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BY-NC-ND 4.0 license Open Access Published by Akademie Verlag April 15, 2023

Geospatial Methods and the Premodern Economy: Mapping the Institutional Landscapes of Northern Europe, 1350–1650

Raumgestützte Methoden und die vormoderne Wirtschaft: Die Kartierung der institutionellen Strukturen in Nordeuropa, 1350–1650
  • Bart Holterman

    received his PhD in 2019 for his thesis on German trade with Iceland, Shetland and the Faroes in the late 15th and 16th century. Aside from his ongoing work on premodern north Atlantic trade, he is one of the founding members of the Viabundus project (2019-today).

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    and Angela Huang

    works on different aspects of the history of the German Hanse, premodern textile trade and capital markets. Since 2017 she has been the leader of the Research Centre for Hanse and Baltic History in Lübeck.


The premodern European transport network was accompanied by a number of institutions that affected the transaction costs related to commercial travel, such as fairs, staple markets and toll stations. With digital techniques and big datasets, it is now possible to study these phenomena on a grand scale and to reveal patterns in supraregional economic exchange. Using the Viabundus dataset on premodern transport and mobility in northern Europe (13501650), this article explores the possibilities that such data offer for understanding large-scale economic activity. By employing GIS mapping for visualisation purposes and methods of network analysis such as the calculation of betweenness centrality, the data on fairs, staple markets and toll stations can help us understand the institutional structure of the premodern economy in which merchants operated.

JEL Classification: N 07; N 93

1 Introduction

Following Douglass North’s seminal works on institutional theory some decades ago, [1] New Institutional Economics have become an integral part of economic history research. Many studies have since then employed this theoretical framework in their endeavours to explain Europe’s economic development from the Middle Ages and leading up to the Industrial Revolution. [2] These studies both employ ‘traditional’ historical research and quantitative studies using regression analysis. In the institutional theory, transportation costs play an important role: The effectiveness of institutions derives from their success in minimizing transaction costs as the intangible insecurities that economic agents face, such as costs in terms of time, but also ‘real’ costs such as tolls, expenses for transport, storage costs and broker fees, and combinations of both like enforcement costs.

Transaction costs and institutions responding to them are a recurring theme in the study of European premodern economic history. Mostly, these are limited to local or regional case studies for two main reasons:

(1) The data needed for the study of the premodern trade system must be compiled and structured for a large area. The market system must be covered as comprehensively as possible, which means recoding as many places (settlements, transit sites etc.) involved in the network as possible. Especially network data is hard to come by for larger areas. Travel times and basic costs of transport can only be estimated if historians possess sufficient knowledge of trade routes; especially inland trade is notoriously hard to track. As infrastructure has so far been mainly studied at a regional level [3] and published in a form that does not allow its combination with other historical data, this often leaves researchers with generalizing economic spheres or calculating linear distances. [4]

(2) Before the advent of digital methods, there was a lack of suited methods for processing such large datasets on trade systems. By now, databases allow the creation of structured data and analysing it with a variety of tools. GIS in particular offers both data visualisation in a spatial context for geocoded data and interpretation via network analysis. The study of the road system of the Roman empire highlights the usefulness of digital techniques. [5]

The Viabundus project is working towards such integrated and structured data to make reasonably representative mapping of trade infrastructure over time possible. The Viabundus dataset and online webmap [6] (see Fig. 1), first published in 2021, is designed to facilitate more accurate estimations of travel times and costs in long-distance trade on a given route in premodern northern Europe (1350-1650). [7] The integrated data of overland trade routes (roads and waterways) and node-related data (town/settlement, tolls, staples, fairs) addresses the need to analyse mobility of people, money and goods more accurately, especially the study of economic activity with a supraregional perspective. [8]

Fig. 1 
Viabundus Webmap displaying the Dataset Viabundus 1.1. Source:
Fig. 1

Viabundus Webmap displaying the Dataset Viabundus 1.1. Source:

By now, the Viabundus database contains a sufficient amount of data to reflect on the possibilities of analysing such geocoded data for investigating larger patterns of premodern European trade. Three things influenced exchange in particular. Two major sets of institutions that guided or even forced traffic and heavily impacted transaction costs were fairs and staple markets. Furthermore, tolls, customs and other transit fees connected to moving commodities from one place to another affected the costs of trade significantly. The Viabundus database collects data on all three of these factors impacting trade. Moreover, we make use of the digitized trade routes (roads, waterways) that allow more accurate estimations of distance and transport time.

