brughmans t. 2009 connecting the dots: exploring complex archaeological datasets with network...

Department of Archaeology

Connecting the Dots: Exploring Complex Archaeological

Datasets with Network Analysis

Case Study: Tableware Trade in the Roman East

By

Tom Brughmans

A dissertation submitted in partial fulfilment of the requirements for MSc Archaeological

Computing: Spatial Technologies by instructional course

September 2009

ACKNOWLEDGEMENTS

When I first started planning the continuation of my education in Southampton, I was shocked by the level of bureaucracy and the sheer costs involved. Strangely, this proved to be a challenge that only made me want it even more. However, I quickly realized some support was necessary, so I tried every option available to help me get to Southampton and make my stay there an unforgettable experience. I am therefore extremely grateful for the generous financial support provided by Rotary District 2170 and the Fernand Lazard Foundation. In particular Rotary club Antwerp-Metropolis for supporting my application and Michel Looyens for his personal guidance and belief in my abilities. On an academic level, my application was supported by Prof. Jeroen Poblome, Prof. Joachim Bretschneider and Prof. Philip van Peer, who patiently and willingly passed through the bureaucratic mill of further education with me.

To my parents: sorry dat ik weg was voor een jaar. Ik zat in Engeland. Het weer en het eten waren slecht. Maar de gedachte aan een liefdevol thuisfront en een prachtige jeugd hielden me overeind. Bedankt!

I arrived at Southampton with the general direction for a dissertation already in mind, as I could work within the exciting framework of the ICRATES project. I would like to thank both Prof. Poblome and Dr. Philip Bes for the freedom they allowed me with their academic child, and the continuous support throughout the last twelve months. A specific research topic developed only slowly. I saw my ideas melt from challenging the entire discipline, to what seemed like pulling out a crucial piece of a house of cards and putting a concrete pillar in its place. This healthy evolution was only possible thanks to the numerous discussions with Leif Isaksen, David Potts, Dr. Graeme Earl, Dr. David Wheatley, Dr. Lucy Blue and Prof. Simon Keay. I especially would like to thank Dr. David Wheatley for his supervision of the project and our entertaining discussions fueled by coffee. My application of Beta-skeletons would not have been possible without a VB program by Dr. Graeme Earl, thanks for patiently walking me through it. The only reason why my dissertation is to some extent readable is because of endless explanations to my classmates who had no clue of what I was doing. Special thanks to Vito, Nick and Nathan for the fun times in the lab when we were supposed to be working. Finally, I would like to thank the many people who read my draft and provided me with extremely useful and necessary feedback: David Potts, Arun Luykx, Dr. Philip Bes, Joeri Theelen, Prof. Jeroen Poblome, Alexander Coudijzer, Dr. David Wheatley and Stefano Costa.

To Annika: twaalf maanden bleek langer dan ik dacht, dit jaar krijgen we nooit meer terug. Maar meer dan ooit kijk ik nu uit naar de vele jaren samen die ons opwachten. Ik hou zielsveel van je.

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.

As this project relies heavily on a large number of complex displays of networks, we decided to

produce a website1 that allows one to explore all networks interactively. Moreover, the project’s

website and blog2 provide summary information on many technical aspects of the networks. We

therefore suggest the reader to consult the project website as it will significantly clarify the

discussions in the text. Links to relevant parts of the website are provided in the text.

1 http://mapserver.arch.soton.ac.uk/networks/ . 2 http://archaeologicalnetworks.wordpress.com/ .

CONTENTS

CHAPTER I INTRODUCTION .......................................................................................................3

1.1. Project goal ................................................................................................................... 4

1.2. The problem: data complexity ...................................................................................... 5

1.3. Research questions ........................................................................................................ 9

CHAPTER II ROMAN POTTERY AS FRAGMENTS OF THE ANCIENT ECONOMY.......................10

2.1. Roman pottery = Roman economy?.................................................................................. 11

2.2. The Roman economy: a battlefield of models................................................................... 12

2.3. Economic processes in pottery structure ........................................................................... 14

CHAPTER III METHODOLOGY.................................................................................................16

3.1. Network analysis............................................................................................................... 17

3.2. Data ................................................................................................................................... 21

3.3. Ceramic networks.............................................................................................................. 24

3.4. Database structure ............................................................................................................. 34

CHAPTER IV METHOD AND ANALYSIS CO-PRESENCE...........................................................36

4.1. Method .............................................................................................................................. 37

4.2. Analysis: co-presence networks ........................................................................................ 43

4.3. Discussion: from structure to processes ............................................................................ 49

CHAPTER V METHOD AND ANALYSIS DISTANCE NETWORKS................................................53

5.1. Method .............................................................................................................................. 54

5.2. Analysis : distance networks ............................................................................................. 57

5.3. Discussion: combined networks of distance ..................................................................... 66

CHAPTER VI TABLEWARE TRADE IN THE ROMAN EAST: 50-25 BC......................................68

6.1. Testing the hypothesis....................................................................................................... 69

6.2. Discussion: the bigger picture ........................................................................................... 75

6.3. Conclusion......................................................................................................................... 79

CHAPTER VII DISCUSSION: ARCHAEOLOGICAL NETWORK ANALYSIS .................................83

7.1. Exploration, visualization and confirmation ..................................................................... 84

7.2. Interpreting complexity ..................................................................................................... 86

CONCLUSION ............................................................................................................................89

BIBLIOGRAPHY.........................................................................................................................90

FIGURES....................................................................................................................................97

GLOSSARY ................................................................................................................................99

APPENDIX A SITES INDEX AND MAP......................................................................................102

APPENDIX B DATA EXPLORATION CHARTS..........................................................................105

APPENDIX C DATABASE STRUCTURE ....................................................................................112

APPENDIX D PROJECT METADATA (DUBLIN CORE) ............................................................113

APPENDIX E NETWORK ANALYSIS APPLICATIONS...............................................................114

APPENDIX E DIGITAL ARCHIVE ............................................................................................116

CHAPTER I

INTRODUCTION

“It is important to understand what you can do before you learn to measure how well you seem to have done it.”

Tukey 1977: v.

TOM BRUGHMANS CHAPTER I INTRODUCTION

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1.1. Project goal

New and continually evolving digital technologies allow archaeologists to study ever larger

volumes of information to formulate and support their interpretations of the past.

Archaeologists are no longer dependant on the projects they are involved in for assembling

their datasets, but can draw upon an ever larger body of literature and digital resources.

However, the aggregation of data from different sources, often leads to very complex and

heterogeneous datasets. As such, the decisions made by archaeologists during data collection

and combination should be understood before interpretations about the past are made.

Selection and accumulation of material obviously also took place in the past, and decisions by

people in the past therefore determine what we find and what eventually ends up in our

datasets.

We believe that archaeological datasets are created by such processes in the ancient past and

academic present, and that these decisions 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.


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1.2. The problem: data complexity

The word “simple” is possibly the worst description of the information archaeologists rely on

for reconstructing the past. This is largely because every aspect of the archaeological process

depends on selection. As we are not directly informed about the past, we have to rely on the

indirect evidence provided by the material remains of human actions. These actions result

from decisions made in the past, and involve processes of cultural and natural selection.

Although it is the main concern of archaeologists to untangle these processes to understand

the actions they reflect, we should not be fooled in believing that this is the only selection that

takes place. Rather than spreading their research efforts equally, archaeologists struggle their

way through academic, administrative and political establishments, until they stumble upon a

site that happens to fit the picture. Based on possible preparatory research and personal aims

and interests, trenches are dug, notes and pictures are taken, archaeological contexts are

created, material remains are considered artifacts, and other material remains are considered

rubbish; all performed with varying attention for detail. After the excavation the information

is often allowed some time to “mature”, after which another round of selection takes place

where it is decided what might be of interest to the people who were not involved in these

decisions. This slightly exaggerated account of the archaeological process clearly indicates

that the information at the archaeologist's disposal results from a series of subsequent

decisions. Even a single pottery sherd or a soil sample can, therefore, not be termed “simple”,

as both cascaded through a complex series of processes into the generic pool we call

archaeological data.

Of course, archaeologists are hardly interested in a single pottery fragment. Indeed, times

where the only archaeology was the study of individual artifacts, revered for their material

beauty and as relics of a deliciously mystique history, have long passed. Ancient objects are

more informative when considered within a certain context: on a site soil contexts can yield

multiple objects whose deposition should be understood in terms of their interaction, while

the soil context in turn relates to surrounding features and structures; an archaeological site

itself is part of a larger spatial context, relating to human and natural features in the

surrounding landscape; the excavation project and its staff are rooted in academic traditions

and are slaves to personal agendas. Considering all these ways in which archaeological

information interacts, and the diversity of processes involved, an image of the archaeological

record as a continually evolving web of dynamic patterns emerges. We can therefore say with


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some certainty that “complexity” is preferable to “simplicity” as a description of

archaeological information.

Although the complexity of the archaeological record is apparent at any scale, the issue

becomes more problematic when examining large datasets. In the 1960’s, methodological

changes in the archaeological discipline led to increasingly large and detailed datasets, and

there is no doubt that we are now witnessing a second data boom, resulting from the digital

and internet revolutions. These days, archaeologists can access published and unpublished

archaeological reports and databases at the click of a button. Moreover, the application of

semantic technologies in archaeology to combine different datasets (Isaksen 2009; Kummer

2009) and the emergence of digital archaeological data warehouse initiatives1 (Inmon 1995;

Kenny & Richards 2005) indicate that assembling large subject-specific datasets will become

increasingly simple. Although we very much welcome any trend promoting data re-use, it

does raise an important issue: assembling data from different sources leads to very complex

sets of often heterogeneous data that cannot be analyzed without question. Archaeologists

should always have a critical attitude towards their data, and acknowledge, as illustrated

above, the diversity of processes that led to its creation. In large and complex datasets,

however, it becomes very hard to take the provenance and limitations of every single drop of

information into account. Given this problem, and as we believe that the analysis of large and

complex datasets is invaluable for making sense of the evolving web of dynamic patterns that

is the archaeological record, we conclude that initial data exploration is crucial.

At this initial stage any assumptions about the data or the results one wants to achieve should

be put aside, as it is the contents and limitations of the assembled dataset itself that needs to

be clarified. In this sense, the relevance of Tukey’s (1977: v) statement cited at the beginning

of this chapter becomes apparent. This principle lies at the basis of Exploratory Data Analysis

(EDA), crystallized in Tukey’s (1977) work of the same name. EDA is a set of techniques for

the detailed study of data, that guides a scholar (often visually) towards structure relatively

quickly and easily (Hoaglin et.al. 1983: 1). Four themes are central to EDA (Cox & Jones

1981: 135-140; Hoaglin et.al. 1983: 2-4):

1. Resistance: insensitivity to local discrepancy in the data. Initially, much attention is

paid to the main body of the data and little to the outliers.

1 E.g. ADS : http://ads.ahds.ac.uk/ ; and ARENA : http://ads.ahds.ac.uk/arena/archindex.cfm .


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2. Residuals: the remainders (after a summary or fitted model has been subtracted from

the data) should be carefully examined.

3. Re-expression: transform the data for easier and more effective description, as to

simplify the analysis of the data.

4. Revelation: the visualization of the data should reveal both the unexpected features

and the familiar regularities.

These themes clearly indicate that EDA is concerned with exploring a dataset in its totality,

often conceptually described as “data = smooth + rough”, with the smooth being the general

pattern in the data and the rough being the deviations from that pattern (Hartwig & Dearing

1979: 10; Shennan 1997: 21). Only after it is clear what the data can tell us, will we be able to

test hypotheses and include assumptions in a confirmatory approach (Hartwig & Dearing

1979: 9-10; Hoaglin et.al. 1983: 2). In light of this short overview, Baxter’s (1994: 220-221)

statement that EDA is an “attitude” that has much to offer archaeologists in the initial stages

of their research can only be confirmed. While the abovementioned trend towards large and

complex datasets urges for the adoption of this attitude in the archaeological discipline, it is as

yet not firmly established in archaeology. Initial data exploration often does not make it to the

final report for practical reasons (exception e.g. e.g. Bell & Croson: 1998), and the major

archaeological quantitative reference works seem to pay only limited attention to exploratory

methods compared to confirmatory techniques (Baxter 1994; Shennan 1997; to a lesser extent

Fletcher & Lock 2005).

But does EDA succeed in exploring the complex web of interactions the archaeological

record consists of? Although its techniques do not ignore any data, they do tend to summarize

it and hide the relationships between the data points. We consider these relationships most

informative and urge for a direct examination of the ways in which they create the image we

have of the archaeological record. The often unintelligible lists of numbers produced by

classic statistical methods cannot help us with this issue. Other methods focus on exploring

the spatial relationships (Anselin 1994; Anselin 1998; Haining et.al. 1998; Shaw & Xin

2003), ignoring any other informative ways in which data could interact (Batty 2005).

Network analysis might provide the techniques to fill this methodological hole, and to bridge

the gap between exploration and confirmation. As we will explain in more detail in chapter

III, network analysis is concerned with visualizing and analyzing the general structure and

local patterns created by dynamic relationships between entities. We will discuss this


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method’s use as an exploratory (chapter IV) as well as a confirmatory tool (chapters V and

VI). To do this, we will turn to the archaeologist’s major source of information: ceramics.

Thanks to its preservation, pottery evidence is abundant in the archaeological record and has

subsequently received the status of being a key to answering all our questions about the past.

Moreover, examining supra-regional patterns in ceramic evidence requires pottery datasets

from very different sources to be combined. In the next chapter we will discuss how drawing

any kind of inferences from pottery sherds is problematic, and how the relationships between

individual sherds can clarify the information pottery carries. A large and complex dataset of

tablewares from the Roman East, assembled in the framework of the ICRATES project2, will

help us develop and test the archaeological application of network analysis.

2 http://www.arts.kuleuven.be/icrates/ .


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1.3. Research questions

We believe that archaeological datasets are created by processes, both in the present and in

the past, that are reflected in the relationships between archaeological data. It is our main goal

to study these relationships in order to understand the complexity of archaeological datasets.

Throughout this project we will address a number of questions that are central to

accomplishing this aim:

• Does network analysis provide a suitable method for identifying structure inherent to complex archaeological datasets?

• Can it enhance our understanding of both past and present processes that led to the creation of such datasets?

• Can the dynamics between geographical and topological structure be understood by analyzing a geographical hypothesis as a topological network?

• In addition to the identification of structure, the method’s part in explaining this structure and the patterns it is made up of should be discussed. Is network analysis an interpretative tool?

We will develop and test our method using a complex pottery dataset to examine how aspects of the Roman economy are reflected in ceramics. Therefore an introduction to current archaeological and historical discussions surrounding this topic and our potential contribution will be discussed in the following chapter. In Chapter III a general overview of our method will be provided, and two network types will be introduced. These network types will be analyzed in chapters IV and V, and in chapter VI they will be compared with each other and a wider interpretative framework. Finally, in Chapter VII the strengths and weaknesses of a networks approach for archaeology will be discussed, and guidelines for using this method in future research will be provided.

CHAPTER II

ROMAN POTTERY AS FRAGMENTS OF THE ANCIENT

ECONOMY

“The knowledge we derive from archaeology is even more certain and explicit than a literary statement; the latter may be challenged, but the vases and jars

are visible and tangible and cannot be disputed. Chance so often yields unexpected treasures that we may hope for further information as the years go on,

long after the last drop of meaning has been squeezed from the words of Varro or Strabo or Pliny.”

Charlesworth 1924: xvii.

TOM BRUGHMANS CHAPTER II ROMAN POTTERY AS FRAGMENTS OF THE ANCIENT ECONOMY

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2.1. Roman pottery = Roman economy?

Thanks to its durability and the enormous quantities of material found in excavations all over

the Eastern Mediterranean, ceramics form a major source of knowledge about the Roman

East. But what is the exact nature of the information pottery carries? We can assume that there

is more to a pot than just the fact that it has been used by someone in the past to serve a

culinary or botanical purpose. Indeed, the frequent discovery of non-local pottery on a site

itself implies that it has been made at a different site for a specific reason, that it was

exchanged and transported along certain routes, it was used for a certain period, and that it

was deposited for whatever reason on the site where it was excavated. This simplified “life

story” of a vessel clearly indicates that every sherd carries information about its cultural,

political and economic context. More than anything else, however, Roman pottery has been

used by archaeologists as proxy evidence for the Roman economy (Fulford 2004: 314; Greene

2005). To some extent this is an understandable approach, as economies are largely shaped by

material constraints that can be examined through the natural sciences (Clark 1952; Trigger

1989: 265). However, Greene (2005: 43) correctly argues that “no direct causal connection

exists between the workings of an economy and the deposition of potsherds on archaeological

sites”. Discussions surrounding the exchange and production of Roman pottery further

emphasize the absence of such a direct connection (Peacock 1982; Peacock & Williams 1986;

Abadie-Reynal 1989: 143; Tomber 1993). We therefore follow Greene (2005: 43) in

approaching Roman pottery evidence as an “indirect analogy” to economic processes in the

past.

To accomplish the aim set out for this project, we will analyze a complex pottery dataset as a

web of meaningful relationships. We will try to explore the structure and patterning evidenced

by this specific collection of Roman pottery and, given the scope of the project, we will

largely restrict our interpretation to economic processes. Defining the nature of the

relationship between Roman pottery and the Roman economy was therefore a crucial first

step. In the following sections we will elaborate on the issues surrounding this relationship,

and discuss how a network approach might contribute to historical and archaeological

discussions on the Roman economy.


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2.2. The Roman economy: a battlefield of models

The structure and performance of the ancient economy has been the focus of much scholarly

debate, both in the archaeological and the ancient historical discipline. Prominent historians at

the forefront in the earlier stages of this debate introduced highly influential models, notably

the fundamentally conflicting modernist (e.g. Rostovtzeff 1926) and primitivist (e.g. Finley

1973) approaches. It was not until fairly recent, however, that a number of publications

signaled a new compromising focus of the discussions, nevertheless stressing the idea that the

ancient economy should be understood on its own terms rather than from a 20th-21st Century

perspective (Morley 2007; Scheidel et.al. 2007).

