherto served the area (White & Senior 1983, p. 11). As such, whilst this study is concerned specifically with Roman route networks (a period itself spanning no less than six centuries), many of the routes we shall be discussing may well have been used since the earliest times. It should by no means be assumed that the plains and valleys were a tabula rasa upon which the pattern of Roman hegemony was inscribed.

§ 6    With a handful of exceptions, we have few material remains of transport infrastructure outside of urban settlements. The itineraries however, along with a limited number of well established routes from other sources, will enable us to construct a theoretical network. It is to this sort of data that Network Analysis can apply a powerful set of tools to create metrics showing the relative importance of individual locations and routes within their wider nexus.

Baetica

§ 7    The history of Baetica’s territory is both long and complex. It played host to a number of literate cultures in antiquity, beginning with the Tartessians [1] in the late Bronze Age, and later to Phoenician traders, Punic colonists and finally the Romans themselves. The relative impact of these societies on the area’s inhabitants and their relations with one another varied considerably, but through the keyhole of the classical sources and a wealth of archaeological data we can at least be certain that a sophisticated network of communications was present long before its conquest by the Scipios (Aubert Semmler 2002, 101-8).

§ 8     Baetica itself, a province created from the southerly part of Hispania Ulterior by Augustus in the late first century BC (Keay 1988, p. 49), is roughly commensurate with modern Andalusia (see Figure 1). Its primary importance to Rome, and indeed many of its previous inhabitants, was the valley of the River Baetis (Guadalquivir), with its broad plains and fertile soil, and the Mons Marianus (Sierra Morena) mountain range to its north, rich in precious metals. The regional economy was not merely dictated by its resources however, it was also greatly affected by its topography. The central valley is virtually cut off to the North, South and East by two significant mountain ranges, the Sierra Morena and the Cordillera Sub-Bética (Keay et al. 2000, p. 1). The natural entry and exit point was therefore via the large tidal estuary (the lacus ligustinus) to the West which was exposed not to the Mediterranean, but the Atlantic. It is no coincidence that the first known colonists were the sea-faring Phoenicians, establishing an outpost on the island of Gadir (Cadiz) to trade with the indigenous peoples during the mid-eighth century BC [2] . The Guadalquivir itself is navigable some 200km inland and the colony of Corduba (Cordoba), capital of Hispania Ulterior and later Baetica, was founded at its furthest navigable point.

Figure 1: The Province of Baetica with district boundaries, capitals and principal watercourses The Province of Baetica with district boundaries, capitals and principal watercourses

§ 9    To complement this natural conduit, the province developed a complex network of roads. These are testified to by numerous milestones and a number of bridges throughout the region. They not only linked towns within the interior, but also connected them to neighbouring provinces, often providing guidance and sure footing through tortuous mountain valleys. On a more regional level, they would also have been necessary in enabling wheeled vehicles to transport local produce to central markets, or entrepôts from which they would be shipped to the wider empire.

§ 10    Of particular interest to us is the fact that the Baetican economy centred on exports. In contrast to many other regions whose economic surplus appears to have been derived either from services to the military or limited regional trade, Baetica produced and exported in large volumes. The principle commodities were metals (especially iron, gold, copper, and silver), fish, wheat, wine, and most importantly, olive oil (Ponsich 1998, 171-82). An interesting aspect of these ‘industries’ is that they appear to exhibit the economic phenomena of complementarity [3] . As Ponsich has noted, Baetica’s regions, each with their own micro-climate and geography, are suitable for differing local economies, but also depend on one another, requiring systems of inter-zonal transport (Ponsich 1998, p. 182). Of particular relevance to this discussion is the fact that these distribution systems also relied on multimodal transport. In other words, goods tended to be transferred by a combination of land, river and sea to their final destination, be it in Baetica or the wider empire. These industries therefore provide us with as good a case-study as we could hope for. The question to be resolved is, how can we relate the knowledge of a dynamic, transport-based economy, to our more diffuse understanding of communication routes?