In the following, we want to explore this data in its usefulness for discerning larger patterns of the premodern system of exchange. We especially want to advocate the usefulness of geocoding and visualisation in the form of maps as a method of data processing. The wish to map important features that influence, hinder, or stimulate traffic for the study of large-scale economic infrastructure and developments has been expressed in the past. Dirlmeier stated already in 1987 that “the reasonably complete mapping of all toll and staple privileges from the late 12th century onwards would create an optically impressive picture of the ongoing densification of the network of institutions that potentially limited trade”. [9] Rothmann expressed a similar sentiment when stating the lack of an analysis of fairs in their geographical and chronological distribution. [10] Our interest here is of a methodological, explorative nature: We want to test what can be done with the existing data, open a discussion on the benefits of compiling a larger dataset and employing GIS rather than present a comprehensive data analysis on the institutional network that Viabundus is still in the process of building up.

In the following section (2), we introduce fairs, staples and tolls/transit fees as the main features structuring premodern exchange and the institutional framework they represent. Section (3) presents the Viabundus dataset on these features and how the historic evidence was processed into the structured data that is the basis for the explorative analysis in the remainder of this article. Section (4) then explores what kinds of observations can be made using such data and in particular the contribution of geographical visualisation to economic history research. Section (5) tests the application of network analysis to the data. In section (6), we conclude our study with a first assessment to what extent large datasets such as Viabundus, and applying digital methods to them, help us in studying the structure and development of the premodern trade system.

2 Fairs, Staples and Tolls: The Institutional Setting Behind the Premodern Market Network

Institutions as the ‘rules of the game’ address the uncertainties economic actors necessarily face – increasing with growing distance involved in a transaction. ‚Good institutions‘ reduce transaction costs of various kinds, namely tangible ones like transportation costs, but also search and measurement costs (e.g. quality control, but also finding business partners), enforcement costs (in case of delay or default) and others. In consequence, they lower uncertainty and can thus promote trade and, more importantly, growth.

Fairs, staples, and tolls are central phenomena in premodern exchange. Each of these phenomena represents a number or ‘bundle’ of institutions that together form the framework for trade activities. Especially fairs – periodic market events with a low frequency – have been praised as central in the economic development of an emerging market society, providing a great number of institutions for open markets. Especially the 12th/13th century Champagne Fairs have been studied in their positive effect on trade through open access institutions. For later periods, mainly the large international fairs such as Antwerp, Frankfurt (Main), and Leipzig, attracting merchants from large parts of Europe, have been studied. [11] However, these well-studied large events are only a fraction of the many fairs that structured commercial activity over the year. Their interplay has not been studied yet, not least due to lack of (curated) data.

What makes fairs special in premodern exchange? In general, it is the openness of fairs (also called ‘free markets’) to outsiders [12] in an economic landscape otherwise shaped by protectionist measures for the own citizenry and restrictions on the economic activity of foreign merchants. The character of fairs as closely defined and recurring periodic events brought the advantage of reduced coordination costs and time investment or search and information costs for both buyers and sellers. [13] This lowered the costs for matching supply and demand. Moreover, fairs provided a special legal regime protecting property rights and facilitating contract enforcement, specifically quick administration of justice via special fair courts. In other words, contract enforcement here was beneficial to those making transactions, it was furthermore based on individual and not collective liability. Security is another major institutional service, provided both at the fair and through seigneurial letters of safe conduct for all on their way to the fairs. Further services could entail a well-established commercial infrastructure (fortifications, roads, canals). [14] A special function of fairs was that of a money market and ‘clearing house’ for international payment and exchange services. As part of that, fairs offered currency security as well. [15]