Although the potential contribution of archaeological sources was recognized at an early stage

(Charlesworth 1924), they did not come to form an integral part of historical models until

Keith Hopkins’ (1980; 1983) work in the 1980’s. In more recent publications the

complementary nature of material and literary sources is now widely acknowledged (Kingsley

& Decker 2001; Laurence 2004; Scheidel et.al. eds. 2007). As we mentioned in the previous

section, an overwhelming emphasis has been placed on pottery in archaeological studies of

the ancient economy. No doubt due to its durability and abundant presence on sites, as well as

the resulting potential for quantification (Orton & Tyers 1992; Tomber 1993). It is the

presence of non-local pottery on sites all over the Roman Empire, however, that seems to

attest of the existence of dynamic inter-regional trade networks. Although pottery was

probably largely not traded for its own sake, it does reflect trade connections in other goods

and one should realize that different types of pottery attest of very different processes (Greene

1992: 58). Used mainly as containers for perishable goods transported in bulk, amphorae can

be considered more informative about direct commercial connections than tablewares for

example, which might have filled in the empty spaces in the merchants’ crafts (Abadie-

Reynal 1989: 143; Greene 1992: 58-59). But these are not the only difficulties in extracting

economic meaning from baked clay. Indeed, we can say with some certainty that we are

completely uninformed of what happened to a vessel between its production and its

deposition. Studies on modes of production (Peacock 1982), modes of exchange (Peacock &

Williams 1986, Davies 2005) and the sheer diversity of contexts in which pottery is found,

illustrates that varying processes that took place at every stage of a pot’s life cycle. But do

these processes influence the archaeological record? And how are they reflected in the

datasets we collect? These issues have recently been addressed in Theodore Peña’s (2007)

comprehensive work on the life cycle of Roman pottery. Considering eight major practices


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during a vessel’s life (manufacture, distribution, prime use, reuse, maintenance, recycling,

discard, and reclamation), Peña suggested distinct flow models for different pottery types. He

does state, however, that although these practices have influenced the creation of the

archaeological record, for the most part archaeologists should not allow them to influence

their data collection, analysis and interpretative methods (ibid. 2007: 341). Although this is an

understandable standpoint, we would like to stress that this will inevitably lead to an

overrepresentation of ‘high commerce’ in our knowledge of the Roman economy (Horden &

Purcell 2000: 144-152). Nevertheless, we can state that at least large-scale processes in the

distribution of pottery will become apparent by collecting the available evidence en masse

(Bes 2007: 10). Such efforts should be complemented by detailed contextual work on key

sites, to contrast with and refine the overgeneralising picture created by the first approach.


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2.3. Economic processes in pottery structure

Inferring ancient trade from Roman pottery is, therefore, by no means a straightforward

process. This can be further illustrated by touching upon the popular research topic of Roman

trade routes. When we limit ourselves once again to the larger economic events, major trade

routes could be identified by considering a small number of parameters like technology,

climate and current (Pryor 1988). Assuming that natural factors stayed essentially the same

over the last two thousand years (Murray 1987), defining shipping highways can support an

interpretation of inter-regional pottery distributions (e.g. Fulford 1989). However, such an

approach is utterly unsuccessful in identifying processes that took place in the archaeological

past, as it does not explain how these shipping lanes were used: did vessels move directly

from one big port to the other, or did they stop in every small port along the way, and is it not

feasible that a succession of vessels and merchants were involved in one transportation

process, rather than one single entrepreneur? We believe that such studies should be

deconstructed at their very core, as the real issue lies in the way they perceive and explain the

relationships between material remains. We hope to illustrate this statement by introducing an

alternative approach, advocated by Horden and Purcell (2000) in their influential work on

Mediterranean history. The scholars discredit attempts including “enumerations of

topographical and climatic features”, but argue that routes should “be treated as a special

instance of a broader phenomenon” as they are often “defined by local knowledge and current

practice, not by physical peculiarities” (ibid. 2000: 124-135). They stress that microregions

cohere on a variety of levels, which should be understood by considering the “highly

complicated and always changing interaction of human productive opportunities” (ibid. 2000:

124). Although such statements might sound vague as they seemingly allow for all-round

connections, their value lies in broadening the focus of research on trade routes by seeing

connectivity as a result of changing human interaction with the environment. We believe that

this interaction is reflected in the structure of pottery distributions. Rather than explaining

these distributions in light of highly determining environmental features, we should focus on

exploring the structure itself as a result of deliberate human actions. In short, we believe that

the patterns evidenced in pottery distributions attest of the nature of the connections between

pottery producing regions, consuming sites, communities and even individuals. Throughout

the chapters we will illustrate how exploring the structure of pottery distributions is a

necessary first step towards understanding the nature of connectivity.


15

In order to do this, however, we will need to select a complex set of ceramic data. Although

plentiful for the Western Mediterranean (e.g. Fitzpatrick 1985; Reynolds 1995, 2005; Bonifay

2004), pottery studies examining the economic patterns in the east are rare (e.g. Abadie-

Reynal 1989; Fulford 1989; Bes 2007). When we consider the vast amounts of Roman pottery

being excavated in the latter region every year, this discrepancy seems incredible. Kingsley

and Decker (2001: 2) partly ascribe this situation to the relatively late adoption in Eastern

Mediterranean archaeology of “methodological and theoretical developments which have

evolved subsequently in the West”, and quantitative analysis in particular. Considering these

last shortcoming, the potential of projects like the Inventory of Crafts and Trade in the Roman

East (ICRATES) platform becomes apparent.3 It aims at collecting and deconstructing the

available material evidence to reinterpret mechanisms of production, exchange and use. In

addition to assembling an exhaustive database of tableware sherds, an elaborate exploration of

distribution patterns has been performed by Philip Bes (2007) in the framework of this

project. This great resource will allow us to explore a complex pottery dataset, and reflect on

how its structure relates to economic processes in the past.

3 http://www.arts.kuleuven.be/icrates/ .

CHAPTER III

METHODOLOGY “Trying to represent a complex system by models

that have the conceptual rigidity required for convenient management and manipulation is like

trying to wrap a ball with an inflexible board: we simply cannot achieve the necessary fit.”

Rescher 1998: 16.

TOM BRUGHMANS CHAPTER III METHODOLOGY

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3.1. Network analysis

In chapter I we argued that relationships between archaeological data are meaningful, because

they are the results of processes that led to the creation of the complex archaeological record

as we know it. We also stated that methods currently used in archaeology are often

unsatisfactory for analyzing the relationships in complex datasets. We believe that network

analysis offers such a methodology, and that an explicitly archaeological application of

network analysis should be developed. In this section we will briefly explore the basics of

network analysis, and how it has been used in archaeology and other disciplines.

3.1.1. Key concepts

The main goal of network analysis is detecting and interpreting patterns of relationships

between subjects of research interest. These subjects could be anything from individuals and

objects to countries or communities. Network analysis is widely applied in sociology and is

now a major paradigm in many social sciences, known as social network analysis (Freeman

2006). To define and visualize patterns of relationships network analysis uses a branch of

mathematics called graph theory (Harary & Norman 1953; Harary 1969; Barnes & Harary

1983). A graph represents the structure of a network of relationships. It consists of a set of

vertices (also called points or nodes) which represent the smallest units in the analysis, and a

set of lines (or ties) between these vertices which represent their relationships. Lines can be

directed (arcs) or undirected (edges), depending on whether the relationship between the

vertices is one-way or two-way. Figure 1 illustrates these basic components of a network.

One of the strengths of network analysis becomes clear when inspecting figure 1: the ability

to transform virtually any kind of dataset into visualizations that facilitate an intuitive

understanding of its structure. That is because the visualization of networks is based on some

basic principles. The most important of which are that the distance between vertices expresses

the strength or number of their ties, and that vertices that are related are drawn closer together

than vertices that are not related (Nooy et.al. 2005: 14). With these principles in mind we can

immediately identify vertices with similar characteristics, and vertices with a strong or weak

position in the network’s structure. For any research concerning relations such a

representation is enlightening and (initially) to be preferred to a geographical representation

for example, as the depicted structure is deduced from the data, rather than being forced on it

by geographical determinism. In fact, both types of representation complement each other, as

we will argue in more detail further on in this chapter.


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While networks represent the overall structure of a dataset, network analysis provides us with

tools to investigate the individual aspects that make up this structure. Structural aspects, such

as the number of ties or the value of ties connected to a vertex, are analyzed through a

calculation which outputs a single number for every vertex. This number can be compared

with that of other vertices, which allows the researcher to group vertices according to different

parameters. The specific structural aspects studied are discussed in chapters IV and V.

However, before any visual or quantitative inspection of a network can take place, one should

be very clear about what the network represents. The nature of the components of a network,

the network itself, and the meaning of the procedure of every type of analysis should be

established from the outset. This is undoubtedly the most important stage in any network

analysis. Indeed, we could calculate the structure of virtually any kind of data, but if we do

not know what this structure and the resulting patterning represents any results will be

meaningless. We will elaborate on the meaning of every aspect of this project’s networks

below.

Its flexibility of defining individual units, relationships and the scale of research makes

network analysis an interesting method for a wide range of research themes, including

history(Malkin et.al. 2007), economics (Smith & White 1992), management (Kilduff & Tsai

2003) and information technology (Krebs 2000). An overview of archaeological and non-

archaeological applications of network analysis is provided in appendix E.


19

Fig. 1: fictitious network of sites illustrating the basic components of a network.

3.1.3. Network analysis software

Although a large number of programs for network visualization and analysis are available

these days,4 we decided to use Pajek for this project for a number of practical and

methodological reasons.5 First of all, Pajek is free to download and use for non-commercial

purposes. The program offers an integrated package, combining visualization services with a

vast array of analytical functions. Although Pajek supports a limited number of import

formats, compatibility with other popular network analysis programs is ensured (e.g.

UCINET), and Pajek’s own file formats are sufficiently simple to allow swift conversion from

standard data storage formats (e.g. spreadsheets and databases). Lastly and most crucially, the

documentation provided by the authors of Pajek is comprehensive and accessible, allowing

social network analysis novices to grasp the basics through online tutorials6 and an

introductory textbook (Nooy et.al. 2005), while more advanced users will find all

functionality explained in detail in the online manual (Batagelj & Mrvar 2009).7

4 For an elaborate overview of social network analysis software : http://en.wikipedia.org/wiki/Social_network_analysis_software. 5 For full documentation and the latest software version, visit the Pajek wiki : http://pajek.imfm.si/doku.php. 6 Pajek’s own tutorial data: http://vlado.fmf.uni-lj.si/pub/networks/pajek/howto.htm; ESRC’s Research Methods Programme tutorials: http://www.ccsr.ac.uk/methods/publications/snacourse/snacourseweb.html. 7 Pajek online manual : http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/pajekman.pdf.


20

The simplicity of Pajek’s file formats is their strength: each network file (‘.net’) consists

merely of a list of all vertices, edges and arcs. In addition, one can group vertices together

using partitions (‘.clu’). These documents can be combined in one Pajek project file (‘.paj’).

For every 25-year period in the analysis, a project file and a number of different networks and

partitions were made.8 The process of extracting the relevant information from the database

and importing it in Pajek is discussed in section 3.4 below.

8 In digital archive : root/networks / .


21

3.2. Data

The key concepts of network analysis and its archaeological applications discussed above,

indicate that it provides a suitable method for examining the structure of complex datasets. In

order to test this method, however, we need a complex archaeological dataset. In this section

we will provide a brief description of the database used for this purpose, and the framework in

which it was created.

3.2.1. The ICRATES platform and database

The Inventory of Crafts and Trade in the Roman East (ICRATES9) Platform of Prof. Dr.

Jeroen Poblome (Catholic University of Leuven) has been assembling a nearly exhaustive

database of tablewares from the Roman East. At the moment over 25,000 individual

tableware sherds are included, derived from publications or ongoing excavations in the

Eastern Mediterranean. The project aims at studying the mechanisms of production and

exchange in antiquity, as they are reflected in material culture. From its inception, ICRATES

was considered an interdisciplinary platform for combining archaeological data scattered

across sub-disciplines. For this aim, the database and the doctoral research by Philip Bes

(2007) form a crucial first step.

Not only does the database incorporate a huge number of tableware sherds, it also contains an

enormously detailed account for every single piece of pottery (summarized in table 1). At its

heart lies the ICRATES table which combines crucial information from all other tables. In the

catalogue table, sherds are identified by unique catalogue numbers and information on ware10,

form11, dimensions and specific features (e.g. stamps) are included. The deposit table lists the

archaeological contexts in which the sherds were found, while the location table mentions

features of the sites themselves. Finally, the publication table serves as a bibliographical

reference to the provenance of all entries.

9 http://www.arts.kuleuven.be/icrates/ . 10 For this project we will use the term ‘ware’ to designate a named tableware or sigillata with recognized properties of fabric composition, typo-chronology and possibly provenance (Bes 2007: 6). 11 For this project we will use the term ‘form’ to refer to a vessel-type included in one of the major typo-chronologies listed in table 2 (Bes 2007 : 6).


22

ICRATES table Catalogue table Location table Deposit table Publication table ICRATES unique

ID ICRATES unique

ID Site name Deposit ID Publication ID

Catalogue unique ID

Catalogue unique ID

Ancient and modern names

Archaeological context

Author

Publication Fabric Environmental

features Deposit date Year

Location Form Modern country Type of deposit Reference

Deposit Stamps Roman province Ceramic objects Year(s) of activity Functional category

Measurements Distance to sea Published notes Nationality team

… … … … Table 1: summary of the information stored in the main tables of the ICRATES database.

3.2.2. Data sample

From the above we can conclude that the ICRATES database is a complex archaeological

dataset, the structure of which might consist of a large number of possibly meaningful

relationships. However, the sheer scale of the initiative, spanning the entire Eastern

Mediterranean from the second century BC to the seventh century AD, requires our analysis

to be restricted to a sample. As the tableware distributions are typically interregional a

geographical definition of our sample would hardly provide meaningful results, while a

typological restriction would limit the inherent complexity and dynamics of pottery

distributions. We therefore decided to make a chronological selection. The period of 150 - 0

BC was considered ideal as it includes periods with both high and low quantities of sherds, as

well as high and low diversities in pottery wares and forms (see charts in appendix B). This

period will be analyzed in 25-year intervals, a level of chronological detail that still reflects

the major patterns in the dataset (Bes 2007: 6-10, figs. 2-3), while allowing for the evolution

of the structure of pottery distributions to be examined. Such a diverse sample will allow us to

test whether economical processes in the archaeological past (e.g. distribution of tablewares)

as well as processes in the academic present (e.g. typological knowledge) are reflected in the

datasets structure.

The remodeling of the ICRATES database to match our method and reflect the data sample is

discussed in section 4 of this chapter.

3.2.3. Tableware typologies

Current knowledge on Roman tableware typologies occupies an interesting position in our

research. As it determines both the phasing and the provenance of sherds, we should be able


23

to establish how it influences the data structure, which will facilitate the identification of past

and present processes.

Sadly, the scope of our research does not allow us to elaborate on typology studies. We limit

ourselves to a list of the major eastern tablewares included in the analysis, our current

knowledge on their place of production, and references to the typo-chronological frameworks

used (table 2). The presumed regions of production are also indicated on the distance

networks section of the project’s website12. For a more elaborate overview of Eastern

tablewares, we refer the reader to Bes (2007: 14-33) and Hayes (1972; 1980; 1985).

Table 2: typo-chronological reference, presumed region of production and possible centers of production used in the analysis for all major wares included in the analysis.

12 http://mapserver.arch.soton.ac.uk/networks/distance.html .

Ware Abbreviation Typo‐chronological

reference Region of production

Producing centers used for analysis (chapters V

and VI)

Eastern Sigillata A ESA Hayes 1985 Coast between Tarsos (TUR) and Latakia

(SYR)

Tarsos, Küçük Burnaz, Antiocheia ad Orontem,

and Leukos Limen

Eastern Sigillata B ESB Hayes 1985

Maeander Valley in western Asia Minor (TUR). Possibly Aydin (ancient Tralleis)

Ephesos

Eastern Sigillata C ESC Meyer‐

Schlichtmann 1988 Pergamon and

surrounding region Pergamon

Eastern Sigillata D ESD Hayes 1985 Cyprus (probably the

western part)

Paphos, Palaipaphos area, Amathous, Kourion, Kition, Ayios Philon, Salamis/Constantia, Panayia Ematousa

Italian Sigillata ITS Ettlinger et.al. 1990 Central Italy Central Italy


24

3.3. Ceramic networks

We mentioned above that, in order for a network analysis to be meaningful, every aspect of

the network and the method should be defined from the outset. In this section we will discuss

what relationships are present in the complex dataset introduced in the previous section, and

why network analysis is a suitable method for exploring them. We do acknowledge, however,

Rescher’s (1998: 16) statement cited at the beginning of this chapter, which stresses that no

one model can represent the full complexity of a system. A data sample or limitation to a

certain type of relationship is always necessary. Therefore two types of ceramic networks will

be introduced, and we will elaborate on how these should be compared and interpreted.

3.3.1. Identifying meaningful relationships

In a dataset as complex as the one we use, a large number of relationships can be formulated.

However, given the aims of this project there are some restrictions to take into account.

Firstly, there is our goal of understanding the complex dataset itself in order to test our

method. This forces us to construct networks that represent the data in all their complexity,

without losing or hiding any of the possibly meaningful information, and independent of any

assumptions. Secondly, we would like to contribute to ongoing archaeological and historical

discussions by exploring the nature of the economic information pottery carries. Although this

will be achieved to some degree by examining the structure of the dataset on its own, we do

feel that testing hypotheses on the Roman economy based on assumptions expressed in

literature might provide a more comparable contribution to discussions. Lastly, we would like

to address Batty’s (2005: 152) statement that “what might appear to be a random distribution

of activity in Euclidean space is often seen as being highly ordered on a network”, and vice

versa. As such, we do not want to restrict our analysis to a geographical perspective, but like

Knappett et.al. (2008) we are more interested in understanding the dynamics between

physical and relational space, as it is reflected in complex datasets.