Network Analysis

§ 11    Node Networks, a formal structure used in Graph Theory, are simply a number of entities, called nodes (or vertices), in real or abstract space that are linked together by lines, known as edges (or arcs, if directional). These may represent anything from molecules, to the World Wide Web, to social networks. In this case we are using them to represent route systems. Besides providing a useful and intuitive tool for describing such systems, they are also susceptible to mathematical analysis in order to ascertain the importance of individual nodes within a network.

§ 12    Networks have been used in a variety of ways to understand connectivity in past cultures, and do not of necessity require the use of computer technology (see e.g. Pitts 1965), although modern processing capacity undoubtedly makes analysis a great deal faster and more practical. Notable recent studies in their application to ancient transport have explored the autogeneration of networks based on maritime trade and transport factors in the Bronze Age Aegean (Evans et al 2007), and combining them with Agent-Based Modelling to analyse the Antonine Itineraries (Graham 2006). These approaches vary greatly in the assumptions they make about their base data and the techniques they employ. It is therefore important to remember that Network Analysis per se is simply an umbrella term for the evaluation of any of the numerous properties that can be expressed in Graph Theory. Batty (Batty 2005) provides an interesting overview of several network-based approaches to transport geography and also highlights the challenges arising from its integration with conventional GIS. This paper will only be looking at two network metrics which are known as closeness and betweenness centrality, and will assume bi-directional links (edges).

Closeness centrality

§ 13     Closeness centrality can be stated as the ease with which a node can reach, or be reached by, any other node on the network. It is an index of how easily accessible a node is to all the other nodes in the network and is a value between 0 (inaccessible) and 1 (directly accessible in 1 step by all). Two graphs which demonstrate this most clearly are a simple star graph, in which the central node has a closeness centrality of 1.0, and a cycle graph in which all the nodes will have identical closeness centralities. In a network of vertices and lines, (V,L) the function, cl(v), of the normalized closeness of a vertex, v, is formally defined as (Batagelj 2005):

Where d(v,u) is the shortest path (or geodesic), in terms of nodes traversed, between v and any other node, u. These distances are summed, and this value is then normalized by dividing by the total number of vertices (n), – 1. Normalization is important as it enables us to compare this node’s closeness with that of nodes on other networks.

Betweenness centrality

§ 14     Betweenness centrality is defined as the probability that a node will be passed by traffic travelling along the shortest route between two other nodes on the network. The index indicates, not how easy it is to reach other nodes, but the likelihood of it being en route when taking the shortest path between other vectors. Nodes with high betweenness need not necessarily have a high closeness centrality but they are classically associated with bottlenecks and focal points of systems. Formally, (Batagelj 2005)

Where: v is a node in a network of vertices and lines (V,L),

g u,t is the number of geodesics between each possible pair of notes u, t, and

g u,t(v)is the number of geodesics between each possible pair of nodes u, t that pass through v.

Once again, the value is normalized to a value between 0 and 1, this time also to take into account the fact that geodesics from u to t, and from t to u will both be included in the equation (hence (n-1) (n-2)).

§ 15    Betweenness is the metric that interests us here most because it indicates which nodes have a higher degree of control over the network (Freeman 1977, 35-36). In a transport context, although this is not likely to be in the form of obstructing traffic, such key nodes have the potential to influence the way in which that traffic flows, perhaps in a very concrete fashion, as we shall see. They may also benefit from the increased degree of economic activity that is created by the confluence of separate linear routes (Pitts 1965, p. 15).

