The second post in the madness series, describing the run-up to my PhD submission! Last time I wrote about why visibility networks might be an interesting method in archaeology. There was a hidden agenda in that post however: I am not just interested in visualising a visibility network, that has been done before by many archaeologists. My main interest is in understanding the decisions that went into the establishment of lines of sight. That is, the processes that led to the visibility network I study. This might sound rather ambitious, since many factors influenced the selection of the settlement locations I study in my PhD, and visibility networks are merely one factor derived from our limited knowledge of past settlement patterns. However, I argue it is necessary to understand such processes. Mainly because when archaeologists formulate assumptions about how lines of sight affected past human behaviour, these assumptions imply a sequence of events rather than a static state. Therefore, a method is needed that allows one to test the assumed processes, and I have some ideas on how to go about this 🙂
Network representations of archaeological data are often used as static snapshots conflating an ever-changing dynamic past. By performing an exploratory network analysis we get an idea of their structure during a given period of time. Such an approach can be considered a type of exploratory data analysis. However, archaeologists use these data networks as representations of past phenomena. It is these past phenomena that archaeologists are ultimately interested in understanding, and most of past phenomena were not static but involved change through time. It is entirely plausible that at an earlier or later stage in time a given network would have had a different structure.
A commonly used technique for archaeologists to overcome this problem is to formulate theoretical assumptions about how the emergence or disappearance of a relationship between pairs of nodes in their data networks affected the change of past networks over time (from here-on referred to as dependence assumptions). Such dependence assumptions are frequently accompanied by (explicitly formulated or implied) expectations of the kinds of network patterns they give rise to. In other words, archaeologists frequently make theoretical statements about dynamic processes that cause change in past phenomena, and how these are represented in networks of archaeological data. Nevertheless, we rarely evaluate whether processes guided by our dependence assumptions can actually give rise to the networks we study, nor do we consider the effect multiple dependence assumptions can have on each other in such processes. Instead, archaeological network analysts have relied on the identification of the expected patterns in an observed network’s static structure when discussing the social processes that caused a network to change from one state to another.
The study of visibility networks in archaeology serves as a particularly good example of this problem. Archaeologists have used visibility networks as a method for studying the role particular visibility network patterns could have in structuring past human behaviour, for example through communication networks using fire or smoke signalling, or the visual control settlements exercise over surrounding settlements. Formulating dependence assumptions for visibility networks implies a sequence of events where new lines of sight will be established as a reaction to pre-existing lines of sight. For example, if we observe that a settlement is positioned in a visually prominent location from where many other settlements can be seen then we might formulate the hypothesis that this location was intentionally selected to enhance communication with or visual control over neighbouring settlements. A further example: if an effective signalling network was considered during settlement location selection then settlement locations inter-visible with other settlements would have been preferred. However, archaeological network analysts have so far studied these processes exclusively through an analysis of static network representations. By pointing out the patterns of interest, an exploratory network analysis can only take us so far to evaluate our dependence assumptions, leaving hypotheses surrounding the intentional creation of visibility patterns untested. A good example of this is Tilley’s (1994) study of a network of inter-visibility between barrows on Cranborne Chase, in which an observed network pattern is interpreted as the intentionally established outcome of an untested process: “One explanation for this pattern might be that sites that were particularly important in the prehistoric landscape and highly visible ‘attracted’ other barrows through time, and sites built later elsewhere were deliberately sited so as to be intervisible with one or more other barrows. In this manner the construction of barrows on Cranborne Chase gradually created a series of visual pathways and nodal points in the landscape” (Tilley 1994, 159).
Very few visibility studies have explored hypotheses about such processes explicitly (see Swanson 2003 for a notable exception). In my case study, however, the decisions to establish certain patterns of visibility among urban settlements are the focus of attention. Most crucially, I will try to evaluate to what degree this changed through time. The approach taken here is experimental. It will initially focus exclusively on the patterns of inter-visibility between settlements, exploring their observed structure as a static snapshot, and then addressing the following hypothetical question: if the visibility patterning that we have observed was the only reason for selecting the locations of sites, what then would be the process that is most likely to have led to the observed patterning? This question will be evaluated through a statistical approach that models the creation of visibility patterns in abstract space (i.e. by simulating the creation of points and lines without taking the landscape’s topography into account as a constraint). Finally, the results of this exploratory network analysis and statistical simulation approach will be re-contextualised within a wider archaeological discussion to shed light on aspects of the changing interactions between urban settlements in the study area through time, as reflected through visibility patterns.
Next time I will introduce the archaeology of this study area and show you some actual results 🙂
As always, I very much welcome your comments. They are very valuable to me in these last stages of my PhD.
References:
Swanson, S. (2003). Documenting prehistoric communication networks: A case study in the Paquimé polity. American antiquity, 68(4), 753–767.
Archaeological Networks 
Very intersting. What statistical approach do you use to create an ” ideal model”?
Tanks! The statistical approach is called exponential random graph modelling, ERGM. I will introduce it in the next post. But it’s basically a method that produces random networks, and pseudo random networks that are meant to express a certain assumption of what relationships mean (e.g. If inter-visibility is assumed to be important we can produce random networks with a higher proportion of reciprocal ties). The structure of the simulated random networks can then be compared with the structure of the archaeologically observed network.