Having formulated these restrictions, we can define two types of networks that require a

different methodological approach, but provide complementary interpretations:

1. A relational network of co-presence, representing pottery distribution patterns.

2. A geographical network of distance, representing a hypothesis of shortest-distance

trade routes.


25

3.3.2. Networks of co-presence

Archaeometric analyses and comparative studies of eastern tableware fabrics, have resulted in

the identification of producing regions, and sometimes individual production centers, for all

the major wares (table 2). We can therefore establish the spread of a producing region’s (or

center’s) distribution, which is reflected in the presence of distinct tablewares in sites all over

the Eastern Mediterranean. An approach centered on wares, however, will not succeed in

identifying different circulation patterns within a ware’s own distribution. We should,

therefore, analyze the distributions of the individual forms of all wares, which will provide

very detailed patterns that can still be added up per ware. Instead of presenting such

distributions as dots on a map, however, we would like to develop a non-geographical way of

approaching them, using the strengths of network visualization.

The full complexity of pottery distributions can be captured in a single undirected network,

with vertices representing sites or forms, the relationships between them indicating the

presence of a form on a site, and the line values representing the number of sherds found on

this site. In this network, sites can only be connected with forms evidenced for the site, and

forms can only be connected with sites on which they were found. This structure is called a

two-mode network as it consists of two distinct sets of vertices (Nooy et.al. 2005: 103), an

example of which is shown in figure 2. To facilitate analysis and interpretation, a two-mode

network can be divided into different one-mode networks, as represented in figures 3 and 4.

As we explained in our discussion of network analysis above, establishing the meaning of

every aspect of a network is the method’s most crucial part. So what do these networks

actually tell us? The ties of a pottery form to all sites on which it is found represents, in its

broadest sense, the distribution network of that pottery form as it is reflected in our dataset.

The presence or absence of forms on the same sites in the same period (further referred to as

co-presence), are an indication of the similarity or dissimilarity of these forms’ distribution

networks. What network analysis allows us to do is to analyze the structure of these

distribution networks which, in the words of Michael Batty (2005: 153), will help us

understand the “processes that reach, maintain and evolve these structures”.

In conclusion, these networks of co-presence are merely an alternative visualization of

ceramic distributions, but will allow us to explore this aspect of the dataset in all its

complexity on its own terms, avoiding any assumptions. Our decision to make ceramic forms

the focus of our analysis, will allow us to test how our current knowledge of typologies

influences distribution patterns. Acknowledging the existence and influence of such decisions


26

made in the academic present, will enhance our ability to identify and interpret past processes

that led to the distribution patterns as they are reflected in the pottery evidence. Moreover,

these networks of co-presence will serve to test our second network type, the hypothesis of

shortest-distance trade routes.


27

Fig. 2: two-mode network of the period 150-125 BC, representing sites connected to pottery forms which are

present at the site. The value indicates the number of sherds of a form that have been found.


28

Fig. 3: one-mode network of the period 150-125 BC, representing sites connected to sites which have evidence

of the same pottery forms (co-presence). The line value indicates the number of pottery forms that are co-present.


29

Fig. 4: one-mode network of the period 150-125 BC, 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

forms are co-present.


30

3.3.3. Networks of distance

In the previous chapter we mentioned that archaeologists have often used ceramics in

attempts to reconstruct trade routes, along which goods and people were transported.

Although it can be said that our understanding of the general direction of trade becomes more

accurate, thanks to an increasingly detailed knowledge on tableware producing sites and

regions, the exact itineraries are still largely unknown. This is mainly caused by a lack of

evidence on what happened between the production and deposition of individual vessels.

Theodore Peña (2007) and David Peacock (1982) illustrated that during its life cycle Roman

pottery was subject to a wide variety of processes, which all contributed to the shaping of the

archaeological record. The choice for a specific route could have been influenced by

numerous factors, ranging from topography and sailing conditions, to the socio-political

environment and even individual motivations. Therefore, if we are to understand these

processes that led to the attested tableware distributions, we should examine how each of the

influencing factors is reflected in the archaeological record.

As our interest in the reconstruction of trade routes is largely limited to illustrating the use of

our method for testing hypotheses and examining geographical networks, we will focus on

testing the influence of one single factor: distance. We state that Roman tableware vessels

were transported during every part of their life cycle over trade routes chosen to minimize

travel distance. If this hypothesis is true, then it should be reflected in our complex dataset.

Network analysis has proven to be a suitable method for understanding the structure of

transportation systems (Batty 2005), and Graham (2006b) and Isaksen (2005; 2008) have

shown that it can be used to examine Roman itineraries. We will, therefore, test this

hypothesis by constructing a distance-based network and comparing it with the networks of

co-presence introduced in the previous section. We will restrict our analysis to the period of

50-25 BC, a well documented period in which two new wares emerge (see appendix B).

Unlike the networks of co-presence, this second network type is not an alternative

visualization of the dataset. It should represent the geographical relationships between sites

based on proximity, rather than relationships that are inherent to the ceramic data. Such a

network can be created using a spatial clustering technique based on the relative

neighbourhood concept. Contrary to more traditional clustering techniques that measure the

absolute distance between two points (e.g. nearest neighbour analysis in Hodder & Orton

1976), the relative neighbourhood concept considers a region around pairs of points for

creating relationships. In the words of Kirkpatrick and Radke (1985), “… two points are


31

considered ‘relative neighbours’ if they are at least as close to each other as they are to the

rest of the points”. The archaeological application of this concept has been discussed by

Jiménez and Chapman (2002), stressing its use for revealing clusters at different resolutions

(Toussaint 1980; Urquhart 1982; Kirkpatrick & Radke 1985). The latter can be achieved

through the addition of a parameter Beta, creating a graph referred to as a Beta-skeleton

(Kirkpatrick & Radke 1985). This notion can be defined as “the complete set of edges joining

B-neighbours for a particular value of Beta” (Jiménez & Chapman 2002: 95). Beta represents

the size of the region of influence for each pair of points: 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 sub-networks. As such, the strength of this technique lies in its ability to visualize

the internal structure of the same set of points as a series of graphs with different densities of

relationships.

A Beta-skeleton would provide the ideal basis for our distance-network as it represents the

shortest paths between sites, taking the relative location of all sites into account. Using a

program written in Visual Basic by Dr. Graeme Earl, a series of Beta-skeletons was created

and visualized in ArcGIS of all sites for which sherds were evidenced in the period under

investigation. We decided to use Beta value 2, creating a graph right before it falls apart in

sub-networks, as we assume that vessels could have been transported from any site to any

site. In addition, Beta 2 restricts the number of paths between sites, removing all but the most

important relationships in its proximity based structure (fig. 5).

Lastly, the transportation from centre of production to centre of deposition following the

shortest path had to be simulated for every single sherd recorded in the database and dated to

this period. To achieve this, the Beta-skeleton was exported from ArcGIS and remodeled in

Pajek ‘.net’ format. We decided to calculate the shortest paths between any two sites in Pajek,

as this program allows one to calculate both geographical and relational distances. This

required the values of the edges in the network to reflect the distances between the sites they

connected. We decided to perform the simulation of pottery transportation over the distance

network manually, as only one period would be examined.

The method described in the previous paragraphs resulted in a distinct network per ware,

reflecting the absolute volume of pottery transportation between any two sites. In addition, a

combined network was created by adding up the values of the networks per ware, reflecting

the complete distribution of tablewares in 50-25BC over the shortest paths. The networks

were produced in both Pajek’s network format and as ArcGIS shapefiles to allow for a


32

geographical as well as a relational analysis. Both formats are available through the project’s

website13 and digital archive14.

13 Networks: http://mapserver.arch.soton.ac.uk/networks/distance.html ; webmaps: http://mapserver.arch.soton.ac.uk/networks/map.html . 14 In digital archive : root/networks/distance ; and root/spatial data/shapefiles.


33

Fig. 5: beta-skeleton (beta = 2) for the period 50-25 BC. Source topography and boundaries: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).


34

3.4. Database structure

As we explained in section 3.2, the ICRATES database contains a wealth of information on

our current knowledge of Roman tableware distributions in the eastern Mediterranean. Our

main goal in transforming the database for the current project was to maintain this detailed

information in its entirety, while enhancing the database structure to support the project’s

method. Two aims were formulated to act as guidelines in the transformation process:

1. The database should allow users to trace every analysis back to the individual sherds

on which it is based.

2. The data-flow from the database to Pajek supported files should be as fast and easy as

possible.

Before any changes were made to the database, a decision was made to maintain the structure

and contents of the tables used by the ICRATES project, as this would facilitate the adoption

of our method into later versions of the ICRATES database. To this end all original tables

were renamed as ‘tbl_icrates_...’ and all new project specific tables named as ‘tbl_pajek_...’.

The original and new database structure can be compared in appendix C.

Keeping both of the above aims in mind, it was decided to embed the Pajek file structure into

the database as far as possible. Pajek’s network file format, consisting of vertices and edges,

is therefore reflected in the new tables. In order for one list of vertices to be reused in every

network file, new unique identifiers were given to all sites (1-477) and form/ware

combinations (478-1780), which are listed in ‘tbl_pajek_vertices_sites’ and

‘tbl_pajek_vertices_forms’. A further field was added to each table, containing the partition

each vertex belongs to in a two-mode network (i.e. to allow Pajek to identify them as either

sites or forms). These tables make up the bridge between the ICRATES and Pajek parts of the

database.

At this point we can already extract data in Pajek’s network format, using a simple query

listing all vertices as well as the edges for a specific 25-year period, a simplified version of

which for period X-Y is provided here:

SELECT sites, forms and count of sherds FROM the relevant tables HAVING Standard Typo-chronological Lower Date < Y AND Standard Typo-chronological Upper Date > X


35

However, performing such a query would reuse every sherd as many times as the number of

25-year periods it is dated to, resulting in a grossly overestimated volume of tablewares for

every period. To attain a better estimation of pottery volumes we use a method devised by

Fentress and Perkins (1988) for ARSW and previously applied to the ICRATES data by

Philip Bes (2007). Their method for quantifying sherds is based on the idea that a vessel of a

specific form, theoretically, has an equal chance of being produced in every single year of its

date range. By dividing the number of attested sherds of a form by the number of years in its

date range and adding the resulting values up according to the 25-year periods, we get a better

estimation of pottery volumes. Figure 21 in appendix B shows the evolution of tableware

volumes based on all sherds included in the ICRATES database.

This calculation was performed for one sherd of every individual form/ware combination

(‘forms_per_25year_period.xls’15) and was added to the database in tables per period (e.g.

‘tbl_pajek_period_X_Y’). These tables allow for the above query to be elaborated, by

multiplying the number of sherds of a specific form/ware combination with the chance it was

produced in the period in question. Such queries provide all edges for a 25-year period two-

mode network of co-presence, representing sites (site vertex ID) for which the presence of a

form is attested (form vertex ID) and the volume of the attested form. The queries were added

to the database (e.g. ‘qmak_pajek_edges_X_Y’), and the resulting tables (e.g.

‘tbl_pajek_edges_X_Y’) were connected to the vertices tables, which allows users to trace

every network back to its primary data.

Some exploratory charts showing the nature and volume of the database’s contents were

added in appendix B. This appendix served as a starting point for the analyses and can be used

as a quick reference for data critique when reading the analyses.

15 In digital archive: root/charts/forms_per_25year_period.xls.

CHAPTER IV

METHOD AND ANALYSIS CO-PRESENCE

TOM BRUGHMANS CHAPTER IV METHOD AND ANALYSIS CO-PRESENCE

37

4.1. Method

In the previous chapter we described how a network of co-present forms might enhance our

understanding of a complex pottery dataset, by visualising and analysing it as a network of

relationships. We decided to create two-mode networks (e.g. fig. 2) illustrating the presence

of ceramic forms on sites per 25-year period, which will be examined as two one-mode

networks (e.g. figs. 3-4). However, a quick look at the resulting dense networks indicates that

a method is needed to make sense of such complex patterns. In this section we will introduce

the qualitative and quantitative network analytical tools used to examine complex networks.

As the project’s method consists of a significant number of tools and acronyms, a glossary (p.

100-102) was added with concise explanations of methods and terms.

4.1.1. Visual inspection: identifying general structure

Given the basic principles of network visualization introduced in chapter III, the co-presence

networks invite visual identification of patterning.16 Vertices close to each other, be it sites or

forms, have a more similar position in the network’s structure than vertices far apart. This

simple rule of thumb allows one to visually classify the vertices according to their similarity,

and identify clusters of sites or forms with a very high similarity. But in order for such

clustering to be understood more precisely we need to introduce some quantitative tools that

will assist visual inspection.

4.1.2. Hierarchical clustering: similarity in overall structure

We will start our quantitative analysis at a general level, using hierarchical clustering to

represent the complete structure of a one-mode network. First of all, the one-mode networks

are visualized as adjacency matrices, showing which vertices are neighbours (adjacent) in the

network and the strength of their ties (e.g. fig. 6). Secondly, the dissimilarity between vertices

is calculated, based on the profile of their rows and columns in the adjacency matrix (Nooy

et.al. 2005: 265-237). We decided to use Pajek’s ‘corrected Euclidean distance’ algorithm to

compute dissimilarity, as unlike other methods included in Pajek this algorithm takes the

value of lines in account:

16 All networks are available on the project’s website (http://mapserver.arch.soton.ac.uk/networks/co-presence.html ) and the digital archive (root/networks/co-presence).


38

∑≠=

−+−⋅+−+−=n

vuss

vuuvvvuusvsuvsus qqqqpqqqqvud,1

22225 ))()(())()((),(

Pajek’s corrected Euclidean distance algorithm, v stands for vertex, + stand for symmetric sum, q is an adjacency matrix of vertices, e.g. vuq (Batagelj et.al. 1992; Batagelj & Mrvar 2009: 33-34).

Next, the hierarchical clustering technique groups vertices that are most similar together in

clusters. We decided to use the ‘average’ clustering method, as it is widely used for

archaeological data (Baxter 1994; Shennan 1997: 239-240) and produced the most satisfying

results for our dataset. Results of hierarchical clustering are visualised in a dendrogram (e.g.

fig. 7), which represents increasing dissimilarity between vertices from left to right.17 All

adjacency matrices and dendrograms are available in the project’s digital archive18.

Although the dendrogram will be used to identify groups of sites or forms with a similar

structure, hierarchical clustering does not allow for an easy understanding of these structures.

Indeed, we should not be fooled in accepting the unchallenged results as being of

archaeological significance (Read 1989: 46; Shennan 1997: 255). We will, therefore, explain

these clusters through an examination of some of the individual structural aspects hierarchical

clustering combines: the number of edges and their values.

17 Absolute values resulting from this technique are available in the hierarchy files of the project’s digital archive (e.g. root/networks/co-presence/0-25/co-presence_0-25_sites_hierarchy.txt’). 18 In digital archive: root/networks/co-presence.


39

Fig. 6: example of an adjacency matrix for the sites of period 150-125 BC, representing the adjacency of vertices in the network and the strength of their ties.


40

Fig. 7: example of a dendrogram for the sites network of the periof 150-125 BC. It represents the increasing dissimilarity of the sites’ tableware assemblages from left to right.


41

4.1.3. M-slices: nested edge values

Similarity of vertices is partly determined by the strength of their ties: the larger the number

of co-present forms found on two sites, the stronger their tie and the more interdependent they

are. We will therefore classify vertices according to their line values, using the concept of m-

slices: in an m-slice, vertices are connected by lines of value m or higher to at least one other

vertex (concept introduced as ‘m-cores’ by Scott 1991; Nooy et.al. 2005: 109). M-slices

consist of nested groups of vertices, as illustrated in figure 8, and the ‘m’ stands for the line

value of the group or ‘slice’. This means that a 3-slice is part of the bigger 1-slice, while some

of the vertices of a 1-slice are not part of the 3-slice.

Fig. 8: fictitious example illustrating the nesting of m-slices.

When we apply this to the co-presence network of forms (e.g. fig. 4), the forms that are part

of a high m-slice are those that are present on many sites. The m-slices in the co-presence

network of sites (e.g. fig. 3), are an indication of the diversity of forms evidenced on these

sites. For this project, m-slices will therefore be used to establish the width of a form’s

distribution network, and the number of distribution networks a site is part of.

4.1.4. K-cores: nested number of edges

Similar to the previous approach, k-cores are also nested and the ‘k’ stands for the core’s

number. Unlike m-slices, however, k-cores represent groups of sites or forms with at least a

certain number of relationships: a k-core consists of all vertices that are connected to at least

‘k’ other vertices within the core (Nooy et.al. 2005: 70-71).

In a co-presence network of forms (e.g. fig. 4), a high k-core consists of forms that are co-

present with many other forms. For the co-presence network of sites (e.g. fig. 3), a high k-

core indicates that a site has evidence of forms that are present on many other sites. We will


42

therefore use k-cores as an indication of the similarity of the distribution networks of forms,

and the width of the distribution networks a site is part of.

4.1.5. Understanding the results

One pattern that is reflected in every network is the existence of a core, with many

connections in- and outside the core, and a periphery with mainly connections to the core.19

This structural dichotomy can be understood as a reflection of the size and diversity of the

dataset, which is more representative for some sites and forms than for others. Both core and

periphery structures are the result of processes in the archaeological past, like the intentional

distribution of tablewares by their producers, the primary use by initial owners, or the possible

reuse by secondary owners. In addition, the patterns in core and periphery also attest of post-

depositional decisions, like the selection of sites for excavation, the ceramologist’s ability to

recognize different types, and the assembling of the database.