Such periodic markets were at the same time inextricably linked to the levying of urban taxes and tolls as an additional source of income for their venues and to cover the additional costs that the cities faced for providing the advantageous services mentioned above. However, tolls and other duties had to correspond to the services provided in return. [16] Thus, institutions promoting exchange and an increase in transaction costs are not mutually exclusive, but rather two sides of the same coin, part of the package instead of a hindrance to exchange.

Whereas it is important to point out that other forms of market organisation could of course grant similar advantages, the combination of institutions to foster unhindered trade is unique to fairs. The specific mechanisms in place at a given fair, however, could vary greatly according to the reach and function of periodic markets that can be broadly categorized by local, regional or supraregional importance (with increasing duration of the events). [17] This ties in with differing functions, as categorized by Rothmann: 1. the manorial annual market for local aggregation of production, 2. the local annual supply market, 3. a supply along long-distance routes, determined by geographic conditions, 4. the regional annual market for crafts production, 5. the local and regional supplementary annual market, and 6. the supraregional distribution market. [18]

Many towns unsuccessfully tried to set up supraregional fairs, modelling their privileges for example after those of Frankfurt (Main). [19] A number of factors affected whether periodic markets could persist long term: a. the location and transport geography had to fit the intended function of the market; b. for local and regional periodic markets proximity to a highly developed agricultural area and/or a strong local economy as well as a well-developed industrial production in the surrounding area; c. a strong political power to reliably guarantee safe conduct; d. for supraregional markets, a centre position between at least two strong economic regions that brought their products to the fair. If these conditions were fulfilled and if the institutions that make up a ‘free market’ could be provided, incentives for economic agents were high to make use of such a place of exchange. [20]

Most fairs remained comparatively unimportant, yet still they contributed to a system of exchange that built on the institutional arrangements offered by this specific type of market. For the Holy Roman Empire alone, Rothmann counted roughly at least 5,000 periodic markets in about 1,500 places before 1500. [21] This highlights that although the functioning of major fairs has been studied, such studies leave the majority out of the picture. The Viabundus dataset works towards a basis for a broader understanding of the role and development of fairs in the premodern economy.

Staples played as important a role in premodern exchange as periodic markets and like them, they differed according to levels of trade. Dijkman identifies two main types: 1. Claiming a regional monopoly on trade in certain commodities, derived from the need to supply the urban population with foodstuff and raw materials for local crafts production; 2. Claiming exclusive rights in supraregional trade in one or more commodities which are in transit through a given area and mainly meant for reshipment. [22] Such leading staple markets for supraregional exchange like Bruges, Dordrecht, Cologne and Hamburg have received quite a bit of attention by economic historians. [23] Gönnenwein’s extensive study on staple markets, [24] however, demonstrates that again their total number goes far beyond the few prominent examples.

Almost as an antithesis of fairs, staples as restrictive and forced markets have been seen in a negative light. Though still open to foreign merchants, they had to interrupt their journey and offer their goods for sale, sometimes even exclusively to locals. [25] Dijkman has highlighted two negative effects in particular: facilitating and supporting surplus extraction (through taxes, but also including effects on price formation) and suppressing the development of trade elsewhere. [26] Being compulsory furthermore meant in theory that traders could not opt out if visiting the staple was disadvantageous for them. Bart Lambert, however, has shown for Bruges that informal markets like nearby Sluis were frequented to evade the costly staple, not least by economic actors who could not compete at a market like Bruges. [27] Staple privileges were furthermore often accompanied by privileges granted to foreign merchants. [28] Hanse merchants held such privileges in Bruges, creating an advantageous situation for them for centuries. Though some wished to leave behind the staple of Bruges in favour of Antwerp already in the 15th century, only in the early 16th century was the trading post finally moved there. [29]