The Antonine Itineraries

§ 16    The first network we will look at is a series of linear routes known as the Antonine Itineraries, a list of both land and sea itineraries between towns throughout the empire attributed to an emperor Antoninus. Analysis of the locations recorded in the text suggests a date around the end of the third century. The ‘lost’ regions of Dacia and the Agri Decumates, between the Rhine and Danube, are notably absent, but on the other hand, Constantinople is generally referred to by its pre-Constantine toponym, Byzantium (Salway 2001, p. 39). Their exact function is unclear, though their internal structure suggests that several regional groups have been ‘stitched together’ to create a ‘global’ itinerary list (Salway 2001, p. 43). Despite providing an invaluable catalogue of information there are a number of surprising lacunae, and many routes do not follow the shortest path. Of the 225 routes described, thirteen are directly relevant (wholly or partially) to this study. The values of centrality we are generating are network dependent so it would not make sense to include all of them, but in this case they are short enough to decide on a case by case basis. In fact, a number of them stop either at, or close to, the limits of Baetica. The network generated is shown in Node Network 1.

Figure 2: Node network of towns mentioned in the Antonine Itineraries Node network of towns mentioned in the Antonine Itineraries

§ 17    What do our centrality indices tell us? Here the importance of a network is shown clearly, especially when displayed visually. Although closeness does not vary dramatically between sites, betweenness does, and it is no surprise to find that three of our provincial capitals dominate the graph below.

The Ravenna Cosmography

§ 18    The Ravenna Cosmography written by an anonymous monk of Ravenna, is an attempt to compile a list of the all the towns in the known world at the end of the seventh century. The (corrupt) version we possess is a Latin translation from the Greek that dates from the ninth century. Though claiming to draw on a variety of ancient sources, recent scholarship suggests that it is based principally on the Peutinger Table – a spatial (though abstract) itinerary map of the ancient world dating from the mid-fourth century (Salway 2001, p. 28). Although a later medieval copy of the Peutinger Table has survived, frustratingly the westernmost section is missing, leaving the Ravenna Cosmography as our only guide to its contents. To further complicate matters, analysis of placenames on the map shows that their grammatical declension is not consistent, indicating that it in turn was compiled from a series of written itineraries. These undoubtedly came from several sources as they juxtapose towns destroyed by Vesuvius with those built in the reign of Constantine (Salway 2001, p. 44). An initial mapping creates a surprising result however – there appear to be three separate networks which do not interrelate. We might suppose, however that the compiler has made a mistake, perhaps by lifting sections out of an unknown source that links itineraries together. Almost all of the itineraries lead toward, but then stop prior to, important towns. If, however, we add in the most obvious candidate in each case (following (Sillières 1990 p. 32)), the network connects itself in a much more understandable fashion.

Figure 3: Node network of towns mentioned in the Ravenna Cosmography Node network of towns mentioned in the Ravenna Cosmography

§ 19    There is a noticeable difference to our first network however. Looking at centrality, although Hispalis is once again the key node, the other capitals do not appear in the top 15 at all.

§ 20    In this network the chief axis is between Hispalis and Malaca (if we are right in taking the additional step from Aratispitani to the coast).

A Combined Network

§ 21    The strength of the database system used to generate the graphs is that it enables us to combine multiple networks easily, adding or removing itineraries and nodes as desired. To investigate the entire known transport system we can create a ‘super-network’ composed of the two networks discussed above with three further additions: The Via Augusta - the principle imperial highway which is known from a set of inscribed goblets, the river transport network, and a route of a single day’s journey is known both from miliari and from Aerial photographs between Astigi and Ostippo (Sillières 1990, 506-8).

Figure 4: Node network of towns in all sources Node network of towns in all sources

§ 22    Which nodes are most important for betweenness and closeness? The graph below leaves no doubt:

§ 23    Despite the fact that closeness values still do not vary much, Astigi, Hispalis and Corduba are considerably more important than any of the other towns in terms of betweenness. In other words, if our data can be taken as broadly representative of the primary transport routes in Baetica, they are unquestionably the focal points of the Baetican transport system. But if we are to accept this as an at least partial explanation for the locations of three of the district capitals, then what of Gades? So far, it has maintained fairly low values in all of our networks. If betweenness really plays such an important role, then why was Gades, rather than candidates such as Carteia, which is more central within the district, or Malaca, with better access to the interior, chosen as capital?