In addition to examining the core-periphery structure, attention should be paid to the

dynamics between different wares’ distribution patterns. All of these major wares were

transported inter-regionally and in large quantities, but not necessarily by the same people or

institutions. Although some wares are better attested than others, it should be possible to

identify the distinct distribution networks as they are represented in our dataset. Comparing

such ware specific networks and looking for sites and forms where they overlap or differ

might enhance our understanding of the processes that lead to the distribution of tablewares in

the Roman East.

19 This core-periphery structure should not be confused with the above mentioned method of k-cores. In what follows we will use the word ‘core’ to indicate the dense centre of a network, and k-core (e.g. 2-core) will refer to a set of vertices with a certain number of lines.


43

4.2. Analysis: co-presence networks

In this section we will explore the networks using the analytical tools described in the

previous part. It is most interesting to see how the amount of data in the database rises steeply

throughout the periods (fig. 9; table 3), resulting in ever more complex networks (see also the

evolution for the entire database in the data exploration charts of appendix B). We will see

whether the analysis succeeds in identifying the structure of well documented periods as well

as underrepresented ones. We will also compare some of the resulting patterns with Bes’

(2007) work on the same database, seeing where network analysis provides the same,

different or more detailed results. All networks discussed here are accessible on the project’s

website20 and the digital archive21. Please refer to the index and map of sites in appendix A

for all the sites mentioned in the analysis.

Fig. 9: evolution of absolute sherds dated to a period, probable volume of sherds per period, the number of forms

attested for a period, and the number of sites on which these sherds were found.

20 http://mapserver.arch.soton.ac.uk/networks/co-oresence.html . 21 In digital archive: root/networks/co-presence/.


44

Period Probable volume Absolute volume Number of forms Number of sites 150‐125 BC 87 224 16 57 125‐100 BC 330 1459 35 135 100‐75 BC 451 2159 65 134 75‐50 BC 496 2676 99 139 50‐25 BC 592 3047 166 158 25‐0 BC 906 3706 294 172

Table 3: evolution of absolute sherds dated to a period, probable volume of sherds per period, the number of forms attested for a period, and the number of sites on which these sherds were found.

4.2.1. 150-125 BC

ESA was the most widely distributed ware in this period. A core-cluster of seven ESA forms

was likely to have been distributed together to many sites. In this ESA distribution network,

Caesarea Maritima, Gindaros, Tel Anafa, Paphos and Petra were important trading links as

they have evidence of most of these core forms. Sites in the 3 and 4-slices must have had a

similar function evidenced in a slightly lower diversity of forms. The same pattern of a

distinct group of early ESA forms (termed ‘Group I’) being mainly distributed to Levantine

and Cypriot sites, was also noted by Bes (2007: 103). We would like to mention, however, the

existence of diversity within this group, evidenced in the distribution patterns of three

peripheral ESA forms (EAA16, EAA17A-B, EAA17A) with each a remarkably distinct

distribution that excluded the major ESA sites.

A second distribution network is evidenced by a small volume, but high diversity, of ESC

forms. Sites like Pergamon, Alexandreia, Tenos, Samos and Ephesos were part of this

network, while Assos with its high diversity of ESC forms, must have played a more

prominent role. These two distribution networks have only one site in common: Tel Anafa, on

which a small volume of ESC (M-SS1) was found.

4.2.2. 125-100 BC

All ESA forms are still widely distributed (between 5 and 61 sites). As the number of

different ESA forms increases, new forms are more widely distributed than forms that already

existed in the previous period, with the six forms in the 24-slice being extremely widely

distributed. It is interesting to note that this pattern was also identified by Bes as ‘Group II’

(2007: 77-78, 104), who related it to the ware’s increasing western distribution in this period.

The important centers in the ESA distribution network increase as well but remain largely

Levantine, adding Apamea, Tarsos, Samaria-Sebaste, Atiocheia ad Orontem and Epiphaneia

to the list of the previous period.


45

The ESC distribution network becomes more similar to the ESA network, as more ESC forms

are co-present with ESA forms. However, the central sites in its circulation (Assos, Samos,

Tenos, Ephesos and Pergamon) are sites of secondary importance in the ESA distribution

network, with ESC being the dominant ware in Assos and Pergamon. Therefore, ESC seems

to maintain a proper distribution network, which is largely independent from ESA trade.

4.2.3. 100-75 BC

As for the previous two periods, the ESA distribution network is very clearly reflected in the

cores of the networks, with most of the ESA forms having a very wide distribution. The

widest distribution (35-61 sites) is attested for the same cluster of forms (24-slice) as in the

previous period, which might be an indication of continuity in the ESA distribution network

between the two periods. Nessana can be added to the previous period’s cluster of important

centres in the ESA distribution network, and Caesarea Maritima somewhat diminishes in ESA

diversity.

Some of the sites central to the ESA distribution network, however, attest of a significant

change in their assemblage. A new ware has emerged, ESD, and nearly the full variety of its

forms was found in Paphos, which also has a significant diversity of ESA forms (16). Two

ESD forms (EAAP37A-B and EAAP37B) have a very wide distribution that is very similar to

that of the core ESA forms. These forms might have been traded over the ESA distribution

network, contrary to other ESD forms that seem to be centered around Paphos and a few other

sites where ESA is the major tableware.

Although ESC knows an increase in the number of different forms, there is no significant

widening in its distribution pattern. ESC and ESD distribution networks are distinct. In Assos

ESC, of which a very high diversity was found (18), is the dominant ware, while Pergamon

has exclusively ESC. In other sites, ESC seems secondary to ESA in volume and diversity.

Only on Tel Anafa, Anemorion and Tenos are ESC and ESD co-present, which leads to the

conclusion that both wares were mainly aimed at different parts of the ESA distribution

network (as is illustrated by the two distinct clusters in the periphery of the forms network),

with different central sites in their own networks.

At this stage it is worth considering a series of events that took place in this period, and that

might be reflected in the dataset. An important trade connection in these pre-imperial times

seemed to have concerned grain exported from Egypt to the west. Large numbers of imported

amphorae indicate that Alexandria was the main centre in this trade, which was connected to


46

Italy via Delos. Both sites played a redistributive role in the eastern Mediterranean, with

Alexandria providing Italian ceramics to sites like Tel Anafa (Berlin 1993, 1997; Bes 2007:

106), and Delos to Athens (Poblome et.al. 2001: 144; Malfitana et.al. 2005; Bes 2007: 105-

107). The situation becomes even more interesting for this project if we consider that Delos

was destroyed in 87 BC during the Mithridatic wars, and again in 69 BC. Such large scale

trade connections and political events, evidenced in the amphorae, might also be reflected in

the tableware data. But no pattern can be reconciled with these events. Delos’ position in

particular remains unclear. The tableware assemblages of Alexandria and Athens on the other

hand indicate that they might have been important redistribution centers in the ESA

distribution network. For this period and the next, however, Delos seems to have been only a

minor link in these networks, and was even less important in previous periods. As was noted

by Bes (2007: 106-107) and confirmed by our approach, these occurrences do not need to be

reflected in the tableware data. Processes in the tableware distribution might have been

dissimilar to some extent from such archaeologically and historically attested events (Lund

2004: 12).

4.2.4. 75-50 BC

The full diversity of ESA forms still has very similar distribution patterns (the same forms as

the previous periods at its core), with a significantly wider distribution than other wares.

While the same core of ESA majority sites still exists, some of the previously central sites in

the ESA distribution network become increasingly central to all distribution networks. Sites

like Tel Anafa, Paphos and Knossos with a high diversity of ESC and ESD but mainly ESA,

belong to very diverse distribution networks and could be considered centers where trade

efforts converge.

ESC significantly increases in both volume and diversity of forms, but its distribution network

does not seem to widen as a result. Only one form (M-SN33a-d) seems to have a wide

distribution that reflects the existence of a distinct ESC distribution network, in which Apollo

Smitheion, Troia/Illion, Samos, Pergamon and, above all, Assos were important centers.

Pergamon’s ESC forms are largely only found on this site, which should be explained in the

light of Pergamon being a production centre of ESC. The geographical nature of this pattern,

being focused on the northern west coast of Asia Minor, has also been noted by Bes (2007:

45-51) and we will discuss this in more detail in the following two chapters.


47

Although the number of sites on which ESC and ESD are co-present increases (adding

Gortyn, Kenchreai, Knossos and Paphos to the list of the previous period), both wares still

show a largely dissimilar distribution. ESD distribution is more similar to ESA networks than

ESC is, with Paphos and Knossos becoming increasingly important centers in its distribution.

4.2.5. 50-25 BC

A large number of ESA forms have a very wide distribution, adding five newly introduced

forms to the list of the previous periods (EAA12, EAA4B, EAA29, EAA9 and EAA7). An

increasing number of core ESA sites has a diversified assemblage (Knossos, Paphos, Tel

Anafa, Ephesos, Gortyn, Alexandreia), although in many core sites ESA remains more or less

the only ware (Apamea, Gindaros, Petra, Nessana, Tarsos, Antiocheia ad Orontem, Jerusalem,

Samaria-Sebaste, Epiphaneia, Amathous). Throughout the last three periods, Caesarea

Maritima gradually lost its position at the core of ESA distribution, contrary to most other

core sites in the Levant.

ESD still has a very limited distribution, apart from form EAAP37A-B whose distribution is

very similar to that of the major ESA forms. ESC is still attested on more sites than ESD, and

in higher volumes, with both wares’ distributions centralized around different sites: Paphos

for ESD; and Assos, Ephesos, Apollo Smitheion, Troia/Illion and Pergamon for ESC. Both

wares are co-present on the same sites as in the previous period (in addition to Gindaros),

which are all core ESA sites. We should mention that the pattern discussed for ESD in the last

three periods, centering around Paphos and increasingly expanding, was also identified by

Bes (2007: 75-83). What is clearly evidenced in these networks and what Bes did not

mention, however, is the role a few widely recognized and distributed forms (EAAP37A-B

and EAAP37B) played in constructing this pattern. This might indicate a duality in ESD

distribution, with some forms being aimed at the local Cypriot market, while a few others

were adopted in the ESA distribution network.

Two new wares emerge in this period, ITS and ESB, both evidenced in very low volumes.

Their distribution is largely limited to core ESA sites with a high diversity, and they are co-

present on many sites (on Ephesos, Knossos, Assos, Gortyn, Corinth and Athens).

4.2.6. 25-0 BC

A duality starts to develop in the core of all networks. Although ESA forms remain most

widely distributed, three ITS forms (consp18.2, consp22, consp22.1) are also attested in a

large number of sites. The increased volume of ITS and enormous diversity of forms mark a


48

change in the core sites, making ITS the dominant ware in sites that were important ESA

centers (Gortyn, Myos Hormos, Stobi, and especially Berenice, Corinth and Carthage). Most

of these sites still have a high diversity and volume of ESA, but the overall diversity of a

number of core sites’ assemblages increases significantly (adding Athens, Tarsos, Jerusalem,

Petra, Epiphaneia, Samaria-Sebaste, Carthage, Kenchreai and Myos Hormos to the list of the

previous period). This increasing diversity seems to indicate that, for the first time, these core

sites (which stay largely the same) are part of a number of different distribution networks

through which they were supplied with tablewares. Another interesting pattern is Corinth’s

increasingly strong position in the core of tableware trade, which Bes (2007: 44) suggests

might be related to the city’s refounding in 44 or 42 BC and its intermediary role in ITS trade.

ESB still has the same limited distribution as in the previous period, which was almost

exclusively to important centers in the ESA and ITS distribution networks.

ESD still has a limited distribution, except for forms EAAP37A-B and EAAP22A, which

seem to be distributed among the ESA and ITS distribution networks.

The majority of ESC forms are still attested for sites in the core with mainly ITS and ESA,

with the same sites as listed in the previous period playing a central role. The ESC forms with

the widest distribution are the same as in the previous period. Although becoming slightly

more similar (adding Kourion and Athens to the list of sites on which ESC and ESD are co-

present), ESC and ESD distribution networks still seem to be largely distinct.


49

4.3. Discussion: from structure to processes

This method clearly succeeds in identifying the general structure of a specific type of

relationship inherent to a complex dataset. Using m-slices, k-cores and hierarchical clustering,

the patterns (big or small) that make up this structure can be highlighted. As such, we were

able to recognize the evolving distribution networks of individual wares, and the position of

sites and forms within these networks. We illustrated that a network analysis approach was

able to identify the same general patterns in more detail as those resulting from research

conducted by Bes on the same dataset. As network analysis allows us to explore only the

structural aspects of such patterns, however, it should mainly be seen as a preliminary step to

an interdisciplinary approach like Bes’ exhaustive study of Eastern Mediterranean tableware

trade.

We should stress, however, that the identification of structure is meaningless if we do not

understand how this structure came to be. It is not the immediately evident general patterning

that shape a system. Systems function from the bottom up, where local action generates global

order. The general structures identified using network analysis, should be explained through

the dynamics of local action (Batty 2005: 153). We should, therefore, try to understand the

basic processes that led to the creation of complex archaeological datasets, before we can

interpret its global structure. Discussions surrounding the interpretation of structure are

numerous, and we will come back to this issue in more detail in chapter VII. Here, we will

illustrate how network analysis can serve to identify patterns at different topological and

temporal scales, to support the understanding of local processes shaping global structure.

We already mentioned the existence of a core-periphery structure in all networks, and how

this reflects our data selection. As the core is based on a large amount of data we consider it

more informative about the past. This does not mean, however, that the less evidenced events

in the periphery exclusively result from processes in the present, like research focus and

selection through publication. Indeed, it are often the peripheral forms and sites that define the

difference between two wares’ distribution networks. To explore this dichotomy we should

examine the individual sites and forms in the periphery more closely. As a network analysis

graph groups vertices with a high similarity, it becomes easy to identify clusters of sites with

a comparable assemblage of tablewares. This is especially true for the periphery where

similarity is often based on the presence of just one or two sherds. As an example we could

consider two peripheral clusters present in all sites networks between 125 and 0 BC, further


50

referred to as clusters 122 and 223. Such clusters cannot be considered meaningful groupings

without question, but they should not be automatically discarded either. Exploration through

Pajek and the project’s database of the individual sherds that created these clusters can often

allow one to pass a judgement on such patterns. Although the sites in cluster 1 have evidence

of just one single form (EAA22A-B), their ceramic data was examined and published by

different scholars (Zahn 1904; Wintermeyer 1980; Kenrick 1981; Bommeljé & Vroom 1995;

Slane 1996a; Karivieri 2003). All sites in cluster 2, on the other hand, have evidence of two

ESA forms which are widely distributed (EAA22A-B and EAA3-4), but more importantly, all

of these sites were excavated by the same British team and the ceramics published in the same

volume by the same author (Kenrick 1981). The situation becomes even more interesting

when from 75 BC onwards the clusters start to fall apart due to an increasingly diverse

assemblage. It should be clear that such a pattern cannot be considered to reflect a distinct

group of sites which had an identical tableware consumption during this period. Rather, these

sites attest of the wideness of the two ESA forms’ distribution networks, as well as the sites’

similarity to other sites excavated by different teams. Moreover, these patterns indicate that

the recognition and publication of different forms is highly dependent on the knowledge and

aims of the ceramologist involved. A wide recognition of specific forms is highlighted by the

high k-cores, while the low m-slices recommend caution in interpreting these specific

clusters. Such processes resulting from the diverse ways in which archaeology is conducted

are not exclusive to the periphery. The core positions of some sites are often a reflection of

the substantial research efforts archaeologists have invested in them (e.g. Tel Anafa, Assos

and Paphos). This is not to say that the abundance and diversity of recorded tablewares is

meaningless; we consider all attested diversity to have been assembled for a reason. Rather,

archaeologists should take this overrepresentation of some sites or forms into account when

using complex datasets. Network analysis can, therefore, provide a powerful tool in exploring

ceramological datasets and identifying patterns that need further examination.

Having explored patterns and their origins in the periphery, we can now turn our attention to

the dense core. Our analysis in the previous section was often limited to listing those sites

with the highest diversity of popular forms, and forms with the widest distribution. Though

these patterns at the very centre of the core are more visible than others, they are not

22 Consisting of Amygdalea, Arikamedu, Asea Valley, Didyma, Glyfada, Priene, Qara Mazraa, Sykea, Tell Aar, Tell Akhtareine, Tell Banat, Tell Bararhite, Tell Berne (west), Tell Kadrich, and Tell Zaitane. 23 Consisting of Aazaz, Bab, El Aareime, Qara Keupru, Tell Hailane, Tell Kassiha, Tell Noubbol, Tell Rahhal, and Yel Baba.


51

necessarily more informative. During most of the time span analyzed ESA dominated the

cores of every network, resulting in other wares being mainly seen in relation to ESA and not

to each other. Although this would provide a detailed picture of ESA trade, we can argue that

looking at the dynamics between the distribution networks of different wares and the role

individual sites and forms play in them would lead to more interesting interpretations on

processes in the ancient past. The identification of sites where different distribution networks

overlap and where they do not overlap might help us understand the decisions being made

when distributing tablewares. Individual forms that have a different distribution from other

forms of the same ware might have been distributed by different people for different reasons.

This is where our three-part approach of analyzing networks of forms, sites as well as a

combination of both is particularly helpful. By confronting all three types of networks,

patterns emerging from each of them can be clarified.

To illustrate this we will provide an example derived from the analysis in the previous

section. One of the most intriguing patterns is evidenced in the relationships between ESA,

ESC and ESD distribution networks. The distribution of ESC and ESD seems to be taking

place largely within the ESA network. This situation becomes more interesting, however,

when we consider the, initially, limited number of sites on which ESC and ESD were co-

present. Throughout the periods analyzed, ESC was always the dominant ware in a small

number of sites, while ESD was, from its inception, largely distributed to major ESA sites.