It is important to note that becoming a staple did not have to be rooted in seigneurial privilege. The city of Ghent enforced its grain staple itself for a while, however, building on an already existing concentration of grain trade there. [30] Although for example Gdańsk never officially had the right of staple, it became de facto a major Baltic staple market from the 16th century onwards when around 80 percent of Polish foreign trade concentrated here. [31] In fact, around 1400 Elbing had been the designated Prussian staple market, supported by its ruler, the Grand Master of the Teutonic Order. Yet, in the following decades, Gdańsk became the leading export port of Prussia and by 1440 at the latest, Elbing’s staple rights were insignificant. [32] This example illustrates that a successful staple market needed an economic importance and favourable location within the network of trade routes to assert itself long-term.

Furthermore, ‘staple’ had two meanings: the more narrow, legal meaning refers to a variety of regulations that forced merchants to trade on a certain marketplace (with certain commodities) – such staples rested on and competed with others for seigneurial privileges and were not least used to exclude competing markets from trade. [33] The broader contemporary meaning, however, used ‘staple’ to identify ‘an important marketplace’. [34] This highlights an important feature of many successful staples: like successful periodic markets, their importance in exchange often preceded or accompanied their rise as staple markets. [35] Whereas seigneurial privileges might grant staple rights, without economic strength staples could not persist in the long run. Location was an important factor for becoming a successful staple market, as well as seigneurial support in claiming such a position as a compulsory market especially for supraregional trade (in certain products) within a competitive market network. [36] Still, establishing a compulsory staple “result[s] from a set of conscious decisions, shaped by the interests of groups and individuals.” [37] The influence of such interest groups distinguishes staple markets from ‘open markets’ as their ‘lobbying’ meant that institutions had a tendency to serve the interests of a small group rather than contribute to the overall functioning of exchange.

Even if being coercive, staples also had positive effects on the development of trade – some being quite similar to those of fairs. As fixed meeting points, they concentrated commercial activity and reduced the costs of matching supply and demand. Staples made it easier to control trade and thus also reduced problems in contract enforcement. They provided a physical infrastructure and services needed for regular, larger-scale exchange. As with fairs, this involved financial services. Especially investments in infrastructure might even have benefited from the element of coercion as returns were guaranteed. In short, staples offered a concentration of commercial institutions beneficial especially for large-scale trade. [38] Staple privileges furthermore included safeguards for visiting merchants, both as safe conduct on their journey and as immunity from arrest for debts at the market itself. [39]

Another functional aspect to be considered is that staple markets often relate to specialised large-scale export production of a given region. The famous market of Bruges was a staple for Flemish (and partly other) woollen cloth that characterised the region’s economy since the 12th and at least until the 16th century. And Gdańsk’s rise as a de facto staple accompanied an economic shift, when (a varied) export production became a more important factor in the economy in early modern times. [40] Staples for specific (groups of) products also meant a specialisation of trade in these goods there. This involved a stronger development of institutions related to such goods, such as product-specific quality control.

Different services and obligations could be tied to the compulsory unloading of goods, such as the compulsory measuring of goods that were sold, the obligation to use local services such as brokerage or transport monopolies for local shipmasters. Finally, being compulsory also affected the relationship between staples and tolls and other duties. [41] In the case of Dordrecht, establishing a staple market was in seigneurial interest to control shipping and thus discourage toll evasion. [42] However, from the second half of the 15th century, a buy-out from staple regulations came into practice. [43]

As we have seen, tolls were closely linked to both fairs as ‘free’ markets and staple markets. In both cases they correspond to the extensive set of commercial institutions offered to those frequenting these markets. In the case of staples, surplus extraction could have negative effects on trade, but still in most cases tolls and other duties to a certain degree matched what was offered to traders. Beyond that, tolls were established at permanent markets as well. As they form an important part of seigneurial incomes, the fragmented political landscape especially in the Holy Roman Empire multiplied the number of tariffs. This complicates its study and partly as a consequence, tolls and customs have only been studied well on a regional level. [44]