§ 24    This is a difficult question to answer, and indeed there may be a number of reasons. It may be as a result of its historic significance and development during the previous Phoenician and Punic periods. It has a deep water port but is also easily defensible from attacks from the mainland, a useful advantage during the civil wars of the republic. It is furthermore the principle harbour in the Bay of Cadiz and provides relatively easy access by boat to the other three capitals. Towns farther east would have to pass through the straits of Gibraltar, a passage which was periodically closed by bad weather. Yet a more convincing explanation might be provided by noting that the model we have been using for our land networks makes no reference to this mode of transport, and incorporating it would be a major challenge. Nonetheless, we can be certain that some patterns existed. Winds, tides and currents all interact with ship technologies in ways that restrict, or encourage the use of, different harbours (Evans 1988, p. 367). That is a topic well beyond the scope of this study, but one that would undoubtedly enlighten, and be enlightened by, Gades’s role within Baetica.

Figure 5: Spatial schematic of itineraries from all sources Spatial schematic of itineraries from all sources

Tabellaria

§ 25    Having looked at some of the structural aspects of the network, we can now turn to the subject matter of the sources themselves, and specifically the tabellaria theory propounded by Benet Salway (Salway 2001, 54-60). The idea, put simply, is that long distance travellers would plan only the key locations of their journey. At each of these locations they would be able to take notes from monumental itinerary lists of the sub-routes (each of a day’s travel) required to reach the next. In this way, geographical knowledge could be provided at a provincial or district level to those who required it. The itineraries that have come down to us, so the theory goes, are compilations of these ‘travellers notes’, perhaps to save a regular traveller the trouble of having to consult these public route-planners on each journey. It might even have been a good way of finding more direct routes that would not be known to an itinerant, planning only from the main nodes. ‘Shortcuts’ are certainly marked in the itineraries. The idea is attractive but the data is ambiguous. First of all, we would need to explain why some of the routes are so tortuous, and secondly, why they appear in some cases to take no account of provincial boundaries at all.

§ 26    Starting with the Antonine Itineraries, there is an interesting correlation, at least within the itineraries considered. Almost all of them [4] lead between district and provincial capitals, and/or ‘provincial frontiers’, which has been summarized in the table below.

Table 1: Start and end points of Antonine Itineraries

Itinerary Type
Gades-Corduba Capital-Capital
Hispalis-Corduba Capital-Capital
Hispalis-Emerita Capital-Capital
Mouth of Anas-Emerita Frontier/Port?-Capital
Hispalis-Italica Capital-?
Corduba-Emerita Capital-Capital
Corduba-Castulo (1) Capital-Frontier
Corduba-Castulo (2) Capital-Frontier
Malaca-Gades Port-Capital
Castulo-Malaca Frontier-Port
Baesuri-Pax Julia Frontier-Capital
Carthago Nova-Castulo Capital-Frontier

§ 27    The correlation is curious as the routes themselves do not necessarily respect provincial divisions, nor do they take the fastest route. We might also note that the number of non-capitals is actually very small – in fact there are only four: Italica (x1), Malaca (x2), Baesuris/Mouth of Anas (x2), and Castulo (x3). In fact, Baesuris and Castulo form the initial/terminal nodes of other itineraries, not included in our network, as well.

§ 28    Turning to the Ravenna Cosmography, we see links between the capitals and frontiers once again:

Table 2: Start and probable end points of Ravenna Cosmography

Itinerary Last Stage? Type
Carthago Nova-Baelo (1) Baesippo? Capital-Port
Carthago Nova-Baelo (2) Baesippo? Capital-Port
Baesippo-Hasta- (last stages missing) ? Port-?
Emerita-Italica Hispalis? Capital-Capital?
Emerita-Carmo Hispalis? Capital-Capital?
Emerita-Castulo Castulo Capital-Frontier
Corduba-Anticaria Malaca? Capital-Port?
Hispalis-Aratispitani Malaca? Capital-Port?
Hispalis-Asido Baessipo/Gades? Capital-Port/Capital?
Hispalis-Seria Pax Julia? Capital-Capital?

