Mapping material flows

As set out in the introduction to this book, the aim of the research presented here was to examine the spatial dimensions of interaction between 3000 and 1500BC over a macro-scale in the Near East. For this reason, the establishment of potential

‘routes of movement’ is an essential first step. However, the aim is not simply to compile a catalogue of potential roads or corridors of movement, but to situate ancient flows of materials in their spatial perspective: in other words to combine natural routes with chronologically and materially-specific distributions in order to better understand ancient interaction. How then can we use GIS approaches, such as the ‘cost-surface’ techniques discussed in detail above, to ‘map’ these material flows?

Figure 3.1. ‘Cost-of-passage’ raster – Model 2. This is the grid model upon which all the cost-based analyses in this book are based. The green areas show the terrain which is the ‘least costly’ to traverse, and the red shows the ‘most costly’. Note that seas are not included in this model.

Figure 3.2. ‘Cost-of-passage’ raster – Model 1. In this model the effects of topography have been lessened.

See Figure 3.1 for explanation of colours.

Figure 3.3. ‘Cost-of-passage’ raster – Model 3. In this model the effects of low-levels of water availability have been heightened. See Figure 3.1 for explanation of colours.

Distribution maps have already been mentioned above as a long trusted, though flawed, tool of the archaeologist (Section 3.2.2). Despite their limitations, we must continue to rely on the founding principle of the distribution map: the spatial location of finds of categorized material. These find spots represent the

‘last known address’ of the objects in question, and as such index the ‘maximum’

extent of their circulation. Context remains important: some objects may be

‘secondarily’ deposited, and others had such long circulation lives that it may be difficult to know how chronologically ‘coherent’ certain lists of objects are.

Various dot distribution maps will be shown in this book. GIS has simultaneously made the production of distribution maps from lists both much easier and much harder: easier because with database technologies, it is possible to make lists cumulative (rather than redraw them from scratch each time); but harder because the epistemology of GIS science demands ‘definite’ Cartesian locations. It is no longer as easy to hide imprecise information about the origin of objects by the scale of the map because GIS programs require a co-ordinate. Finding accurate and precise co-ordinates for sites and findspots is often extremely difficult, even for relatively well investigated locales (see further discussion in Appendix B), especially given the multiplicity of site spellings.

However, using GIS, distribution maps can be taken further, with more informative basemaps. The ‘dots’ of the distribution can be overlain onto environmental features, such as topography. This can help the viewer think in terms of landscapes rather than abstract space. More importantly the distributions can be used as factors within the GIS analysis, to create visualizations which can highlight geographical relationships more clearly. This combination of distribution and environmental factors is what I would like to call an ‘archaeotopogram’.

3.5.1 Archaeotopograms: cost-of-passage to cost-surface

Archaeotopograms are graphical tools of archaeological data placed into topographic context to allow comparison between different data sets and to better think through relationships between material culture and their flows across space.

They use static models of the environment such as the ‘natural routes’ or ease of transportation described above (which only indicate the potential for movement), and combine them with archaeological distributions using cost-based analysis to produce meaningful visualizations.

Many different types of archaeotopograms could be designed based on different techniques and different data sets. The most basic example of a simple distance archaeotopogram (what I categorize as type A1 below) would be a diagram that shows the relative distance (measured in effort or time) of all locations on the map from a particular site, when taking account of suitable cost/friction factors such as topography and climate in the cost/friction model. The resulting map gives an indication of the effective influence that a site might be expected to have on its surroundings (or vice versa) in the absence of, for example, political boundaries.

More complex and potentially informative archaeotopograms can be created by combining the results of multiple ‘cost-distance’ functions together. The key point is that the aim is precisely not to create a map of a route in the form of a path or road in the traditional manner, but instead to represent different aspects of archaeologically-relevant distributions whilst taking into account the landscape.

When compared with other archaeotopograms or other data, these visualizations

can be used interpretatively to characterize the nature and shape of ‘routes’ and the directionality of interaction. As such, these archaeotopograms form merely one tool amongst many that may be deployed to aid the interpretation of the archaeological data.

3.5.2 Some types of ‘cost’-based archaeotopograms

The choice of archaeotopogram type will depend on the available data and what needs to be represented. The resultant visualization may not necessarily contain linear patterns that one would normally expect from a ‘route’ but instead attempt to place objects, sites and collections into their effective landscapes. The following sections suggest some basic archaeotopogram forms and their aim and interpretation. (See also Appendix A.3).

