Presentation HESTIA colloquium Oxford 2 July online

On 2 July I gave a presentation at the HESTIA colloquium in Oxford. Project HESTIA aims to explore the perception of space as it is reflected in Herodotus’ Histories, through novel digital approaches including network analysis. Read more on the project here.
You can find my presentation slides on the bibliography page. Here is the abstract:


Understanding Roman table ware distributions in the Mediterranean: an exploratory and confirmatory network analysis of the ICRATES database
Roman table ware distributions are traditionally explored through their presence in specific places and visualised as dots on a map. As such they seem to represent distinct entities that do not relate, other than in their relative proximity. This paper challenges an exclusively geographical perspective by proposing a networks approach for exploring ceramic distributions. It states that it is equally informing to explore the dynamics between physical and relational space. There can be no doubt that places and people in the past were connected to each other, and this paper will explore to what extent this connectivity is reflected in the relationships between ceramic data. In order to understand the nature of this connectivity it is necessary to explore the structure of pottery distributions.
This paper aims at addressing the following issues:
To what extent can the relationships between table ware sherds inform us of processes that led to their distribution as we know it?
How can topological and geographical networks complement each other in understanding such processes?
The ICRATES database of table wares from the Roman East (Prof. Jeroen Poblome, Katholieke Universiteit Leuven), containing exhaustive information on over 20,000 published sherds, will allow for these issues to be tested. Firstly, this paper will illustrate how analysing ceramic distributions as networks of interactions can help to identify the general structure and local patterns in a complex dataset. Secondly, the potential of network analysis for testing a geographical hypothesis will be evaluated. The results of both types of analyses will be confronted to validate the geographical hypothesis with ceramic data and to explain some of the patterns that emerged from the topological approach. As such, this paper aims to start discussions on comparing archaeological and historical networks generated from different data types.

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Presentation Southampton 18 March

On 18 March I will present my recent work on archaeological network analysis at the University of Southampton in the Computer Applications in Archaeology seminar series. Read the announcement here. This discussion session will focus on understanding pottery distributions by using network analysis, using the ICRATES database of table ware sherds from the Roman East. I will present networks of co-present ceramic forms on sites, and I will test a geographical hypothesis on Mediterranean trade routes using distance-based networks. Moreover, I will reveal a completely new network type that draws on the results of the previous two and emphasises the chronological aspect. This third network type will explore the gradual adoption of table wares in sites and look for factors that influenced this adoption.
You can read up on these network types in my dissertation, available from the bibliography page. The presentation itself will be available next week.
For those in the vicinity of Southampton on 18 March: its in the archaeology department from 12 to 1

CAA UK

On Saturday 20 February I will present a paper at Computer Applications in Archaeology UK chapter conference at University College London. The talk is based on a paper that will appear in one of the forthcoming issues of Oxford Journal of Archaeology (the pre-published version is available on the bibliography page). My aim will be to convince the audience that current archaeological applications of network analysis are based on an incomplete and sometimes uncritical adoption of network principles from other disciplines, and that the need exists to work towards an explicitly archaeological network analysis. An abstract is included below.

Do have a look at the CAA UK website, I’m very much looking forward to all the other talks.

ABSTRACT

In recent years network analysis has been applied in archaeological research to examine the structure of archaeological relationships of whatever sort. A first generation of archaeological applications of network analysis has succeeded in providing an innovative view of long discussed issues by stressing the importance of exploring relationships between objects/people/data directly. However, these archaeological applications share a number of issues concerning:

  • the role of archaeological data in networks
  • the diversity of network structures, their consequences and their interpretation
  • the critical use of quantitative tools
  • the influence of other disciplines, especially sociology

This paper concerns a deconstruction of past archaeological methods for examining networks. Through a case-study of Roman table wares in the Eastern Mediterranean, it will highlight a number of issues with network analysis as a method for archaeology. It urges caution with the uncritical application of network analysis methods developed in other disciplines and applied to archaeology. However, it stresses the potential benefits of network analysis for the archaeological discipline and acknowledges the need for developing a specifically archaeological network analysis, which should be based on relational thinking and can be expanded with an archaeological toolset for quantitative analysis.