§ 29    The patterning is similar to that of the Antonine Itineraries, and with similar ‘frontier-towns’. Malaca features again (x2), as does Castulo (x1). The new case is Baesippo/Baelo, although in two cases this is mentioned within the context of the straits of Gibraltar [5] and the third case is a repetition of the first. We know from the Antonine Itineraries that Baelo seems to have been a port for crossing over to Tingitania which fits the pattern of ‘frontiers’ nicely.

§ 30    What do our previous results suggest about the idea of key itinerary nodes with tabellaria? The evidence seems to be mixed. If the theory is correct, we might expect to observe two sets of phenomena in our data. First, there would be a limited number of departure and arrival points based at strategic locations and they would be highly correlated. The evidence in Baetica does seem to bear this out. Both sets of itineraries use a very similar set of start and end points, and they can all be reasonably interpreted as having strategic importance within a transport network. Secondly, as the hubs within the network, we would probably see a correlation in betweenness and these key nodes. Here the results are more mixed. Clearly, Hispalis and Corduba are important, both as centres on the network and as frequent departure points and destinations within the itineraries. Unfortunately we do not have much to say about the external towns as they are also connected to networks which we have not considered. Likewise, the port towns of Malaca, Gades, Baelo/Baesippo and perhaps Baesuris could all reasonably be seen as parts of wider networks. There is, however, one glaring exception. Astigi, which along with Hispalis and Corduba appears to be in a league of its own with regard to betweenness, does not feature as a terminal node on any of the itineraries. If certain locations are centres of transport information and that is reflected in the itineraries, then Astigi, a district capital, does not appear to be one of them. This is certainly not strong proof against the theory, but it suggests that a broader study would have to be done to give further support, one way or the other.

§ 31    Being able to break the network down by individual itinerary, however, also gives us a clearer idea of how each relates to the other, and perhaps some further clues as to the nature of their sources. For example, some of the itineraries clearly could be described at their origin, taking a fairly direct route. Others take diversions that might also be permissible, especially when they go by way of important towns such Astigi, Acci, or Corduba. There are one or two however that could surely not be described on a public itinerary table. Only the initial stages of the Gades-Corduba Antonine Itinerary could have been described on a tabellarium at its departure point, and it is unlikely that the remainder would even be described at Hispalis. The itinerary from Baesuris to Pax Julia is even more bizarre, circling its goal almost entirely. Such itineraries suggest very specific purposes and must have been created either post factum, or with some other kind of guidance available. As Salway points out, the compilations are probably comprised of itineraries created under various circumstances, and it is certainly possible that these are exceptions to a general rule, but without looking at a larger dataset, the evidence from Baetica is not compelling.

§ 32    Yet the information we now possess may give us a new way of looking at the problem. The general shape of the overall network is surprisingly regular, and this regularity is also seen clearly in the Network Analysis diagrams. In fact it is remarkable how many similarities exist between the Ravenna Cosmography and Antonine Itineraries, and how well they complement each other, especially as the individual itineraries are quite different. Such similarity suggests, though it cannot prove, that the rationales behind them may reflect some wider reality. Including the river network and the extensions of Sillieres, the chief features (with starting points here chosen arbitrarily) seem to be

  • Two central axes running North-South and East-West. The former running Emerita-Celti-Astigi-Antikaria-Malaca. The latter, Castulo-Corduba-Astigi-Hispalis-Baesuris.
  • A separate circuit traces the main extents of the Gudalquivir Valley, running Hispalis-Urso-Antikaria-Corduba-Celti-Italica-Hispalis
  • To the East, a route connects the end nodes of Malaca and Castulo, whilst another (not included in the database as it lies entirely outside of Baetica) joins Cordoba and Emerita. There is also a direct road between Emerita and Corduba, the provincial capitals.
  • To the West, the lacus prevents any direct land route between Malaca and Baesuris, but there is a route to Hispalis. There is also a route from Hispalis to Emerita.