We believe that this pattern is an indication of the existence of subnetworks within the

massive ESA distribution network. Moreover, these subnetworks are not evidenced in the

ESA forms’ distributions. This means that different processes were responsible for the spread

of the same ESA forms. This is hardly surprising, as the ways in which ceramics can be

transported, sold or exchanged are numerous. However, by identifying the specific sites and

forms that took part in these subnetworks, network analysis enables us to narrow the causes

down, facilitating the interpretation of such patterns. On the one hand, ESA tablewares were

traded or exchanged in one set of sites where ESC vessels were also available and sometimes

dominant. On the other, ESD was available in a different group of sites, completely adopting

a specific part of the core ESA distribution. The increasing number of core ESA sites with a

diverse assemblage attests of a third set of processes, ever more providing the entire ESA

distribution network with different types of tablewares. Whether vessels of these wares were

transported and exchanged by the same or completely different people or institutions cannot

be clarified at this stage, but we can state that they were aiming at specific and overlapping

markets to barter their goods.


52

In this discussion we have briefly illustrated how network analysis succeeds in identifying

structure in complex datasets, and aiding its interpretation. Exploring pottery datasets as a

network allows one to consider it as a web of patterns resulting from processes in the ancient

past as well as in the present through typological knowledge and data selection, whose

identification can be further refined through exhaustive querying of one’s data. The

interpretation of such processes, however, is largely outside the scope of this methodological

tool. As we mentioned at the beginning of this section, patterns are not necessarily

meaningful, and we would like to elaborate on this statement by stressing the wide variety of

processes that could result in such patterns. As such, we will not advocate a “covering laws”

approach to interpret structure. Rather, we will illustrate and discuss in chapters V, VI and VII

how networks can be placed in a wider geographical, archaeological and historical

framework, to demonstrate the diverse range of decisions that can create, maintain and evolve

the attested patterns.

CHAPTER V

METHOD AND ANALYSIS DISTANCE NETWORKS

TOM BRUGHMANS CHAPTER V METHOD AND ANALYSIS DISTANCE NETWORKS

54

5.1. Method

In this chapter we will explore the second network type introduced in chapter III: the

geographical network of distance, representing a hypothesis of shortest-distance trade routes.

Unlike the network of co-presence discussed in the previous chapter, this network of distance

is not simply an alternative visualization of our complex dataset. It represents a geographical

hypothesis in network form, while allowing it to be explored and tested using network

analysis. For this purpose, we will introduce a series of quantitative tools and briefly elaborate

on their meaning and application for this project. As the project’s method consists of a

significant number of tools and acronyms, a glossary (p. 100-102) was added with concise

explanations of methods and terms.

5.1.1. M-slices: nested arc values

Although we described the concept of m-slices in the previous chapter, its use and meaning

slightly alters as the networks of distance are directed, which means that we are now looking

at nested arc values instead of nested edge values. As a consequence, we can limit our

analysis to arcs arriving at a vertex (input), departing from a vertex (output) or both (all). We

decided to use the input method as it reflects the ceramic data attested for specific sites, while

the output and ‘all’ methods are more a result of the simulation. Input m-slices represent the

volume of pottery being transported to sites. The number of vessels being transported over a

route between two sites will serve as an indication for the strength of the relationship between

those sites, as well as the overall activity of a specific trade route.

5.1.2. Closeness centrality: reachability of a vertex

The use of centrality measures for analysing ancient transportation networks has already been

explored by Isaksen (2005; 2008). Unlike Isaksen, however, we wish to incorporate the

method in a more elaborate toolbox, contrasting and complementing its results with diverse

approaches.

As our networks represent the transportation of goods over trade routes between sites, we may

wish to understand how easily they are distributed, and what sites are more easily reachable

than others. The closeness centrality method builds on the idea that the closer a vertex is to all

other vertices, the easier information, goods or people may reach it, and the higher its

centrality. It is defined as “… the number of other vertices divided by the sum of all distances


55

between the vertex and all others” (Nooy et.al. 2005: 127; Pajek calculation based on

Sabidussi 1966).

Again, as our networks are directed we should make a distinction between input, output and

‘all’ closeness centrality. In this project’s distance networks, the output closeness centrality

represents the relative ease with which a site’s pottery can be transported to all other sites. All

closeness centrality, on the other hand, combines the input and output of vessels, and

therefore reflects how easy a site can be reached by all other sites, and vice-versa.

5.1.3. Betweenness centrality: vertex as an intermediary

A second centrality measure focuses on the idea that a vertex is more central if it is more

important as an intermediary in the network. The betweenness centrality of a vertex is defined

as the proportion of all shortest paths between pairs of other vertices that include this vertex

(Nooy et.al. 2005: 131). If the flow of goods between sites can be severely disrupted by the

removal of one site, then this site is a crucial go-between to the transmission of goods in the

network. Betweenness centrality will therefore provide us with a tool to measure the influence

and control individual sites exercise on the transportation of tabelwares.

5.1.4. Degree: number of arcs

Contrary to centrality methods, the degree measure only takes a site and its direct neighbours

into account. In a directed network, the outdegree of a vertex is the number of arcs it sends

(Nooy et.al. 2005: 64). Defining the outdegree for every vertex allows us to identify all

junctions in the trade routes, and distinguish between the number of coinciding trade routes.

5.1.5. Domains: number of vertices a vertex connects to

When goods are transported from site A to both sites B and C, the latter and all subsequent

sites are dependent on the first for their provision of goods. The number of sites connected to

site A serve as an indication for its domain of influence. The output domain of a vertex can be

defined as the number of all other vertices that this vertex connects to by a path (Nooy et.al.

2005: 193). A site’s domain therefore represents the number of sites for which tablewares are

evidenced that were transported through this site. Although this measure is implied by the

betweenness centrality, the exact number of sites in a site’s domain is an interesting measure

for comparison between sites. Moreover, it helps us understand and compare sites where

routes diverge, i.e. sites with an outdegree of more than one.


56

5.1.6. Understanding the results

Now that we have filled our arsenal with diverse and useful analytical weaponry, we should

reflect on the results they produce before we start attacking the networks. It could be said that

most of the methods described tell us something about a part of the beta-skeleton selected for

these networks more than the ceramic data themselves, while only the m-slices take the

transported volume into account. Both aspects are equally important, one informing us about

the structure of the shortest trade routes, the other about the most evidenced trade directions.

A balanced exploration of the proposed trade routes should therefore explain structural

aspects in the light of transported volumes and vice-versa.

Although all of these tools will serve in exploring the geographical networks, we should not

lose track of a more important aim: testing the hypothesis. In order to do this by comparing it

with the networks of co-present forms, however, we should select methods that provide

comparable results and examine similar aspects of the complex dataset. No single method will

serve this cause, and we will explain in the following chapter how a combination of tools

allows for individual patterns on the networks of distance to be validated or discarded.


57

5.2. Analysis : distance networks

Having constructed a series of networks that embody the hypothesis of shortest distance trade

routes, we will explore the structure of these networks using the methodological tools

described in the previous section. The networks resulting from this analysis and which are

discussed below, are available from the project’s website24 and digital archive25. Please refer

to the index and map of sites in appendix A for all the sites mentioned in the analysis.

5.2.1. Eastern Sigillata A

As most ESA forms had a very wide distribution, it is no surprise that their transportation

networks are very dense (fig. 10). However, one distinctive pattern is very clear in every

analysis. Two major trade routes extended outwards from the producing region, one going

westwards until Siphnos and another going South to Mamphis. All the sites in between belong

to the highest m-slice (m=81), indicating that a large volume of tablewares was transported

through these sites. While sites in the producing region occupied the best positions to

distribute ceramics (high output closeness centrality), sites along the major trade routes (until

Samos in the West and Hippos-Sussita in the South) and on eastern Cyprus were more

important as exchange centers (high betweenness centrality and all closeness centrality). The

most active area of ESA distribution and exchange, according to the current hypothesis,

should therefore be identified as its producing region and sites leading immediately south-

and westwards. Sites on Eastern Cyprus seem to supplement the two major trade routes, by

providing alternative routes between Tarsos and Berytus via Kition, and from Leukos Limen

to Anemorion via Salamis.

We should also mention the ambiguous structural position of Leukos Limen, a producing site

and therefore an important distribution centre (high output closeness centrality and output

domain). As the major trade routes provided shorter paths to consuming sites, however,

Leukos Limen was less important than other producing sites as an intermediary in the

transportation of ceramics (low betweenness centrality and m-slice value).

5.2.2. Eastern Sigillata B

From the producing site of Ephesos, a route with relatively high volumes extended to the west

via Siphnos and beyond to Crete and the Greek mainland, while a second route with a

24 Networks: http://mapserver.arch.soton.ac.uk/networks/distance.html ; webmaps: http://mapserver.arch.soton.ac.uk/networks/map.html . 25 In digital archive : root/networks/distance ; and root/spatial data/shapfiles.


58

significantly smaller volume continued east- and southwards to Myos Hormos and Meroë

(fig. 11). Apart from transporting the highest volume of vessels (m=2), the routes between

Anemorion and Isthmia were also the most important sites in the ware’s distribution (high

closeness centrality). Serving as a bridge in the distribution towards Crete and the Greek

mainland, Siphnos was the most important ESB distribution centre in the Aegean, with a

closeness centrality that rivaled that of Ephesos. We can therefore conclude that routes in Asia

Minor and the Cyclades supported the bulk of ESB trade.

Sites on the eastern and southern route, however, were important intermediaries in ESB trade,

which can be concluded from the high betweenness centrality and all closeness centrality

values. As we know that this route is largely a construction of the Beta-skeleton we selected

for the analysis, the above statement should be validated through comparison with the entire

dataset: were these sites, for which no ESB sherds are attested, important intermediaries in

eastern tableware trade? Providing an answer to this question will considerably improve our

understanding of the validity of our hypothesis.

5.2.3. Eastern Sigillata C

From the producing site of Pergamon, all ESC vessels were transported along three major

routes: one going north along Assos, another heading south and west branching out at

Siphnos, and a third route with a smaller volume heading east (fig. 12). Large volumes were

transported along the south-western route until Siphnos and along the northern route towards

Troy. An enormous amount of ESC vessels were found in Assos, however, resulting in a very

high volume of trade between Pergamon and Assos. Lower volumes were transported from

Siphnos to Argos and Alexandria, and along the eastern route towards Cyprus and Tel Anafa.

Sites along the eastern and, especially, along the south-western route were central to the

transportation of ESC vessels. Siphnos, Anemorion, and (to a lesser extent) Kition were

important junctions in this ware’s trade, serving as essential go-betweens for a large number

of sites. Although the northern route was used to transport large volumes of ESC vessels, it

was less important in the ware’s distribution as it connected fewer sites (low betweenness

centrality, output domains, and output closeness centrality values).

5.2.4. Eastern Sigillata D

Three major trade routes can be distinguished, each leaving the producing region (Cyprus)

through a different site (fig. 13). From Kition, a large volume of vessels is transported

southwards to Mamphis, where the route branches off to Myos Hormos and Nessana. A


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western route departs from Salamis/Constantia, transporting a significantly lower number of

vessels towards Knossos, and further west to Carthage and Lepcis Magna through Siphnos

and Antikythera respectively. A rather small volume of ESD sherds attests of a third route,

connecting Ayios Philon on Cyprus with North-Syria and Antioch.

Kition, Salamis/Constantia and Ayios Philon were important distributing centers (high output

closeness centrality), while Siphnos acted as an important junction on the western route.

Unlike Salamis/Constantia and Kition, Ayios Philon was less crucial for the overall ESD trade

flow (low betweenness centrality), as it is not directly connected to the western and southern

routes. Over these last two routes, the bulk of the attested ESD sherds seem to have been

transported, and the sites along these routes are therefore relatively central in this ware’s

distribution and exchange.

In conclusion, although Western-Cypriot sites have high volumes of ESD sherds and occupy a

central position in their distribution, it are the sites in the east of Cyprus, according to our

hypothesis, that act as the gates to the mainland. Through these sites, ESD vessels were

transported to western and southern consuming or redistributing centers and, to a lesser

extent, North-Syria.

5.2.5. Italian Sigillata

This ware was produced in the Western Mediterranean, for which our dataset provides a very

limited amount of information. As such, the ITS producing centers were reduced to a single

point in Central Italy, which is connected to only two other sites, Carthage and Sykea (fig.

14).

Although a relatively large volume of ITS is attested in Carthage, other sites belonging to this

south-western route have a very low volume. As it is only connected to the rest of the ITS

network through the producing region, this route was irrelevant for the attested distribution of

ITS further east. From Sykea, on the other hand, a number of routes branch out, the most

substantial of which passed by Kallydon and Alexandria to Myos Hormos and Jerusalem.

Also heading through Kallydon is a slightly less evidenced route that passes the Greek

mainland and Cyclades to reach Asia Minor. A very small number of ITS vessels were

transported over a northern route to Cyprus and, eventually, Arikamedu in India. Sites on the

latter route have a relatively high output closeness centrality and betweenness centrality,

which is largely caused by the sheer number of sites along this route. Sites along the other

routes are less central, and only sites where different trade routes meet, like Sykea, Kallydon,


60

Isthmia and Salamis/Constantia, can be considered to occupy an important position in the

redistribution of ITS.


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Fig. 10: geographical representation of the ESA distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).


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Fig. 11: geographical representation of the ESB distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).


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Fig. 12: geographical representation of the ESC distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).


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Fig. 13: geographical representation of the ESD distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).


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Fig. 14: geographical representation of the ITS distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).


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5.3. Discussion: combined networks of distance

In the previous section we identified the major trade routes and important centers in the

transportation of individual wares, given the hypothesis that tableware vessels would always

have been transported over the shortest path. But how did these distinct networks relate to one

another? Were the major trade routes and important centers the same for all wares? When we

combine all networks of the individual wares, we get an impression of all active trade routes

in the tableware trade that took place in the Eastern Mediterranean between 50 and 25 BC.

This combined network is, not surprisingly, identical to the beta-skeleton that embodies the

hypothesis (fig. 5). In this discussion, we will provide a summary of the previous section and

look for overlapping structural positions of trade routes and sites. The combined network will

be analyzed, and the contribution of the individual wares to this network will be evaluated.

The above description of the individual wares’ trade routes suggests some very similar

patterns. Producing centers or regions transport their wares over a limited number of major

trade routes, from which local routes branch off. These major routes are often the same for

different wares. The path from Anemorion in the east towards Siphnos in the west was used

for the transportation of large amounts of ESA, ESB, ESC and, to a lesser extent, ESD

tablewares. In addition, both Siphnos and Anemorion occupy a structurally favourable

position for the redistribution of these wares, while most sites along the way would have

contributed to the route’s active trade. The same can be said of a southern route between

Apamea and Mamphis, over which mainly ESA, ESD and some ESB vessels were

transported. Its position on the hinge between these routes, allows Cyprus to play a

supplementary role in the transportation of most wares. These major routes and the

redistribution role of its sites are also reflected in the combined network, although the general

direction of trade is largely defined by the large amount of ESA data. In this overall network,

the northern route from Pergamon to Sykea also occupies a surprisingly central position

through its connection to Central Italy, while the southern route to Mamphis is less important

as a passageway. The role of East-Cyprus as an alternative route is emphasized by high

centrality scores, being preferable to North-Syria for the transportation of tablewares south-

and westwards. As we will discuss in the next chapter, however, this role suggested for

eastern Cyprus is purely a result of the hypothesis.

Though pottery from the four Eastern Sigillata wares was transported over similar routes with

similar key centers, their core region of distribution is very distinct. The majority of all wares’

vessels were transported to sites in the proximity of their distribution centers. For ESC and


67

ESB, the Aegean can be considered the main market, while ESD vessels are present in large

quantities on Cyprus and along the southern road on the Levantine coast. ESA, on the other

hand, is not restricted to its producing region and provides very large quantities of its pottery

to most sites along the major trade routes between Siphnos and Mamphis, as well as Cyprus.

Unlike the Eastern Sigillata wares, ITS was hardly transported along routes between Siphnos

and Mamphis. Its most substantial route ran over the Greek mainland and Crete to the

northern and eastern African coastlines. Its most important redistribution centers, Sykea and

Kallydon, play only a minor role in the other wares’ transportation.

These patterns are obviously largely the construction of our hypothesis: the beta-skeleton

provides only few alternative routes, the shortest path condition makes the selection of a

major route quite simple, while the number of connections of a site (outdegree) influences its

role in the transportation of tablewares. The analysis of these networks is also highly

influenced by our data selection. For example, clusters of geographically neighbouring sites

(e.g. North-Syria) are often topologically distant, which affects the closeness centrality

measure. It should also be stressed that we purposefully restricted the analysis to one possibly

influential factor, namely distance, and ignored all other features (environmental, political,

social, economical, personal, …) that determined the decisions to take one route or another.

As such, the results of this chapter’s analyses should not be considered an identification of

trade routes in the Roman East. The addition of our ceramic data, however, does result in a

surprisingly detailed description of what trade routes might have been like if distance was the

only factor to be taken into account. We can, therefore, conclude that network analysis

provides a suitable tool for visualizing and exploring a spatial hypothesis. In the next chapter

we will evaluate its use for testing such a hypothesis, by confronting the distance networks

with the networks of co-presence, and placing our results in a wider archaeological and

historical framework.

CHAPTER VI

TABLEWARE TRADE IN THE ROMAN EAST: 50-25 BC

“This frankly speculative and theoretical essay has attempted to avoid the prison of conventional

interpretations of limited data samples, in order to explore alternative possibilities. Only when the range of alternative models has been defined may

we usefully test between them in a series of carefully selected, detailed analyses on restricted data.”

Clarke 1978: 35.