Tolls and other fees levied on traffic have often been seen as political institutions that hindered economic development, not least by contemporaries. Most premodern tolls were transit tolls, levied on commodities, vehicles or (draught) animals passing through certain points on a route. The existence of a transit toll station therefore presupposes a considerable amount of traffic on the route where it was located. Although technically stations of safe conduct (Geleit) are not tolls, in practice they were toll-like fees extracted from passing travellers. They were a regular part of the transaction costs that economic agents had to factor in. Safe conduct fees were levied by territorial rulers, and by the time this article focusses on, well-established systems were in place to administer safe conduct in a similar way as tolls. [45]

It is important to note that at this time, given that they contributed to a large degree to the income of territorial lords or towns, transit tolls were not levied to hinder commercial traffic, but to profit from it. Territorial protectionist custom policies, such as we know from modern times, where for example high import duties are levied to protect the economy of a certain region from competition from abroad, did not yet exist. [46] Some tendencies towards a territorial toll policy can be seen here and there, for example in East Frisia, where commodities for which customs had been paid in the ports of Norden and Greetsiel could pass duty-free through Emden. [47] However, these can hardly be considered protectionist toll policies in the sense of the (post-) industrial age, since the objective might have been not hindering trade too much by reducing payment of the toll in a certain territory. According to Dirlmeier, tolls were therefore not so much an instrument for the hindrance or protection of the economy, but rather an instrument in territorial power politics, since reducing the toll stations of an opponent meant reducing his income. In this sense, only the toll stations of others were seen as disturbing, not tolls and customs per se. [48] Moreover, the mere existence of a toll station does not necessarily mean that money changed hands: many groups of people were exempted from toll payment [49] and in practice not all who theoretically had to pay were charged. [50] Individual toll and safe conduct stations sometimes formed systems – if the fees had already been paid at one station, traders could prove this with toll tokens or receipts at other customs offices. [51]

Some kinds of tolls were also installed for financing the improvement of infrastructure (bridge and road tolls), next to corvée (compulsory labour) and earmarked taxes, which grew in importance in the late Middle Ages with the growth of trade and territories. [52] The investment in infrastructure seemed to have had a positive effect on transport costs. For a long time, the medieval and early modern road network was seen as badly maintained, consisting of unpaved roads, with only minor interest of public authorities in improving it. The few studies on medieval and early modern transportation costs, however, have shown otherwise. Masschaele proved, based on English sheriff accounts, that transport in the 14th century was relatively cheap, [53] and Harrison has shown that the infrastructure in the form of bridges in England was well suited to meet the demands of the economy. [54] For the duchy of Brabant, Ballaux and Blondé argued that transport prices were largely dependent on prices for grain that was used to feed the draught animals, and that the transportation system comprising land and waterways, while functional, neither hindered nor promoted economic growth in the 16th century. [55]

Tolls were effectively fees for the maintenance and improvement of infrastructure on markets and transport routes. Similarly, safe conduct fees also financed safety of road transport, at least in theory. The system of safe conduct developed parallel to a growing volume and frequency in regional and supraregional trade. [56] Both duties therefore express reduced uncertainty and thus transaction costs. They therefore not only have negative effects on economic exchange, not least as an expression of rent-seeking but also represent strong positive effects.

To sum up some main results for the following analysis, fairs and staples were heterogenous phenomena, but both represent a higher concentration of commercial institutions. Both market types did differ slightly in their economic function, especially at the supraregional level, where fairs have a stronger focus on transit trade and staples relate more strongly to a region’s export potential. However, the general roles of fairs and staples in the economic system were providing a beneficial institutional setting and functioning as intermediaries between different levels of trade or different economic areas, and as such, they are not that different. Tolls, although certainly not favoured by traders, were also proxies for investments in secure trade routes and good infrastructure.

That fairs, staples, and toll stations all represent a higher level of institutional development, suggesting increased commercial activity, speaks in favour of using systematic data on these features to learn more about larger patterns of the premodern system of exchange.