§ 33    If we draw a schematic diagram of the principle route network, and mark on the places in which itineraries begin, an interesting pattern emerges. All of the itineraries begin on the boundaries of Baetica, except for Corduba, the provincial capital. They are particularly common in port towns as well, notably those that are known to have direct connections with other provinces. Whilst it is difficult to understand how they could have been constructed from monumental tabellaria, the idea of travellers taking notes down on arrival at a new province does not seem at all far-fetched. Likewise, we would expect such information to be available at its capital. If the system did work in this way it would provide an explanation for Astigi’s absence from the list of starting nodes, as well as explain its fundamental role within the route system as a whole (it is the central node of the entire network).

Figure 6: Abstract schematic of itineraries with points of departure Abstract schematic of itineraries with points of departure

§ 34    We would be foolish to jump to conclusions on the basis of just one province, but the co-incidence of starting points with clear points of provincial interface is one worthy of further investigation. If we are to use the Roman itineraries then we have to try and understand the rationale behind them. The implication of the study just described is that they may well have been written by visitors to Baetica, rather than the native population. If that is the case, it is no great wonder that the network appears strangely symmetrical whilst the routes meander, for they are plans made by people who have had Baetica explained to them.

Future Directions

§ 35    The foregoing discussion will hopefully have made clear that there is a great deal more potential in the use of Network Analysis than can possibly be looked at within the scope of this paper. On the one hand, it has turned out to some surprisingly strong results. It should not surprise us that the district capitals show a higher level of degree centrality (direct links to other sites) than other cities. Indeed, it would be curious if they did not. It is also not particularly unusual to see fairly even levels of closeness within the network as a whole, as there is a surprising degree of symmetry and it is quite strongly interlinked. What comes as a greater surprise is the variation in betweenness. This index shows to a high degree of probability that the capitals were chosen either as de facto, or intentional hubs within the province.

§ 36    As has been reiterated throughout this discussion however, such results must be approached with caution. The network remains a model founded on data that is both corrupt and incomplete. It does not take distances into account, nor links which are not explicitly stated within the record. It is also based on a subset of data which, though not selected arbitrarily, may conceivably not reflect the ‘real’ social, political and commercial structures of Baetica during the Roman period. Until further work has been done on the robustness of such networks, i.e. the degree of change resulting from the presence or absence of nodes and links, then any conclusions drawn are of interest only in so far as clear correlations can be drawn with independent factors (such as the political status of sites). The network model itself cannot be deemed reliable in any of its particulars. But despite all this, they are interesting results. Even if they do not show the complete system of routes used by the resident traders, magistrates and other itinerants, they may well reflect the thoroughfares perceived to be important by external visitors. This may go some way towards explaining a layout which often seems to make more sense from an abstract perspective than it does on the ground.

§ 37    One of the more exciting aspects of the work is that now the framework has been created, it is in fact very simple to extend. The dataset considered here is only a fraction of the itineraries that have come down to us. Both the Antonine Itineraries and the Ravenna Cosmography/Peutinger Table cover almost the entire ancient world, much of the latter in a visual format. There are numerous others, large and small. This is not to suggest that in creating a ‘super-itinerary’ we are mapping the Roman world. The problem with such approaches is that they tend to consolidate all the information into a unified network. But by introducing such data into a transparent framework we are able to see much more clearly the different sources, the starts and finishes, the virtually-certain, and the wild-speculation. In this way we may be able to glean some better understanding of the way itineraries worked and how they were used by travellers, rather than chasing the chimera of a complete knowledge of Roman transport structures.