TOM BRUGHMANS CHAPTER VI TABLEWARE TRADE IN THE ROMAN EAST: 50-25 BC

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6.1. Testing the hypothesis

In the previous chapter we visualized and analyzed the hypothesis that tableware pottery was

always transported along the shortest paths, considering distance the most influential factor

for explaining the attested tableware distributions. In this chapter we will test this hypothesis

by confronting it with the ceramic data, in the form of the networks of co-presence introduced

in chapter IV. Moreover, we will place these networks and the results of our analyses in a

wider archaeological and historical framework, complementing a networks approach with

previous approaches to examining the same topic. This might allow us to refine our results

and propose a model of tableware distribution in the Roman East for this short period of time.

We should, however, briefly elaborate on how these two different network types can be

compared. Although some of the methodological tools used are the same for both types, they

represent completely different aspects of the data and do not allow for a quantitative

assessment. Therefore, our focus will lie on comparing the most obvious patterns in both

network types. The presence or absence of local patterns and general structure, evidenced in

the volume and diversity of our ceramic dataset, has a reason of some sort. By confronting

both network types, we will evaluate to what extent this reason was influenced by distance.

When distance is considered the most influential factor, then sites with a high diversity and

volume should have been supplied with tablewares along the shortest route possible, and vice

versa. As this situation is optimally represented by our networks of distance, we will be able

to evaluate whether distance provides a suitable explanation for the assemblages attested at

sites in the eastern Mediterranean.

The networks discussed below are available from the project’s website26 and digital archive27.

Please refer to the index and map of sites in appendix A for all the sites mentioned.

6.1.1. Eastern Sigillata A

Proximity to the producing centers seemed to have been an important factor for sites with a

low diversity of tablewares in which ESA was the dominant ware: Apamea, Gindaros, Petra,

Jerusalem, Nessana and Epiphaneia. All these sites occupy structurally strong positions along

the Levantine route, through which ESA vessels could easily reach them. Levantine sites with

a higher diversity of tablewares but a majority of ESA, like Jerusalem, Tel Anafa and 26 Co-presence: http://mapserver.arch.soton.ac.uk/networks/co-presence.html ; distance: http://mapserver.arch.soton.ac.uk/networks/distance.html ; webmaps: http://mapserver.arch.soton.ac.uk/networks/map.html . 27 In digital archive : root/networks/distance ; and root/networks/co-presence


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Samaria-Sebaste, can be considered important centers in the distribution of most tablewares.

This is only weakly reflected in the ESA transportation network, and we would like to suggest

a more central position for these sites, replacing Mamphis as an important junction and

connecting to sites like Odoba, Nessana and Petra. Of the four sites that presumably produced

ESA vessels, only Antiocheia ad Orontem and Tarsos occupy a strong position on the

transportation networks. The ceramic evidence confirms such a situation, as Tarsos and

Antioch are major ESA centers with little diversity in wares, while Leukos Limen and Küçük

Burnaz have a very small and uniform assemblage. Although a detailed study of ware

producing centers lies far beyond the scope of this project, based on our data it seems that

Antiocheia ad Orontem and Tarsos might have been the main distributors of ESA tablewares.

The ESA distance network suggests an alternative connection between the western and

southern routes via Cyprus. The course of this route, however, is not supported by the data

available. Moreover, it assigns relatively minor roles to sites which were important centers in

ESA trade, like Anemorion, Paphos, and Amathous. Connections from Anemorion to Paphos

and Amathous, and further to Apamea and Epiphaneia on the Levantine coast, seem more

plausible.

Although the location of Samos and Ephesos as important centres in the Aegean is feasible,

their connections further west are less certain. Being on the crossroads of two diverging

routes, Siphnos should have had some role to play in ESA distribution. Compared to sites like

Athens, Corinth and Kenchreai, however, Siphnos has a very small and uniform assemblage.

The data suggest that the Greek mainland and Crete (with Knossos and Gortyn) were more

central to ESA distributions, and indeed the entire tableware trade, which should be reflected

in a model of ceramic transportation.

6.1.2. Eastern Sigillata B

The analysis of the ESB distance networks revealed a focus on Aegean sites, a situation which

is confirmed by the ceramic evidence, and which can be explained by the proximity to the

ware’s producing centre of Ephesos. Although the routes between these sites seem feasible for

Asia Minor, the situation on the Greek mainland and Cyclades is doubtful and requires a

different interpretation. According to the hypothesis, Siphnos was an important centre in ESB

trade as it could be easily reached from Ephesos and vessels could be redistributed rapidly to

a large number of different sites. Siphnos has no evidence of ESB however, and contrary to

sites like Corinth, Kenchreai and Athens on the mainland and Knossos and Gortyn on Crete, it


71

does not seem to have played a significant role in the distribution of any ware. Transportation

of ESB in this area was likely to have been centered around the latter sites, and therefore the

ESB distribution network is closely related to the western branch of the ESA network.

Given the similarity with ESA transportation patterns in the west, it is not unlikely that the

ESB vessels found in Myos Hormos and Meroë were transported via the Levantine route

mentioned above. Given the focus of ESB in the Aegean and the introduction of ITS

tablewares being transported west to east, we cannot exclude a route via Crete and the

northern Egyptian coast either. We will discuss this issue in more detail in the next section.

6.1.3. Eastern Sigillata C

The predominantly ESC assemblage of some sites can definitely be explained in light of the

hypothesis: for Troy, Apollo Smitheion, Ephesos and Assos, proximity to the producing

centre of Pergamon is a valid explanation. Moreover, apart from Ephesos these sites seem to

play a role of limited importance in all other wares’ distribution networks. Anemorion, being

geographically distant but topologically close, occupies a favorable position in the ESC

transportation network which is validated by the ceramic evidence. Such an explanation does

not apply for other sites central to the ESC distribution network, however. The position of Tel

Anafa on the end of the eastern ESC trade route contrasts with its diverse assemblage, which

should be interpreted within the combined network of distance. In the Aegean we encounter a

similar situation as for the previous two wares. Argos and Athens, although being

geographically close to Pergamon, have a structurally weak position, while Siphnos where no

ESC was found functions as a redistribution centre. For these sites the hypothesis does not

provide a suitable explanation. Alexandria offers an interesting issue, as it is not only

peripheral in the ESC transportation network, but in that of all wares. Even if we were to

consider Alexandria an important consuming centre, we would still expect a more central

position to provide it with the tableware assemblage evidenced. As such, the networks of

distance do not succeed in explaining Alexandria’s assemblage, which will be discussed in

more detail below.

6.1.4. Eastern Sigillata D

For its producing region of Cyprus, the ESD distance networks showed some interesting

patterns. Sites on eastern Cyprus (Ayios Philon, Salamis/Constantia and Kition) were

considered bridges between the island and major trade routes on the mainland. For these sites

hardly any ESD pottery is attested, while Paphos on the western part of the island has a very


72

high diversity and volume of the ware. Although we do not wish to make any statements

about the producing region of ESD, we should mention that Paphos might be considered as

the main distributing site on Cyprus. Together with Amathous, and to a lesser extent Panayia

Ematousa, Paphos was also an active centre in ESA trade. This situation evidenced from the

pottery assemblages therefore contrasts with the distance hypothesis, moving the focus of

tableware trade routes from the east to the west of Cyprus.

As Knossos is central in the distribution networks of most wares, we consider its weak

position at the end of an ESD trade route doubtful. It might take over the favorable position

ascribed to Siphnos, which has no evidence of ESD vessels. Although Knossos could have

been a consuming centre, we believe that its position in all distribution networks is indicative

of the active role it played in their distribution. The diverse assemblage from Tel Anafa, on

the other hand, can partly be explained by its position on the Levantine route, although an

even more central position in the combined tableware trade would be expected.

6.1.5. Italian Sigillata

Although ITS tableware found in Carthage is abundant and diverse, we cannot examine its

position in the ware’s transportation given the geographical limitations of our dataset. As for

the ware’s eastern distribution, hardly any aspect of the distance network created by the

hypothesis can be backed up with ceramic evidence. The most important issue involves the

connection with the producing region. For now, all the eastern sites are provided with ITS

pottery through Sykea, highlighting Sykea’s as well as Kallydon’s importance in the ware’s

transportation. For these sites hardly any ITS was found, while Corinth, and to a lesser extent

Knossos, Athens and Gortyn, are among the most central sites in these first stages of ITS

distribution. It seems more likely to consider one of these sites as having maintained

connections with Central Italy and possibly Carthage.

Another issue concerns the route to the eastern sites in which ITS was found: Myos Hormos

and Arikamedu. Although the shortest route via Knossos and Alexandria is consistent with

these sites’ assemblages, based solely on our evidence we cannot exclude a southern route

starting at Carthage, or a route that adopts the established ESA transportation network in the

east.

This discussion confirmed Slane’s (2004: 32) statement that ITS distribution in the east

cannot be explained by geographic proximity to Italy. In the following sections we will build


73

upon this idea and make some suggestions to improve our understanding of the attested ITS

distribution patterns.

6.1.6. Conclusion: combined transportation networks

The above evaluation of the transportation networks indicates that a short distance between

sites and the proximity to the producing centers can be considered an influential factor, that

can serve to explain some patterns evidenced in the ceramic dataset. We were also able to

indicate some sites and wares for which distance was not a valid explanation, notably

Alexandria, most of the Aegean and the connection with the Western Mediterranean. Here,

we will briefly summarize our results and discuss how this influences our knowledge of the

combined transportation networks.

In our discussion of the combined networks of distance we identified several major trade

routes. Although this statement is to some extent validated, the ceramic data seem to indicate

that some modifications are necessary. The Levantine route connects sites which were mainly

active in ESA and ESD trade, although some sites seem to have played some role of

significance in the overall tableware trade, which are underrepresented in the transportation

networks. Based on the networks of co-presence, we suggest that a more centralized position

should be assigned to Jerusalem, Samaria-Sebaste, and Tel Anafa, being the main centres of

tableware trade, and connecting to other sites over secondary trade routes. The northern part

of this Levantine route is less accurately represented by the networks of distance. As we

suggested above, Paphos, and to a lesser extent, Amathous should be considered the hub of

tableware trade in Cyprus. Such a shift of trade routes from eastern Cyprus further west would

mean that the island’s connections with the mainland should be reconsidered. Possible

candidates for such connections would be Anemorion, Epiphaneia, Apamea, Tarsos and

Antioch, although any decision on this matter should be motivated by a more thorough

archaeological and historical study.

The route running west over Asia Minor and the Cyclades also requires further examination.

The above discussion made it clear that Siphnos had only an insignificant part to play in

tableware trade, hinting that its strong position in transportation networks might be overrated.

Instead, we would like to suggest a focus of tableware trade for the Aegean on sites like

Athens, Kenchreai, Corinth, Knossos and Gortyn. Another issue we should raise is

Pergamon’s position on this route. Although the site was evidently important in ESC trade,

based on the data we cannot find any reasons why it should be central to the overall tableware


74

trade. Instead, sites with a more diverse assemblage like Assos or Ephesos might replace

Pergamon’s strong position in the transportation networks.

As we illustrated, some issues cannot be resolved by analyzing and comparing our different

network types. The position of Carthage, the distribution of ITS, the route towards Egypt via

Crete and the far southern and eastern routes feature among these problems. Most of these

largely result from the geographical and thematic limitation of our data, resulting in peripheral

distortions that might be considered “edge effects”. Any interpretation on these routes,

however, will seriously influence a model of eastern tableware transportation. Before we can

apply the changes we discussed in this conclusion, we should therefore place our data in a

wider archaeological and historical framework. By doing this the benefits and disadvantages

of a networks approach compared with other methods and their complementary nature will

become clear.


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6.2. Discussion: the bigger picture

When we consider distance to have been the only factor influencing decisions made in the

distribution of tablewares, some of the patterns identified do not seem to make any sense.

These issues confirm that pottery exchange mechanisms functioned as a dynamic system,

with many small-scale actions leading to the general patterns we see in the archaeological

record. One of the key issues for understanding these mechanisms is the relationship between

east and west, which is evidenced in ITS pottery being transported from Central Italy all

around the eastern Mediterranean. The presence of this ware in sites like Corinth, Knossos

and Alexandria, for example, cannot be understood without considering their political

situations and physical positions. In the following discussion we will touch upon several

aspects of this Mediterranean system, to complement our results drawn from network

analysis.

So how were ITS vessels transported to the east? We already mentioned that Sykea’s

connection to Central Italy is not supported by the data. Stobi’s assemblage and its position

along the Via Egnatia, on the other hand, might be indicative for its engagement in eastern as

well as western distribution mechanisms (Slane 1996b; Bes 2007: 45-46). The diversity of

ITS forms found in Corinth, however, might be evidence of this city playing “an intermediary

role between west and east” (Bes 2007: 44), starting in this period which marked Corinth’s

refounding (44 or 42 BC) after its destruction in 146BC. But was Corinth a bottleneck

through which western tablewares were distributed in the east? Significant diversity of ITS

was found in Berenice in the Cyrenaica, for example. Although our distance network suggests

an itinerary over Greece and Crete, we cannot exclude the route between Carthage and the

Cyrenaica along the North African coast. There are some issues surrounding these last

connections, however. Given the prevailing winds and currents, sailing along the North-

African coast in sight of land would have been very dangerous, running the additional risk of

being driven into the dreaded Gulf of Sirte (Fulford 1989: 171). Although a route from the

Cyrenaica northwards to Crete and the Peloponnese would challenge the northerly winds to

some extent, this route would allow sailors to stay in sight of land for the bulk of their voyage

(Horden & Purcell 2000: 127, map 9). Moreover, the Roman annexation of Cyrenaica in 74

BC was followed by the region being combined with Crete in a single proconsular province.

This suggests that connections between both regions on an administrative level must have

been strong, which makes the existence of a direct major route of transportation even more


76

probable. Therefore, we consider it most likely that the bulk of Italian tablewares reached

Berenice via Crete.

Although this confirms our previous statement declaring Corinth as an intermediary between

east and west, the situation in Greece itself is not clarified. Indeed, as Fulford (1989: 179-180)

argues, the Italian wares found in the Cyrenaica would initially not have been aimed at this

region’s market, and they might have come from a “trading pool” in Greece. With its large

number of major urban centres like Athens, Corinth, Knossos and Ephesos, connections of

any sort within the Aegean would have been dense. Sailors would always be able to stay in

sight of land, and island hopping would mean safety and ease of navigation while

significantly reducing chances of being caught by the elements (Horden & Purcell 2000: 133-

135, 140-142). Connections between Asia Minor and the Greek mainland would almost

certainly have passed through the Cyclades, but the identification of major centers along this

route remains problematic. We mentioned in chapter IV that Delos’ assemblage does not

allow us to ascribe it a more than average role in tableware trade connections, and its second

destruction in 69 BC does not contradict such a statement. Moreover, our dataset does not

provide us with alternatives to Delos for this position in tableware trade, apart from Samos

which was more related to the ESC and ESA patterns along the west coast of Asia Minor. We

can therefore conclude that, although the ceramic evidence leaves us in the dark about the

important centers, the natural features and assemblages in the Aegean suggest strong

connections between its major urban centers, which should be reflected in a model of this

period.

The identification of an eastern route along Asia Minor providing the Aegean cities with ESA

tablewares needs some clarification as well. To evaluate this route, however we will have to

look at Alexandria’s situation first. As we mentioned before, the city’s assemblage clearly

indicates a strong connection with the west and a central position in the tableware trade of the

east. A reason for this might have been the transportation of Egyptian grain westwards, a

trade that worked both ways and explains the Italian tablewares in Alexandria. But how was

Alexandria connected to the west? Our distance networks suggest a route over Crete, which

would again confirm the role Aegean sites played in connecting east and west. Moreover,

Fulford (1989: 171) mentions that a route between the Cyrenaica and Alexandria would have

been dangerous in both directions, because of the prevailing winds. He elaborates on this

statement by indicating that the ceramic evidence does not attest to regular connections

between both regions, and indeed, that there was little need for them as both regions’ major


77

export good was grain (1989: 180). A route over Crete to Alexandria is therefore most likely,

but we should mention that this might largely have been a unidirectional flow. For voyages

from the east to the west a northern route would be preferred. Major shipping lanes identified

by Pryor (1988)28, mostly based on winds and currents, suggest a route from Alexandria to the

central Levantine coast, and further north to Antioch and the Cypriot south coast. Such a

course would indicate that Alexandria was the major port through which Levantine cities

were provided with Italian and Aegean wares. This interpretation of Alexandria as a pulling

and redistribution centre in the east was also noted by Bes (2007: 106) who, on the other

hand, saw Alexandria as playing a major part in providing the Aegean with ESA pottery as

well. Based on the ceramic data and given the scope of this project we can only confirm that

such a direct involvement of Alexandria in the Aegean is a possibility. A northern route

cannot be excluded, however. From Cyprus and Antioch, shipping lanes ran west towards the

Aegean. Although Horden & Purcell (2000: 139) stress that this northern route should not be

overpraised, giving the example of the challenging coastlines along Palestine and south

Anatolia (but see also the existence of cabotage in this region: Hohlfelder & Vann 2000), it

would provide a more suitable explanation for the assemblages of sites like Paphos,

Anemorion, Knossos and Epehesos. A northern sailing route would also solve another issue

by removing Pergamon’s central position in the overall tableware transportation network.

Finally we should discuss the Levantine route along which a large part of the ESA vessels

might have been transported. Due to a large number of sites and sherds from this region being

included in the database, the real complexity of pottery distribution in the past begins to

surface. The southern Levantine coast was dotted with ports, some of which attest of a

redistributive role in ESA trade. Although short voyages between these ports definitely

contributed to the tableware distribution along the Levantine coast, it was the urban centers

further inland with a high diversity in wares and forms that seem to have been the focus of

tableware trade. Sites like Jerusalem and Tel Anafa seem to suggest that there were strong

links with coastal sites which provided them with Italian and Aegean vessels, as well as an

elaborate overland network of consuming and redistributing cities in which they played a

central role. Further north the assemblages become more uniform, and can be explained

through more or less direct contacts with the ESA producing sites, possibly Tarsos and

Antioch. As we explained above, however, we can only suggest possible routes through

28 See also their evaluation in Horden & Purcell 2000: 137-143.


78

which the large amount of ESA pottery attested in the Aegean reached its destination: a

southern route via Alexandria or a northern route via West-Cyprus and Anemorion.