Type A1: Site accessibility archaeotopogram (simple cost-distance) Essentially this involves a straightforward anisotropic cost-distance analysis from a single node. The resultant diagram effectively shows the relative accessibility of the surrounding landscape to the particular node (i.e. site) in question, and hence a general likely sphere of two-way geographical influence, though of course the actual cultural sphere of influence at any particular moment in time is also based on technological and socio-political factors. The resultant diagram shows how accessible the location or site is over the long distance, and thus how easy (or how likely) travel to all other locations is. Since this only deals statically with one site, and routes are obviously a dynamic factor of multiple (and changing) locations, this is of limited utility for our project, but it does allow a characterization of the ‘distance footprints’ of different sites and can be used to help explain cultural and resource links between different sites. Examples are shown here as Figures 3.4-3.8 (where orange indicates relative proximity and green/blue indicates relative distance: the same colour symbology was used in each to make these maps comparable). The sites selected (Ur, Gonur Depe, Shengavit, Arslantepe and Harappa) are distantly located from each other since nearby sites will produce similar results. Each visualization indicates the likely geographical

‘trend’ of interconnections based on ease of travel: for example, that Gonur Depe is ‘relatively’ much further away from Ur than Arslantepe, despite a fairly small difference in terms of Euclidean distance. Cultural connections which do not correlate with this relative distance must therefore indicate differences of travel technology or fewer social barriers to travel.

Type A2: Source accessibility archaeotopograms (multiple-source cost-distance)

The background processing is identical in principle to type A1, and is a cost-distance analysis from multiple nodes. Again the resultant diagram shows the relative accessibility of the landscape to these multiple nodes. This is most useful when looking at the relative accessibility of particular natural resources, or (conversely) the cost of travelling to the closest source of this type – no differentiations of the individual characteristics of particular sources are made, however. (For examples, see Figures 4.2a, 4.4, 5.2, 5.3).

Figure 3.4. Relative distance (site accessibility) from Shengavit (Armenia), based on an archaeotopogram ‘type A1’. (Compare Figure 3.8 for key).

Figure 3.5. Relative distance (site accessibility) from Gonur Depe (Turkmenistan), based on an archaeotopogram ‘type A1’. (Compare Figure 3.8 for key).

Figure 3.6. Relative distance (site accessibility) from Ur (Iraq), based on an archaeotopogram ‘type A1’.

(Compare Figure 3.8 for key).

Figure 3.7. Relative distance (site accessibility) from Harappa (Pakistan), based on an archaeotopogram ‘type A1’. (Compare Figure 3.8 for key).

Figure 3.8. Relative distance (site accessibility) from Arslantepe (Turkey) based on an

archaeotopogram ‘type A1’. The coloured contours show the relative distance (time or energy ‘cost’) required to travel from the source point (in this case Arslantepe) to every space across the map calculated with an anisotropic cost-distance analysis using the ‘cost-of-passage’ raster shown in Figure 3.1. In the symbology used here, brown indicates low relative distance, and blue relatively high distance. It is interesting to note the south-easterly reach of Arslantepe’s position which may help explain the area’s early involvement with Syro-Mesopotamian cultures such as the Uruk.

Type B1: Cultural zone archaeotopogram (sum of simple cost-distances)

This type of archaeotopogram uses cost-distance to define a cultural zone geographically by combining the site accessibility of multiple nodes. The idea is that by adding multiple cost-distance results together, this reveals an area in which certain cultural features (e.g. a certain object type, or package of objects – however one wants to define the zone) are accessible. This produces something like a ‘culture area’ map akin to those used by the ‘culture history’ school of archaeology but with the substantial improvement that boundaries are relative, and rely on the landscape through ‘cost’. (For examples, see Figures 5.45, 5.50, 6.40b, where the symbology uses a yellow-red-purple progression from ‘proximal’

to distant).

Type B2: Resource zone archaeotopogram (sum of multiple-source cost-distances)

This type of archaeotopogram uses cost-distance to define a resource zone geographically by combining two or more ‘type A2’ archaeotopograms. This reveals the relative accessibility to multiple resources – and thus zones in which the two (or more) resources can most easily be accessed together. (For example, see Figure 5.11).

Type B3: Resource-site zone archaeotopogram (sum of multiple-source cost-distance with simple cost-distance)

This type of archaeotopogram combines one ‘type A1’ and one ‘type A1’ or ‘A2’

archaeotopogram together to highlight the relationship between a site and a set of resources. This reveals a kind of corridor of least cost between the site and its (potential) resources. (For example, see Figure 4.4).

Type C: Corridor archaeotopograms (sum/factor of cost-corridor of multiple natural neighbours)

This type of archaeotopogram is designed to show a set of ‘route-like’ corridors by combining the cost-corridors of multiple pairs of nodes, the pairs selected on the basis of ‘natural neighbour’ (or other technique). The resultant visualization would show the areas of least-cost between the selected nodes, and hence an overall route map for the particular phenomena being mapped from the nodes. In practical terms, such iterative models are is a very computationally- and time-intensive.

(For this reason, no meaningful examples are shown in this book). Resistivity-models described above may, with suitable testing, offer ways to produce equivalent diagrams in a more efficient way in the future.

3.5.3 Applying archaeotopograms

In the following chapters of this book, archaeotopograms are used selectively to help clarify spatial relationships between locations and the location of material flows. In all instances shown, Model 2 was used as the base cost model for all analysis. This model was combined with an elevation model (Elevation) and a Vertical Factor (Slope) into an ‘Anisotropic Accumulated Cost Surface’ procedure

(see Appendix A.2 for details), and processed using whichever distribution data was relevant. The final GIS files for this model are available for download from the web address listed in Appendix A.