MSc dissertation finished

My dissertation concerning the archaeological application of network analysis is finally finished. In this post you will find an abstract of the completed work. The project is far from over though. I will continue exploring and writing on archaeological network analysis through a number of different projects. So stay in touch and feel free to contact me if you have questions or if you are interested to collaborate.
The full dissertation is available on Scribd, and is embedded at the bottom of this post.
The project’s results can be seen on the project’s website.

ABSTRACT
New and continually evolving digital technologies allow archaeologists to study ever larger volumes of information to formulate and support their interpretations of the past. A downside to this trend, however, is that the accumulation of archaeological data from different sources often leads to heterogeneous and complex datasets. Archaeologists should be aware that the data they combine results from a series of decisions taken in different stages of the object’s life cycle (e.g. initial distribution, re-use) as well as after their deposition (e.g. site selection, publication). Given the wide range of processes that lead to the creation of large and complex archaeological datasets, initial data exploration is invaluable. We believe that these processes are reflected in the relationships between archaeological data. It is our aim to develop a method for exploring these relationships, in order to understand the complexity of archaeological datasets. It is argued that network analysis can serve this purpose. To test this method, it will be applied to a large and complex database of tablewares from the Roman East. Firstly, it will be illustrated how analyzing archaeological data as networks of meaningful interactions can help to identify the general structure and local patterns in a complex dataset. Secondly, the potential of network analysis for testing a geographical hypothesis will be evaluated.

Method update: beta-skeletons

This second update of the project’s method concerns the distance networks based on beta-skeletons described in an earlier blog post. We mentioned that the reconstruction of ancient trade routes is extremely complex as a number of variables should be taken into account, so our best bet is to focus on one parameter that might have been influential in determining trade routes. Using beta-skeletons and graph theory we will investigate whether the distance between centre of production and site of deposition is reflected in the ceramic evidence and whether it significantly influenced the selection of trade routes.

Although we mentioned in a previous post that the beta-skeleton would be compared with a reconstruction of trade routes based on the shortest path for every sherd from centre of production to site of deposition over this beta-skeleton, we now have to confess that this is nonsense as we would compare the beta-skeleton with a slightly altered version of itself that is based on a large number of assumptions concerning the intermediary sites. We realized that these shortest paths actually contain the hypothesis that we are testing, as they represent trade routes based on the ceramic evidence in which distance surpasses all other factors in importance.

To create such a network of trade routes we will make a beta-skeleton in which every site has at least one connection, so that all of them would be reachable. This will be done in ArcGIS with a beta-skeleton calculator programmed by dr. Graeme Earl, applied to all the sites in the database and their geographical coordinates. For every sherd the shortest path in geographical distance from centre of production to centre of deposition over this beta-skeleton will be calculated in pajek (although this can be done in ArcGIS, pajek is able to calculate geographical as well as graph theoretical shortest paths). Edge value will represent the number of sherds passing between two sites and edges with a value of zero will be discarded.

At this point we have a reconstruction of the trade routes over which the vessels would have been transported if the distance between start and ending point would have been the only factor taken into consideration by their transporters. This network embodies the hypothesis we want to test, which can be done by comparing it to another network visualisation of ceramic evidence. The networks of co-presence described in the previous post will provide this basis for comparison, as they do not contain any assumptions of their own (before their analysis that is).

Now, there is an obvious danger of comparing things with different meanings, so we need to be very clear of what aspects of both networks will be used for comparison. We will focus on a couple of phenomena that we think are represented in both types of networks: bridges and centrality.

A bridge is a line whose removal increases the number of components in the network (de Nooy et.al. 2005: 140). In our networks of co-presence a bridge is a site that forms the connection between two different groups of distribution networks. Such a site should play an important role in dispersing information on the pottery market as it is linked in with highly differing networks, but does not necessarily play a central role in the entire network. On the distance network these sites should play a similar role in connecting different distribution networks, in order for the hypothesis to be valid.

Sites belonging to the centre of a pottery distribution network can be easily reached by new pottery forms from diverse producing centres, they are central to the communications network of the pottery trade as it is represented in the ceramic evidence. This is true for both our shortest path network and our co-presence network, and can be measured using the closeness centrality method: sites are central in distribution networks if their graph theoretical distance to all other sites is minimal. In network terms: the closeness centrality of a vertex is the number of other vertices divided by the sum of all distances between the vertex and all others (de Nooy et.al. 2005: 127). Although this method will provide comparable numerical results (a score between 1 and 0), we will not compare these absolute values. Rather, we will focus on seeing whether sites that are central (or not) in our co-presence network are also central (or not) in our shortest path network.