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6.3. Conclusion

6.3.1. A model of connectivity in tableware trade

In the previous section we confronted the results of our analysis with a wider archaeological

and historical perspective. This allowed us to clarify some issues network analysis could not

provide answers for, and make some suggestions on the tableware distribution system in the

eastern Mediterranean during this short period of time. We consider Corinth to have been an

important intermediary in contacts between the east and the west. The Aegean at large,

however, had a pivotal role within the eastern Mediterranean tableware trade. Through its

major urban centres (especially Knossos, Athens and Ephesos) ITS and ESC was transported

south to the Cyrenaica and further east to Alexandria. The latter city itself had a key position

in the Middle East, as it was probably the focus of trade efforts through which ITS reached

the southern Levantine cities. ESA distribution in the Levant indicates that strong connections

existed between its cities, which were focused on a limited number of more central sites. A

northern route connecting the ESA producing region and the Aegean, completes the picture

that resulted from our successive analyses. Figure 15 shows how this new model differs from

the original hypothesis of shortest distance trade routes (interactive versions of these networks

are available on the project website29).

Two key points of the resulting model require some explanation. Firstly, it should be clear

that we only tried to model the patterns that were most obvious in our networks. This

inevitably results in a generalizing image in which distance, environmental features and the

specific dataset we used are highly determining factors. Therefore, it seems that we have

fallen for the “glamour of high commerce” as Horden & Purcell (2000: 144) call it. This is

mainly due to the aims and scope of the current project, which did not allow for more than a

recognition of the existence of processes on different scales (like redistribution on urban,

household or individual level). However, we do wish to stress that the analysis of our

complex pottery dataset resulted in the identification of large- and small-scale patterns, whose

interplay is not in the least less complex than the dataset itself. Such patterns result from the

wide variety of ways in which ceramics were transported, of which evolving sporadic contacts

between secondary ports, or cabotage, was an invaluable part (Horden & Purcell 2000: 144).

In summary we agree with Horden & Purcell (2000: 142) that the “conceptual gap between

potentially all-round communications [as it is manifested in their ‘connectivity of

29 See especially the regional webmap: http://mapserver.arch.soton.ac.uk/networks/webmap.html .


80

microregions’ (ibid. 2000: 123)] and the restricted shipping lanes [as in Pryor’s approach

(1988)]” can be bridged by examining such small-scale casual processes as cabotage. These

should be identified through a variety of approaches, four of the most promising of which are

landscape archaeology, urban archaeology, shipwreck archaeology and pottery analysis

(Kingsley & Decker 2001: 14). Network analysis should be seen as the tool that can connect

the dots between these approaches, to identify and explore patterns evidenced in them.

Secondly, we should discuss the nature of the connections identified. Given our previous

point, we cannot assume that these are accurate reflections of physical trade routes that were

active in the past. So what do they represent if not the transportation of tableware vessels? We

do believe that the patterns inherent to the archaeological record evidence of relationships of

some sort between communities in the past (Fulford 1989: 173). From the previous discussion

it seems as if the economic nature of such connections came as a product of other factors, an

interpretation that mainly results from the archaeological discipline’s reliance on proxy

evidence rather than from the archaeological record itself. Although the dynamics of the

Roman economy might be a much desired goal for many archaeologists and ancient

historians, we must acknowledge that processes of an economic, political, individual and

environmental nature all contribute to shaping even the economic relationships in the past. A

networks approach allows us to identify this connectivity and forces us to recognize that

relationships of some sort existed between (micro)regions, cities or even individuals. From

this connectivity and considering the complexity of the processes involved (Davies 2005) we

can infer major trade flows with some confidence. We should, however, not be fooled in

thinking that studying these in isolation will bring us any closer to understanding the past

(Horden & Purcell 2000: 144-146).

6.3.2. Network analysis for complex pottery datasets

Throughout the last three chapters we have explored the archaeological application of

network analysis by applying it to a complex pottery dataset. As archaeologists rely heavily

on ceramological evidence, the processes in the past and the present that led to the creation of

pottery datasets should be identified before any conclusions about the ancient past can be

inferred from them. We can conclude that the method’s topological approach succeeds in

identifying large- and small-scale patterns of relational, geographical or temporal

significance. These patterns are visualized in structured graphs that encourage visual

inspection. Moreover, a range of analytical tools can be used to examine the different

structural aspects these patterns consist of. It can highlight areas of research focus and


81

underrepresented topics, as well as major processes in the past attested in large volumes of

data. As such, the main contribution of network analysis to pottery studies and the

archaeological discipline at large, is its potential for initial data exploration.

We have argued that general structure consists of local patterns which result from a variety of

processes. The identification of patterning is therefore meaningless if we do not understand

how and why it came to be. Although the network analysis process allows for exhaustive

querying of the primary data, its use for interpreting structure is fairly limited. However,

network analysis is complementary to a fundamentally interdisciplinary approach to the past,

and the method’s potential for combining different types of material culture will prove

invaluable in light of the current trend of increasingly large and complex archaeological

datasets.


82

Fig. 15: geographical representation of a model of tableware distribution for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).

CHAPTER VII

DISCUSSION: ARCHAEOLOGICAL NETWORK ANALYSIS

TOM BRUGHMANS CHAPTER VII DISCUSSION: ARCHAEOLOGICAL NETWORK ANALYSIS

84

7.1. Exploration, visualization and confirmation

Resistance, residuals, re-expression and revelation. Throughout the last three chapters we

have seen that these four major themes in exploratory data analysis (introduced in section 1.2)

also apply to network analysis. Does this mean that network analysis is just another technique

in the EDA toolbox? It should be clear by now that to some extent it is and to some extent it is

not, therefore we will have to look further than “yes” or “no” when answering this question.

Like EDA, network analysis succeeds in isolating patterns in the data and revealing them to

the analyst. The general trends are most obvious in both visualization and analysis, which are

only to a limited extent influenced by outliers (resistance). However, all data points are

included in both visualization and analysis, and can be carefully examined (residuals). Visual

inspection of networks forms an integral part of the method, and new networks can be drawn

to highlight different aspects of their structure (revelation). But the similarity with the EDA

principles falls short when considering re-expression, which aims at finding the right scale

that would simplify the analysis of the data (Hoaglin et.al. 1983: 3). It was our goal to

confront archaeological datasets in all their complexity, and to analyze their inherent

interactions directly. Looking at the resulting networks suggests that we have succeeded in

representing a web of meaningful interactions, but in no way can it be described as

“simplifying”. Only the most general structure is immediately apparent, while subsequent

analyses reveal local patterns. Networks do not “force their messages upon us” like EDA

techniques do (Tukey 1977: vi). This problem can only partly be resolved by resorting to

interactive media for visualizing complex networks (e.g. the Scalable Vector Graphics (SVG)

and webmap on the project website30). Another significant difference is network analysis’

ability to examine both multivariate (two-mode networks) and univariate (one-mode

networks) datasets, while EDA is largely concerned with exploring a single variable through

its displays. Even as a multivariate method does network analysis score poorly on simplicity

of representation, for which other multivariate techniques are preferable (Baxter 1994). We

have to acknowledge that the exploratory aspect of network analysis is limited by its

representation of relationships which are inevitably complex. However, its exploratory

powers are more effective on a different level, compared to EDA and multivariate techniques,

by its ability to examine the structure of relationships directly.

30 SVG Co-presence networks: http://mapserver.arch.soton.ac.uk/networks/co-presence.html ; SVG networks of distance: http://mapserver.arch.soton.ac.uk/networks/distance.html ; webmaps: http://mapserver.arch.soton.ac.uk/networks/map.html .


85

Our analysis in chapters V and VI illustrated that network analysis can also be used in a

confirmatory way. Given our aim of testing a geographical hypothesis, we decided to use a

beta-skeleton to create a network that reflects the geographical rather than topological logic of

the hypothesis. Network analysis also allows one to test hypotheses topologically, however,

by creating networks with structural features that optimally fit the model. Yet again, due to its

complexity the benefit of confirmatory network analysis should be seen in light of its ability

to examine the structure of hypothetical relationships. As analyzing a dataset in different ways

will inevitably lead to an unmanageably large amount of output and the diversity of results is

not always reflected in publications (Baxter 1994: 5), archaeologists should select the most

suitable confirmatory method, and should only choose network analysis when the

relationships between data points are the focus of research. We can therefore conclude that

network analysis is largely distinct from other statistical techniques applied in archaeology,

and that its contribution to the archaeologist’s toolbox should be seen in light of untangling a

complex web of interactions.

Unlike any other method focused on knowing IF archaeological data relates, network analysis

allows the archaeologists to understand HOW their data interacts.


86

7.2. Interpreting complexity

In the previous chapter we concluded that network analysis can only to a limited extent be

used as an interpretative tool. We are not suggesting, however, that the exploratory and

confirmatory modes should not be followed by an interpretation. In this respect we do not

agree with Tukey’s (1977: vi) statement that most interpretations of the results should be left

to the subject-specific expert, as in practice that will rarely happen. Chances of other

academics re-using the exact same dataset for the same research aims are very slim indeed.

Instead, formulating an interpretation of the attested patterns, however naïve it may be, is

crucial for provoking discussion on an issue one considers problematic.

As we mentioned in section 4.3, however, interpreting networks is not a straightforward

process. Networks are complex systems of parts in interaction (Bertalanffy 1968), consisting

of local patterns and global structure. These are shaped by the dynamic and continually

evolving interactions between the individual parts of the system. As such, a system cannot be

understood as the mere sum of its parts, rather it functions from the bottom up where the

dynamics of local action give rise to global structure (Batty 2005: 153). Systems are therefore

inevitably complex, leading us back to the problem raised at the beginning of this project:

data complexity. In line with the above, Rescher (1998: 1) sees a system’s complexity as “a

matter of the quantity and variety of its constituent elements and of the interrelational

elaborateness of their organizational and operational make-up”. The latter part of this

description hints on the idea that complexity is not contradictory with order. Indeed,

“complexity is certainly not a lack of order as such, seeing that any order be it lawful or

taxonomic or structural, or whatever – is itself something that can be more or less complex”

(Rescher 1998: 16). Moreover, such order in a system is generated by “processes that reach,

maintain and evolve” the system’s structure (Batty 2005: 153), indicating that any attempt at

understanding complex systems should focus on the interactions between its parts, which is

exactly what network analysis allows us to do.

We cannot state that network analysis as a method directly explains such processes, but it

does support a subsequent interpretative stage in two ways: by providing an index of

complexity and describing a system’s make-up. Firstly we should explore the measure of

complexity of a dataset, which will give analysts an idea of the scale and diversity of the

patterns they are looking for. In this we follow Rescher’s (1998: 17) reasoning that “by and

large, the amount of effort that must be expended in describing and understanding the make-

up and workings of a system is our best practical indicator of complexity, and its inverse is


87

our best practical indicator of simplicity”. A quick comparison of the outputs of network

analysis indicates that every analytical tool provides such an index of complexity for specific

structural aspects. For example, the high number and diversity in sites, forms and interactions

reflected in the co-presence networks of 25-0 BC is indicative of a complex set of patterns,

whereas the clear core-periphery patterns of the 125-100 BC networks in comparison can be

considered fairly simple. Secondly, network analysis allows for patterning and interactions at

different scales, be it spatial, temporal or structural, to be identified and examined in isolation

or in relation to the entire system. We therefore do not consider exploration and explanation

to be mutually exclusive. Indeed, the value of an exploratory phase in guiding interpretation

cannot be overstated, as Cox and Jones (1981: 141) argue by stating that “in general, testing

hypotheses with the aim of producing firm decisions may be less valuable than attempts to

summarize the quantitative evidence available and efforts to be open to the indications

provided by the data”.

Finally, as we touched upon the idea of ‘systems’ above we should explain exactly what we

mean by this, in the archaeological discipline, very loaded term. We purposefully referred to

von Bertalanffy’s (1968) seminal work on general systems theory rather than its early

archaeological applications (Flannery 1968; Rathje 1975), as our approach is more concerned

with systems as a concept and differs from these archaeological applications on a number of

key issues. Most importantly we stress the importance of local action contrary to covering

laws (this is contested by Flannery (1973) however), bringing about processes that result in

the global order evidenced in the archaeological record. Rather than being guided by a limited

number of factors, these processes are numerous, operate on every scale and are infinitely

diverse in nature. In this sense we adopt von Bertalanffy’s (1968: 19-37) description of

systems as an organized complexity of parts in interaction, but we are not concerned with

identifying principles valid for systems in general. Nevertheless, our approach shares

Trigger’s (1989: 308) critique that “in terms of causal factors, a systems approach serves to

describe rather than to explain change”. From the above discussion it should be clear that we

are very aware of this limitation, which results from a methodological and theoretical hiatus

of the relationship between structure and event (Preucel & Hodder 1996: 215). We believe

that this gap can be bridged by agent based simulation methods, as they allow for boundless

local variability to be tested on any scale: individuals, communities, artifacts, ideas. Graham

(2006b) indicated how a combination of network analysis and agent based modeling could

lead to innovative perspectives on old data. Such a combined approach merges the


88

exploratory strengths of networks with the interpretative strength of hypothetical processes

simulated through agent behaviour.

But by discussing agent based approaches we are, yet again, violating the scope of our

project, which finally draws to a close with the discussion of the benefits, disadvantages and

future prospects of archaeological network analysis provided in this chapter.

TOM BRUGHMANS CONCLUSION

89

CONCLUSION Similar to every discipline, the way archaeologists conduct their research evolves continually.

Methods for collecting, analyzing and interpreting archaeological data are a never ending

story of challenging trends and innovative methodological answers. In this project we focused

on a recent trend in data collection towards ever more complex and large datasets. We

illustrated that these datasets are the result of numerous decisions made by people in the

ancient past as well as people in the academic present, and all of them shape the

archaeological record and influence its interpretation. As a result, reconstructing the past by

means of datasets derived from different sources is problematic and requires initial data

exploration. As we were concerned with the phenomenon of large and complex datasets, a

fundamentally contextual approach was adopted, focusing on the relationships between the

data. Contrary to other methods, it was our goal not to simplify these interactions but rather to

confront an archaeological dataset in all its complexity, being open to the local as well as

general patterns evidenced in the data themselves. Armed with this attitude, we suggested

network analysis as a method for exploring large and complex datasets.

Throughout this project we illustrated how network analysis can serve to represent the full

complexity of a dataset’s inherent relationships. Different structural aspects and the general

makeup of the entire network can be explored, resulting in the identification of local patterns

and general structures. We concluded that network analysis succeeds in highlighting the

patterning that is present within a complex dataset, and stressed that an explanation of these

patterns was a necessary first step before drawing inferences about the past. Although

network analysis is not an interpretative tool, it can be used to test hypotheses of any sort

(geographical, temporal or structural) and facilitates a subsequent interpretation by its detailed

identification of patterns that require interpretation.

The past is more than a big jigsaw puzzle of material remains which, when we put all pieces

in the right position, will allow us to travel through time. The archaeologist’s task is more like

completing a coherent picture from a mix of fragmentary jigsaw puzzles through a

blindfolded middleman. We hope this project contributes to the archaeological discipline by

acknowledging the presence of middlemen whose ways of dealing with puzzles are dissimilar

to ours, and warning archaeologists not to put on a blindfold themselves.

TOM BRUGHMANS CONNECTING THE DOTS : EXPLORING COMPLEX ARCHAEOLOGICAL DATASETS

90

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SLANE, K. W. 1996b. The pottery of Stobi and its outside connections (book review of V.R. Anderson-Stojanović 1992). Journal of Roman Archaeology 9: 556-557.

SLANE, K. W. 2004. Corinth: Italian Sigillata and other Italian imports to the early colony, in Poblome, J.; Talloen, P.; Brulet, R.; Waelkens, M. (eds.) Early Italian Sigillata. The chronological framework and trade patterns. Proceedings of the First International ROCT-congress. Leuven, May 7 and 8, 1999: 31-42. Leuven/Paris/Dudley.

SMITH, D. A. & D. R. WHITE. 1992. Structure and Dynamics of the Global Economy: Network Analysis of International Trade 1965-1980. Social Forces 70: 857-893.

TOMBER, R. S. 1993. Quantitative approaches to the investigation of long-distance exchange. Journal of Roman Archaeology 6: 142-166.

TOUSSAINT, G. T. 1980. The relative neighborhood graph of a finite planar set. Pattern Recognition 12: 261-268.

TRIGGER, B. G. 1989. A history of archaeological thought. Cambridge.

TUKEY, J. W. 1977. Exploratory data analysis. Reading, Mass.

URQUHART, R. 1982. Graph theoretical clustering based on limited neighborhood sets. Pattern Recognition 15: 173-187.

WALLERSTEIN, I. 1974. The modern world-system I : capitalist agriculture and the origins of the European world-economy in the sixteenth century. San Diego.

WASSERMAN, S. & K. FAUST. 1994. Social network analysis : methods and applications. Structural analysis in the social sciences, Cambridge.

WINTERMEYER, U. 1980. Katalog ausgewählter Keramik und Kleinfunde, in: K. Tuchelt, Didyma. Bericht über die Arbeiten der Jahre 1975-1979. Istanbuler Mitteilungen 30: 122-160.

ZAHN, R. 1904. Thongeschirr (Scherben von Sigillata gefässen), in Wiegand, T.; Schrader, H. (eds.) Priene. Ergebnisse der Ausgrabungen und Untersuchungen in den Jahren 1895-1898: 430-434. Berlin.


96

Websites cited

(all last accessed 30/09/2009)

ADS : http://ads.ahds.ac.uk/.

ARENA : http://ads.ahds.ac.uk/arena/archindex.cfm.