§ 38    A further direction to explore is the incorporation of distance into the equation but there are difficulties to be overcome – not least of which is what distances to use as the concept of distance requires some unpacking. Some progress has been made towards this in the application of ‘cost-distances’ which factor in the relative difficulties in crossing different kinds of terrain. Whilst this is clearly an improvement on utilizing simple Euclidean distances, it is still an inadequate measure of transport cost. First, transport is above all a value-based activity. It involves a playoff between the advantage gained and the cost (or ‘friction’) involved. This friction is better understood as an equation involving the two coefficients of time and effort/expense. The value of either may vary in both absolute and relative terms but no-one moves things if it’s not worth it.

§ 39    Secondly, transport friction is not a single conceptual entity but composed of (at least) the following factors:

  • Space. The actual distance involved.
  • Environment. The natural surface over which transport takes place, affected by both physical and socio-cultural barriers (e.g. mountains and borders).
  • Static technology. Infrastructure such as roads or jetties.
  • Dynamic technology. Planes, trains and automobiles, or in our case, ships, barges, oxen, etc.
  • Systems. The individual and collective behaviours through which the technologies are utilized. These include the abilities of the populace to use certain technologies, and the laws and norms that dictate how they do so.
  • Load. The volume and nature of passengers and cargo (and dynamic technology) transported.

§ 40    The challenge in modelling transport is that there is, and can be, no natural ‘friction’ for any point of space, nor any fixed ‘price’ for any mode of transport. Whilst all transport has a ‘cost’ it is always a unique calculation. Though it is possible to talk about ‘average costs’, they may bear no relation to actual realities of particular cases. What we need to look for is the economic rationale behind the activity. The Grand Tours of the eighteenth century make no financial sense, but socially, and hence politically and economically, they do. In contrast, many early Roman military campaigns (of which transport logistics were a key component) were of greater financial than military benefit to their planners [6] . In short, there can be no common algorithm suitable for calculating transport costs or benefits in all circumstances.

§ 41    Thirdly, the ubiquity and aggregative cost of transport makes it conducive to economies of scale. Transport technology, both static and dynamic, does not change frequently because the cost would be prohibitive and returns are only made in the long run. This is advantageous to the archaeologist, for transport networks have a heavy historical bias which will often be based on a wide range of interests, including many which are long since past. When considering such ‘legacy systems’, however, we must be careful to differentiate between the users and investors. Most infrastructure reflects the agenda of those wealthy and/or powerful enough to create or influence it and it will not necessarily represent the preferences of those who use it - it may just be the best means available for their requirements.

§ 42    If we want to try and understand transport as anything more than historic contingencies of landscape we need to try and understand the forces at work behind it. Specifically we need to ask who was involved in the transport process, what their primary concerns were, and what obstacles and resources surrounded them. The inclusion of physical distances is just a single, if important, aspect of this.

Acknowledgments

The research presented was undertaken as part of a Master’s dissertation under the aegis of the Urban Connectivity in Iron Age and Roman Southern Spain Project, an AHRC-funded project based at Southampton University [7] that aims to “analyze changing social, economic and geographical relationships between towns and nucleated settlements in southern Spain…between c.500 BC and AD 500.” [8] The assistance of Prof. Simon Keay, Dr. Graeme Earl and Dr. David Wheatley is gratefully acknowledged. The complete thesis and a copy of the dataset can be obtained from the author.

Notes:

[1] . On Tartessian literacy, see Strabo 3.1.6 (Jones 1969).

[2] . Ruiz Mata 2002 p. 158 (for the traditional account see Strabo 3.5.5)

[3] . A concept first expressed in Ullman 1956.

[4] . The exception being the curious, single-step route from Hispalis to Italica

[5] . “Item super fretum Septem sunt civitates, id est. Bepsipon…” (Ann. Rav. 305-6)

[6] . See, for example, the behavior of the consul L. Lucullus in Appian, Iberike 51-55

[7] . In collaboration with the Seville Office of Culture and the Department of Prehistory at Seville University

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