Pairs of contemporary networks of both types will be compared using these methods in order to provide an answer to our hypothesis “was distance a significant factor in selecting trade routes?”

Method update : co-present forms and wares

In a previous post we described how a network analysis of co-present forms and wares might help us understand the distributions evidenced by the ceramic data. Here we will elaborate on this type of network by explaining how we will create the network, what it represents, how we are planning on analysing it and what the results of our analyses actually mean.
At the basis of our analysis lies a two-mode network: a network in which vertices are divided into two sets, and vertices can only be related to vertices in the other set (de Nooy et.al. 2005: 103). In human language, sites are connected with forms/wares that are present on the sites, and the forms/wares are themselves connected to other sites on which they were found. A fictitious example of a two-mode network is given in figure 1. A major benefit of using two-mode networks is that we do not lose any information present in the dataset, the specific forms and numbers of sherds present in specific sites are represented in all their complexity. The data will be extracted from the project’s database to form such two-mode networks.

Two-mode network

Fig. 1: A fictitious two-mode network representing sites connected to pottery forms which are present on the site. The value indicates the number of sherds of a form that have been found. (click to enlarge)

To facilitate the analysis of the data, however, we need to transform this two-mode network into two distinct one-mode networks. This is done for the example network of figure 1 and represented in figures 2 and 3. Both one-mode networks provide us with a different type of information: the first one (Fig. 2) represents the sites as vertices connected by the number of forms that are present on both sites; the second one (Fig. 3) represents the forms as vertices connected by the number of sites on which both forms are present. The strengths of a visualisation of ceramic distributions as networks should already be apparent in these one-mode networks.

One-mode network 1

Fig. 2: A fictitious one-mode network representing sites connected to sites which have evidence of the same pottery forms (co-presence). The value indicates the number of pottery forms that are co-present. (click to enlarge)

One-mode network 2

Fig. 3: A fictitious one-mode network representing pottery forms connected to other pottery forms which have been found on the same site (co-presence). The value indicates the number of sites on which both forms are co-present. (click to enlarge)

Now, what do these networks actually mean? As it is our goal to shed light on the relationship between ceramics and the dynamics of Roman trade, we should be very critical and clear about this point. We state that when sites have evidence of a specific pottery form in common, they have a connection of some sort. The nature of this connection represents, in its broadest sense, the distribution network of a pottery form. What network analysis allows us to do is to analyse the structure of these distribution networks, which will help us understand the processes that reach, maintain and evolve these structures.
A first step in our attempt at understanding the structure of Roman ceramic distributions lies in identifying strong components using m-slices (de Nooy et.al. 2005: 109-113) : we will look for vertices which are strongly connected to each other and have high edge values (ie. number of sites or co-present forms). For the first one-mode network (Fig. 2) such a strong component will contain sites that are all part of the distribution networks of a variety of pottery forms. In this fictitious example Athens, Rhodes and Sparta all have evidence of the same two pottery forms (EAA1 and EAA2), which might lead us to conclude that similar processes led to the deposition of these specific sherds on these sites. For the second one-mode network (Fig. 3) the strong components indicate pottery forms that are present in the same sites and, therefore, have a similar distribution pattern.
Such an analysis might considerably improve our understanding of ceramic distributions as it allows us to answer questions such as: What pottery forms had a similar distribution? Can this be explained by the proximity of the producing centre to the consuming sites? Is there a significant difference in the distribution of pottery forms made from the same ceramic ware group (ie. the same producing region)? Is there a similarity between distribution patterns of forms from different wares (which might indicate similar processes of distribution for different producing centres)?
Apart from identifying clusters of sites that form part of similar distribution networks and pottery forms that had a comparable distribution, we can examine the position of individual sites in these networks. When we restrict our attention to the connections in the networks, we get an impression of the diversity of trade relations. Every edge represents the membership of a site or pottery form to a distribution network. Vertices with many edges have access to many and diverse distribution networks, which might indicate better knowledge of trade patterns or a stronger position in pottery trade, as more information on pottery distribution networks is at their disposal. Such aspects can be studied by focusing solely on the number of absolute or relative edges, using methods to define degree, K-cores, closeness, betweenness, bridges and week ties. Although we can’t elaborate on their exact application here, these measurements help us understand the position and roles of sites and pottery forms in different distribution networks. We might be able to identify sites which played a dominant or regulating role in the distribution of specific pottery forms or wares. We would like to stress that identifying such sites is crucial in any attempt to reconstruct trade routes, as they might serve to fill in the gaps on a transportation route from producing centres to consuming centres.
Another strength of our approach will lie in the analysis of networks from different time periods, allowing for the evolution of distribution patterns to become apparent, and threshold periods to be identified.
This type of networks will form the basis for a comparison with contemporary shortest-path networks, described in the next method update.
The analysis of the structure of the distribution patterns as they are represented in the co-presence networks will be studied in more detail using hierarchical clustering based on dissimilarity measurements. This refinement of our method will be described in a later blog post.

