Geographic interconnections

In this blog post we continue our quest to develop a method for studying trade routes as they are reflected in the ceramic evidence. It provides an alternative and in some ways parallel to our previous post concerning Beta-skeletons.

A computerised model was developed by Rihll and Wilson (1991) to study the interconnections between sites based solely on their geographical coordinates, while taking size, importance and interactions between sites into account. The only thing one needs to enter into the model are the locations of all sites. Other factors are simulated and develop when running the model thanks to three assumptions:

  • Interaction between any two places is proportional to the size of the origin zone and the importance and distance from the origin zone of all other sites in the survey area, which compete as destination zones.
  • The importance of a place is proportional to the interaction it attracts from other places.
  • The size of a place is proportional to its importance.

Through a number of simulations starting from an initially egalitarian state (equal size and importance for all sites), the most likely pattern of interconnections between sites is determined.

Shawn Graham successfully used this model in his analysis of the brick industry in the Tiber valley. Networks of interconnections between sites in the Tiber valley were created to “explore the effects of geography, stripped of all other considerations” (Graham 2006b: 77; Graham 2009: 678-681).

As the Relative Neighbourhood Graph (RNG) this method uses straight line distances, which will allow us to study the influence of distance in the distribution of table wares. However, as it is a probabilistic model its potential for testing hypotheses is far greater.

We could use this method to create a network of all sites included in the distribution patterns of table wares. The network can be analysed to determine the relative positions of all sites, knowing that distance is a significant factor and taking size and importance into account, but most importantly, exactly knowing the value of all these factors for a given result.

We should stress that the simulated importance represents the importance of a site in the table ware trade, given that distance is a significant factor (this might require a revision of the mathematics underlying the model). Instead of running an egalitarian simulation, we can therefore enter the values for importance into the model as they are present in the ceramic data. When we rerun the model we will be able to analyse a network of a certain distribution in a certain period knowing that distance is influential and being able to calculate this influence. Moreover, we can compare these ceramic networks with multiple stages in the egalitarian simulation.

Again, this is just an idea that might bring us one step closer to understanding the decision made by people involved in the distribution of table wares, but it is by no means without its issues:

  • We assume a direct correlation between number of sherds and importance in trade patterns. Should we use the diversity and relative amounts of ceramic forms as an index of importance? As this is a simulation we accept that we enter arbitrary values for something we try to study (the relative position of sites in different ceramic distribution patterns). Still we should beware for circular thought patterns which will eventually tell us that the things we think are significant will turn out to be significant.
  • What with the ‘size’ factor? Should we remove it from the model or can it represent another aspect of ceramic trade?
  • Is it useful to apply this model to the ceramic evidence, or should we just run the analysis without including the number of sherds, to see how sites relate to one another in space? Such an approach might allow us to compare a distance-based simulation with Beta-skeletons of ceramic distributions?
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