ESRC’s Research Methods Programme Pajek tutorials:

http://www.ccsr.ac.uk/methods/publications/snacourse/snacourseweb.html.

ICRATES platform: http://www.arts.kuleuven.be/icrates/.

Pajek online manual: http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/pajekman.pdf.

Pajek tutorial data: http://vlado.fmf.uni-lj.si/pub/networks/pajek/howto.htm.

Pajek wiki : http://pajek.imfm.si/doku.php.

Social network analysis software links, Wikipedia :

http://en.wikipedia.org/wiki/Social_network_analysis_software.


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FIGURES Fig. 1: fictitious network of sites illustrating the basic components of a network. ..................19

Fig. 2: two-mode network of the period 150-125 BC, representing sites connected to pottery forms which are present at the site. The value indicates the number of sherds of a form that have been found........................................................................................................................27

Fig. 3: one-mode network of the period 150-125 BC, representing sites connected to sites which have evidence of the same pottery forms (co-presence). The line value indicates the number of pottery forms that are co-present. ...........................................................................28

Fig. 4: one-mode network of the period 150-125 BC, 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 forms are co-present...................................................29

Fig. 5: beta-skeleton (beta = 2) for the period 50-25 BC. Source topography and boundaries: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&)....................33

Fig. 6: example of an adjacency matrix for the sites of period 150-125 BC, representing the adjacency of vertices in the network and the strength of their ties...........................................39

Fig. 7: example of a dendrogram for the sites network of the periof 150-125 BC. It represents the increasing dissimilarity of the sites’ tableware assemblages from left to right..................40

Fig. 8: fictitious example illustrating the nesting of m-slices...................................................41

Fig. 9: evolution of absolute sherds dated to a period, probable volume of sherds per period, the number of forms attested for a period, and the number of sites on which these sherds were found.........................................................................................................................................43

Fig. 10: geographical representation of the ESA distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).....................................................61

Fig. 11: geographical representation of the ESB distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).....................................................62

Fig. 12: geographical representation of the ESC distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).....................................................63

Fig. 13: geographical representation of the ESD distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).....................................................64

Fig. 14: geographical representation of the ITS distance network for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).....................................................65

Fig. 15: geographical representation of a model of tableware distribution for the period 50-25 BC. Source topography: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).....................................................82


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Fig. 16 sites referred to in the text. Source topography and boundaries: Demis WMS server (http://www2.demis.nl/wms/wms.asp?wms=WorldMap&)...................................................104

Fig. 17: number of forms per 25 year period, including ITS. ................................................105

Fig. 18: number of forms per 25 year period excluding ITS......................................................1

Fig. 19: total number of form/ware combinations per period.....................................................1

Fig. 20: total number of edges in co-presence network per 25 year period. ..............................1

Fig. 21: total number of sherds recorded in the database per 25 year period.............................1

Fig. 22: total number of sites tableware sherds in the database were collected for per 25 year period. .........................................................................................................................................1

Fig. 23: number of probable forms per ware per 25 year period................................................1

Fig. 24: part of the project’s database relationships, showing the interaction between its ICRATES and Pajek parts. .........................................................................................................1

All figures were produced by the author.


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GLOSSARY ADJACENCY MATRIX: matrix showing which vertices are neighbours (adjacent) in the network

and the strength of their ties (e.g. fig. 6). The dissimilarity between vertices is

calculated based on the profile of their rows and columns in the adjacency matrix

(Nooy et.al. 2005: 265-237). (see p. 37)

ARC: a directed line between two vertices in a network, representing a directed relationship

between these vertices. (see p. 17)

BETA-SKELETON: graph output of a clustering technique combining the complete set of edges

joining Beta-neighbours for a particular value of Beta. The Beta value represents the

varying region of influence around point data that determines where reltationships are

drawn (Jiménez & Chapman 2002: 95). (see p. 31)

BETWEENNESS CENTRALITY: the proportion of all shortest paths between pairs of other

vertices that include this vertex (Nooy et.al. 2005: 131). (see p. 55)

CLOSENESS CENTRALITY: the number of other vertices divided by the sum of all distances

between the vertex and all others (Nooy et.al. 2005: 127). (see p. 55)

CO-PRESENCE: the presence or absence of forms on the same sites in the same period. (see p.

25)

DEGREE: (see also outdegree) structural measure that only takes a vertex and its direct

neighbours into account. The number of lines connected to a vertex in an undirected

network. The number of arcs a vertex sends or receives or both in a directed network

(Nooy et.al. 2005: 64). (see p. 55)

DENDROGRAM: visualization technique used in this project to represent the relative

dissimilarity of vertices in a network. Output of the hierarchical clustering technique.

(see p. 38-40, fig. 7)

DOMAIN: (see also output domain) in a directed network the (input, output) domain of a vertex

is the number or percentage of all other vertices that are connected by a path to this

vertex (Nooy et.al. 2005: 193). (see p. 55)


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EDA: exploratory data analysis. EDA is a set of techniques for the detailed study of data, that

guides a scholar (often visually) towards structure relatively quickly and easily (Tukey

1977; Hoaglin et.al. 1983: 1). (see p. 6)

EDGE: an undirected line between two vertices in a network, representing an undirected

relationship between these vertices. (see p. 17)

ESA: eastern sigillata ware produced in the coastal region between Tarsos (Cylicia) and

Laodicea (North-Syria) (Hayes 1985). (see p. 23)

ESB: eastern sigillata ware produced in the Maeander valley, possibly Ephesos (Hayes 1985).

(see p. 23)

ESC: eastern sigillata ware produced in Pergamon and the surrounding region (Meyer-

Schlichtmann 1988). (see p. 23)

ESD: eastern sigillata ware produced in (western) Cyprus (Hayes 1985). (see p. 23)

GRAPH: a set of vertices and a set of lines between pairs of vertices (Nooy et.al. 2005: 6) (see

p. 17)

HIERARCHICAL CLUSTERING: clustering technique that groups vertices together based on their

structural similarity. Outputs dendrogram. (see p. 37-38)

INDEGREE: the number of arcs a vertex receives in a directed network (Nooy et.al. 2005: 64).

(see p. 55)

INPUT DOMAIN: the input domain of a vertex can be defined as the number of all other vertices

that arrive at this vertex by a path (Nooy et.al. 2005: 193). (see p. 55)

ITS: Italian sigillata ware produced in Central Italy (Ettlinger et.al. 1990). (see p. 23)

K-CORES: a k-core consists of all vertices that are connected to at least ‘k’ other vertices

within the core (Nooy et.al. 2005: 70-71). (see p. 41)

LINE: (see also tie) a relationship between two vertices in a network, can be directed (arc) or

undirected (edge). (see p. 17)

M-SLICES: in an m-slice, vertices are connected by lines of value m or higher to at least one

other vertex. M-slices consist of nested groups of vertices and the ‘m’ stands for the

line value of the group or ‘slice’ (Nooy et.al. 2005: 109). (see p. 41, fig. 8)


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NETWORK ANALYSIS: a combination of analytical techniques for visualizing, detecting and

interpreting patterns of relationships between subjects of research interest. (see p. 17)

ONE-MODE NETWORK: network consisting of one set of vertices, can be deduced from a two-

mode network (Nooy et.al. 2005: 103). (see p. 25)

OUTDEGREE: the outdegree of a vertex is the number of arcs it sends (Nooy et.al. 2005: 64).

(see p. 55)

OUTPUT DOMAIN: the output domain of a vertex can be defined as the number of all other

vertices that this vertex connects to by a path (Nooy et.al. 2005: 193). (see p. 55)

PAJEK: software program for the visualization and analysis of large and complex networks.

(see p. 19-20)

TIE: (see also line) a relationship between two vertices in a network, can be directed (arc) or

undirected (edge). (see p. 17)

TWO-MODE NETWORK: network consisting of two distinct sets of vertices, can be split up in

two one-mode networks (Nooy et.al. 2005: 103). (see p. 25)

VERTEX: the smallest units of a network analysis, represented as points in a network (see p.

17)


102

APPENDIX A

SITES INDEX AND MAP

Database ID Site name Map

referenceDatabase

ID Site name Map reference

1 Aazaz H4 84 Ed-Dur U.A. Emirates

2 Abdera E2 86 Eisodei tis Theotokou kai Ayios Petros (26) D4

10 Agioi Pandes E5 87 El Aareime H4 11 Aigeira D3 91 Ephesos E4 12 'Ain Dara H4 92 Epiphaneia H5 13 Aizanoi F3 93 Eretria D3 16 Alexandreia F7 97 Gadara H6 19 Amathous G5 98 Gebel Barkal Sudan 21 Amorion G3 100 Gindaros H4 22 Amphipolis E2 101 Glyfada D4 23 Amygdalea D2 102 Gortyn E5 24 Anemorion G4 106 Halikarnassos E4 25 Antikythera shipwreck D4 111 Hippos-Sussita H6 26 Antiocheia ad Orontem H4 116 Isthmia D3 28 Apamea H5 117 Jalame H6 32 Apollo Smintheion E3 118 Jebel Khalid H4 34 Argos D3 120 Jericho H6 35 Arikamedu India 121 Jerusalem H6

36 Arsameia am Nymphaios I3 123 Kallion D3

38 Asea Valley D4 126 Kallydon D3 39 Ashkelon H6 127 Kanatha H6 40 Assos E3 128 Karamildan I3 41 Athens D3 131 Kastro Tigani E4 45 Ayios Philon G5 132 Kenchreai D3 46 Azotos (Ashdod) H6 137 Kition G5 49 Bab H4 139 Knossos E5 57 Berenice C6 143 Kourion G5 59 Berytus H5 144 Kozluca F4 62 Caesarea Maritima H6 145 Küçük Burnaz H4 63 Carthage A3 146 Kululu H3 66 Corinth D3 149 Labraunda F4 68 Cyrene D6 151 Lepcis Magna A5 69 Damaskos H6 152 Leukos Limen H5 70 Danakaya H4 153 Lidar Höyük I4 73 Delos E4 157 Malta B4 78 Didyma E4 158 Mampsis H7 79 Diokaesareia G4 160 Marina el-Alamein F7 80 Doliche H4 163 Meroë Sudan 82 Dor H6 164 Methymna E3 83 Dura Europos I5 205 Mutatio Heldua H5-6


103

Database ID Site name Map

referenceDatabase

ID Site name Map reference

208 Myos Hormos Egypt 374 Siphnos E4 212 Nessana G7 385 Sparta D4 216 Oboda H7 387 Stobi D2 223 Olympia D4 389 Sultantepe I4 233 Oumm el-'Amed H6 397 Sykea D3

234-327 Palaipaphos Area G5 399 Tanagra D3 328 Palaityr/Tell 'Arqa H5 409 Tarsos H4 329 Panayia Ematousa G5 411 Tel Anafa H6 330 Paphos G5 413 Tel Mevorakh H6 332 Pella H6 414 Tell Aajar H4 333 Pelusium G7 415 Tell Aar H4 334 Pergamon E3 416 Tell Aarane H4 335 Perge F4 420 Tell Bahouerte H4 336 Petra H7 421 Tell Banat I4 338 Phaselis shipwreck F4 422 Tell Bararhite I5 339 Philadelphia H6 423 Tell Berne (West) H4 344 Porphyreon H6 427 Tell el Qoubli H4 345 Porsuk H4 428 Tell Fafine H4 347 Priene E4 431 Tell Haourane H6 349 Pylos D4 435 Tell Kaffine H4 352 Qara Keupru H4 436 Tell Kassiha H4 353 Qara Mazraa H4 444 Tell Rahhal H4 362 Sabratha A5 445 Tell Rifa'at H4 363 Salamis/Constantia G5 450 Tenos E4 364 Samaria-Sebaste H6 453 Timna' Yemen 365 Samos (Heraion) E4 460 Troia/Ilion E3 369 Scythopolis H6 n/a Central Italy B1


104

Fig. 16 sites referred to in the text. Source topography and boundaries: Demis WMS server

(http://www2.demis.nl/wms/wms.asp?wms=WorldMap&).


105

APPENDIX B

DATA EXPLORATION CHARTS

Fig. 17: number of forms per 25 year period, including ITS.


106

Fig. 18: number of forms per 25 year period excluding ITS.


107

Fig. 19: total number of form/ware combinations per period.


108

Fig. 20: total number of edges in co-presence network per 25 year period.


109

Fig. 21: total number of sherds recorded in the database per 25 year period.


110

Fig. 22: total number of sites tableware sherds in the database were collected for per 25 year period.


111

Fig. 23: number of probable forms per ware per 25 year period.


112

APPENDIX C

DATABASE STRUCTURE

Fig. 24: part of the project’s database relationships, showing the interaction between

its ICRATES and Pajek parts.


113

APPENDIX D

PROJECT METADATA (DUBLIN CORE) Metadata for the HTML files was entered using the META tag. The properties and fields of

the pajek section of the database were provided with explanations. Metadata for the shapefiles

was entered using the Go-Geo metadata tool31.

Project metadata:

Contributor: na

Coverage: Mediterranean Sea and bordering countries.

Roman period between 200 BC and 700 AD.

Creator: Tom Brughmans

Date: 2009-09-30

Description: a project concerned with developing and testing network analysis as an

exploratory and confirmatory method for the archaeological discipline. This

method was applied to a large and complex database of tablewares form the

Roman East.

Format: see digital archive

Identifier: Brughmans_msc_dissertation

Language: en

Publisher: na

Relation: na

Rights: ICRATES project; Tom Brughmans

Source: Digital archive: Brughmans_msc_dissertation; website: http://mapserver.arch.soton.ac.uk/networks/.

Subject: Archaeological network analysis; tableware trade in the Roman East

Title: Connecting the Dots: Exploring Complex Archaeological Datasets with Network Analysis; Case study: tableware trade in the Roman East

Type: Collection

31 In the digital archive : root/spatial data/metadata .


114

APPENDIX E

NETWORK ANALYSIS APPLICATIONS

Non-archaeological examples

The flexible definitions of the key concepts of network analysis indicate that the method can

be applied to study any kind of structure, as long as the researcher is clear about what his

results represent. As such, network analysis has been applied to a wide range of disciplines,

with sociology often leading the way. Social network analysts assume that ties between

people matter, because they transmit behaviour, attitudes, information, or goods (Nooy et.al.

2005: 3). Over the years, numerous studies have been performed to detect and interpret the

nature of such ties. This resulted in the development of a range of analytical tools,

underpinned by a vast body of literature in social network theory (Wasserman & Faust 1994;

Freeman 2006). Economists were quick to adopt network analysis, as they realized its

potential in understanding the dynamics of trade. Among other applications, the method has

been used to classify countries in the world economy of the capitalist world system, as it was

conceptualized by Immanuel Wallerstein (1974), into core, periphery and semiperiphery

(Smith & White 1992). In disciplines like management and information technology, network

analysis is used to evaluate and enhance information flow in organizations and computer

systems (Krebs 2000; Kilduff & Tsai 2003). As a special issue of the Mediterranean historical

review illustrates, network analysis is also increasingly applied to historical research (Malkin

et.al. 2007). Historical sources are used to examine the interactions between ancient

communities, cities and people, in order to improve our understanding of the transmission of

goods and ideas.

Archaeological examples

In recent years, archaeologists too have turned to network analysis to answer a wide range of

research questions. In his study of the Roman brick industry, Graham (2006a) uses social

network analysis to examine how individual interactions shaped the social power enjoyed by

the biggest players in this industry. He succeeds in painting an extraordinarily intriguing

picture of the dynamics in Roman society as it is evidenced in a complex dataset, which

consists of a combination of brick stamps, brick production centers (fabric identification) and

places of deposition. As we explained above, one of the strengths of network analysis is its

ability to enhance our understanding of the transmission of goods and ideas, and indeed, the


115

majority of archaeological applications make use of this strength. As Isaksen (2005; 2008)

and Graham (2006b) indicated, approaching ancient itineraries as networks sheds light on the

spread of goods and information as well as on the archaeologically attested road system,

while Sindbæk (2007) interpreted the emergence of towns in Early Viking Age Scandinavia

through patterns in networks of long-distance trade. Of particular methodological interest is

the approach by Knappett, Evans and Rivers (2008; forthcoming), who use techniques derived

from statistical physics and network analysis to study maritime networks in the southern

Aegean Bronze Age. The aim of achieving an ‘archaeology of relations’ is embodied in their

model that tries to incorporate physical as well as social interactions.

All these archaeological examples have in common that relationships, spatial or social, are

considered crucial for understanding the past. In this project we tried to build on these

previous approaches to understand the variety of information relationships carry about

processes in the past and present that led to the creation of complex archaeological datasets.


116

APPENDIX E

DIGITAL ARCHIVE The project’s digital archive is provided with this dissertation text as a CD, but most of its

content is also available from the project’s website32. The archive consists of six main folders:

1. Dissertation text: a .pdf version of the dissertation and all the images used.

2. Spatial data: shapefiles produced for the project and spatial metadata.

3. Networks: all Pajek network and project files, ordered per network type (co-presence,

distance) and further ordered per period. These files can be opened using text editing

software or Pajek. All the results of the analyses were also provided in a graphical

format as .svg files which are always accompanied by an .html interface that allows

one to click layers of the .svg file on and off. Graphical outputs are also provided in

.eps format, which can be opened using software Adobe Photoshop, Gimp or

Ghostview. Finally, the outputs of the hierarchical clustering was added as a .txt file.

4. Database: the original ICRATES database used, and the project’s modified version of

this database which was used for data extraction.

5. Worksheets: contains all data exploration charts and the division of forms in 25-year

period.

6. Website: all files used for the project’s website. This website can be accessed from the

digital archive without internet connection by opening the ‘index.html’ file.

32 http://mapserver.arch.soton.ac.uk/networks/ .

brughmans t. 2009 connecting the dots: exploring complex archaeological datasets with network...

Documents

complex datasets

david wheatley

david potts

project website

ik zat

msc archaeological computing

complex database of

initial data exploration