Relative Neighbourhood graphs and Beta-skeletons

Although our preliminary method indicates that a reconstruction of pottery trade flows involves a lot of complications, we cannot seem to let this research topic go. One reason for this is that most archaeological attempts to study the ancient economy make interpretations about trade routes based on ceramic evidence (e.g. Abadie-Reynal 1989 ; Fulford 1989), yet none have ever attempted a networks approach. In this post we will discuss a geographical network in which distance is a significant parameter, an assumption that is not without its complications.

We believe that relative neighbourhood graphs (RNG) and Beta-skeletons might prove to be useful tools for constructing distance-based networks. Unlike other types of cluster analysis (e.g. nearest neighbour) these methods take the position of all points in account. Jiménez and Chapman (2002); discussed the archaeological application of RNG, and summarize its construction as a graph in which “the link between two points is determined by taking into account not only the proximity between the two points, but also the relative distance of each pair to the remaining points ». Lines are drawn between two neighboring points that have no other points in a region around them. By varying the size (beta) of the region of influence for each pair of points, graphs (called Beta-skeletons) can be created with different levels of connectivity: if the region is small, more relationships will be drawn between the points; if the region is large, the network will start to fall apart in smaller networks (see Fig. 1).

beta-skeletons example
Fig. 1 Beta-skeletons with varying regions of influence, indicating that for a higher value of beta the network will start to fall apart. Taken from Jiménez & Chapman 2002.

Of particular interest for our study is a Beta-skeleton of sites in the Eastern Mediterranean at the stage just before it starts to fall apart, so without any unconnected sub-networks (similar to the network for ‘Beta=2’ in Fig. 1). This Beta-skeleton can be analysed as a network, which will allow us to define the relative position of every site for the hypothesis “what if straight-line distance were a determining factor in the distribution of table wares?”

Such a network obviously avoids all complications but is invaluable in testing a distance-based hypothesis. For every ware in every period the number of sherds being transported from centre of production to centre of consumption can be plotted on such a Beta-skeleton (only including those sites in which the ceramics in question were found). We can easily compare the relative positions of sites in these transportation networks, as we know the influence of our basic ‘distance’ network.

To test our hypothesis that proximity is an important parameter in the distribution of table wares, we have to analyse our ceramic networks and compare them to our basic networks. If the relative position of sites weighted by the ceramic evidence is similar to sites in a ‘distance network’, we can conclude that distance played an important role in determining trade relations and thus trade routes. If there is a significant difference between ceramic and distance networks, we can conclude that distribution was influenced by other parameters, e.g. personal contacts of traders and land owners. Testing the hypothesis for 15-year periods will allow us to identify periods in which distance was more likely to be a determining factor than others.

Some of the numerous issues with this method should be listed:

• although RNG is a formidable method for cluster analysis, it still does not take into account any of the complexities that determine trade routes. Could this method be combined with a cost-surface analysis to paint a more accurate picture of regional overland trade?

• Will the ceramic evidence influence the distance network to such a degree that its basic connectivity can be altered?

• Using a Beta-skeleton as the basis for testing our hypothesis might lead us to find exactly what we were looking for (distance = significant) because it is inherent in the network. Should the Beta-skeleton be compared with a more neutral network of ceramic distribution through space?

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