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Archaeology

Digital maps illuminate ancient trade routes

Nature volume 543, pages 188189 (09 March 2017) | Download Citation

How did the relationship between human societies and their surrounding terrain shape the formation of long-distance trade networks such as the Silk Road? Digital mapping and computer modelling offer insights. See Article p.193

Although the Silk Road sounds like a single route, it was in fact a complex series of pathways that formed a network through which trade goods, people and ideas moved. Archaeologists have long known the basic geography of major ancient trade routes such as the Silk Road, which formed a heavily travelled network between China and Europe by the third century BC (ref. 1). However, specific details, such as how trade contacts originated and what forces governed the movement of early travellers as social networks first formed, have been challenging to determine. On page 193, Frachetti et al.2 investigate the relationship between Silk Road routes and the movements of nomadic herders in mountainous areas with suitable pasture.

The authors used satellite imagery and GIS (geographic information systems) mapping software to compare the locations of major Silk Road archaeological sites with the modelled annual movements of livestock herders making journeys from high-elevation pastureland in the summer to warmer, lower-elevation pasture in the winter. In the authors' analysis, elevation derived from satellite imagery provided a key map layer with which herders' projected movements were modelled using GIS software. Frachetti and colleagues propose that ancient herders moving annually between highland and lowland areas would not have simply followed the least costly route in terms of time or energy expended travelling across rugged landscapes, but instead would have favoured the routes with the most productive grassland pasture.

In GIS analysis, flow-accumulation algorithms are usually used to model how water flows over satellite-derived maps that have grid cells representing elevation3. Frachetti and colleagues adapted this algorithm to model the flow of herders along routes with the best pasture, as represented by the normalized difference vegetation index (NDVI; a map layer that shows present-day vegetation health). Maps of NDVI, obtained from satellite-imagery analysis, are calculated based on the fact that healthy vegetation differentially absorbs and reflects certain wavelengths of light4. Because NDVI measures modern vegetation health, it does not necessarily reflect ancient conditions. The analysis by Frachetti and colleagues thus presumes that the spatial patterning of the best pasturelands has remained roughly similar through time.

The authors applied 500 rounds of flow-accumulation modelling to represent 500 years of herders' seasonal movements through mountainous terrain, using a model in which herders prefer routes that offer suitable pasture. Frachetti and colleagues' modelling of movements correlates with independently documented locations of Silk Road archaeological sites5,6, indicating that the spatial distribution of grasslands, and the people and animals seeking them, contributed to the formation of the Silk Road network (Fig. 1). The authors' analysis represents a significant advance in the study of an ancient trade network, a development achieved through the use of tools for spatial analysis that continue to transform scholars' understanding of ancient geographies.

Figure 1: The Silk Road follows herding paths.
Figure 1

Livestock move past the remains of a Silk Road city that has been buried for a millennium in the mountains of Uzbekistan. Frachetti et al.2 used satellite images and computer modelling to investigate the relationship between Silk Road archaeological sites and areas of good grassland pasture in mountainous regions that formed the paths of ancient herding routes. Image: M. Frachetti, 2011

Since the mid-1990s, spatial technologies such as satellite imagery, GIS software and Global Positioning System tools have revolutionized archaeological research by enhancing researchers' ability to analyse ancient geographies and spatial relationships. The wide-ranging effects of these technologies have even been compared to the transformative influence that the discovery of radiocarbon dating in the late 1940s had on archaeologists' ability to understand ancient chronologies and build timelines. Frachetti and colleagues' work exemplifies the most recent surge in archaeological applications of spatial technologies, such as the use of drones7 or the creation of 3D models using laser scanners and photographs8.

A related approach, social network analysis (SNA), holds great potential for archaeological studies9,10. It builds on network-analysis techniques developed in mathematics, physics, biology, economics and sociology that focus not only on individual entities, but also on the interconnectedness and emergent properties that arise from relationships between nodes that form part of a larger system.

SNA could be used to extend Frachetti and colleagues' work using additional data about sites on the Silk Road network. Settlement-pattern analysis, a type of investigation that examines the distribution of hamlets, villages, towns and cities in a region, has been used in archaeology since the 1960s. However, many settlement-pattern analyses have treated archaeological sites merely as dots on maps, even though villages, towns and other locations often preserve a vast and diverse array of information about ancient human activities11. Using data about architecture, plant and animal remains, and artefacts from sites along the Silk Road for SNA might help to reveal the nature and strength of ties between sites, and perhaps identify sites or groups of sites where particular trade activities were concentrated.

Frachetti and colleagues' research is innovative in breaking new ground without breaking any actual ground. A logical next step might be to apply SNA to the Silk Road to help identify the economic, social and political dynamics of this important ancient network.

Notes

References

  1. 1.

    The Silk Road: A New History (Oxford Univ. Press, 2012).

  2. 2.

    , , & Nature 543, 193–198 (2017).

  3. 3.

    Arc Hydro: GIS for Water Resources (ESRI Press, 2002).

  4. 4.

    Remote Sens. Environ. 8, 127–150 (1979).

  5. 5.

    The Silk Roads: An ICOMOS Thematic Study (ICOMOS, 2014).

  6. 6.

    Old World Trade Routes (OWTRAD) Project;

  7. 7.

    Archaeol. Prospect. (2017).

  8. 8.

    & (eds) 3D Recording and Modelling in Archaeology and Cultural Heritage: Theory and Best Practices (Archaeopress, 2014).

  9. 9.

    J. Archaeol. Method Theory 20, 623–662 (2013).

  10. 10.

    (ed.) Network Analysis in Archaeology: New Approaches to Regional Interaction (Oxford Univ. Press, 2013).

  11. 11.

    & in Surveying the Greek Chora: The Black Sea Region in a Comparative Perspective (eds Bilde, P. G. & Stolba, V. F.) 27–46 (Aarhus Univ. Press, 2006).

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  1. Michael J. Harrower and Ioana A. Dumitru are in the Department of Near Eastern Studies, Johns Hopkins University, Baltimore, Maryland 21218, USA.

    • Michael J. Harrower
    •  & Ioana A. Dumitru

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Correspondence to Michael J. Harrower.

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https://doi.org/10.1038/543188a

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