3 Dataset and Method of Data Collection

The Viabundus database consists of two central components: the nodes, i.e. the settlements and other places of interest for the traveller, and the edges, i.e. the geometric representations of the roads that connect these places. Because each edge connects two nodes, but nodes can be connected to more than one edge, the database assumes the form of a network model, which allows calculations such as least cost path finding.

The dataset started as a digitisation of the long-distance trade routes from the atlas Hansische Handelsstraßen by Bruns and Weczerka, [57] which covers a large part of mainland northern Europe along the North Sea and Baltic Sea coasts. Subsequently, a regional approach was adopted to refine the database with more precise and additional data region by region. As the database is published as a work in progress with regular updates, the density and quality of the data is better for some regions than for others. In the latest version 1.1 that was used for this article, Denmark and parts of Germany (Lower Saxony, Saxony-Anhalt, Schleswig-Holstein, Thuringia and parts of Brandenburg and Saxony) can be considered well-covered. For the Netherlands, much data has been entered already, whereas other regions are less well or hardly covered (see Fig. 2). Of course, this has consequences for the usability of the data in large-scale studies, which will be part of the discussion below.

Fig. 2 Current Coverage of the Viabundus Dataset. Regional State of Research in the Viabundus Database: green: completed; yellow: ongoing research/ much information already included; orange: road network refined for historical accuracy, some additional node information added; red: road network roughly drawn, limited additional data.
Fig. 2

Current Coverage of the Viabundus Dataset. Regional State of Research in the Viabundus Database: green: completed; yellow: ongoing research/ much information already included; orange: road network refined for historical accuracy, some additional node information added; red: road network roughly drawn, limited additional data.

For the ‘completed’ regions, the database contains the following elements: (1) precise mapping of the land routes; (2) all towns are included and connected with the street network, using the Deutsches Städtebuch or similar repositories of towns in other countries; [58] (3) all navigable waterways are mapped, with the exclusion of those waterways only used for the export of raw materials (e.g. timber or peat) from the point of their extraction to the nearest market; (4) additional information is added about toll stations, fairs, staple markets, bridges, harbours, ferries and shipping locks, which are added as attributes to the nodes table. Care was taken to include all fairs, toll stations and staple markets in the respective region within the selected time period 1350-1650, whereas bridges, ferries, shipping locks and harbours have been added where relevant, but were not researched systematically. The current version of the dataset 1.1 contains 15,128 nodes, of which 1,188 are towns, 403 fairs, 121 staple markets and 918 toll stations. Within the completed regions, there are 356 towns, 224 fairs, 29 staple markets and 459 toll stations.

Only in some cases, overviews exist of fairs and tolls within a certain region and a certain time period. [59] For others, the data was entered based on mentions in local historical literature. Staple markets have been mainly recorded from the compendium Das Stapel- und Niederlagsrecht by Otto Gönnenwein. [60] Since the latter work contains almost all staple markets within the area covered by the Viabundus database, the data about staples can be considered more consistent for the entire region than the information about tolls and fairs, the quality of which is more prone to regional variation. Its usability for spatial analysis is therefore limited to the completed regions. Future versions of Viabundus will hopefully see a more homogeneous coverage of data for a much wider region, improving the dataset’s suitability for large-scale spatial analysis.

For any of the attributes, the database contains at least a start and an end year between which the attribute existed and a short description with references to literature. The start and end year are not always easy to determine: often the charter granting staple rights, fairs or tolls is not known, so in those cases the year of first mention is usually taken as start year, even though the attribute might have existed already for a while before it was first mentioned in writing. Imprecise dating also causes difficulties, for example “in the second half of the 15th century” will be entered with 1450 as start year. Although these choices are explained in the description field, an analysis performed on the date fields will have to acknowledge that the data are not as exact as suggested by these fields.

Another uncertainty in the collection of data is the large variety of types for certain attributes. This means that the existence of an attribute does not necessarily say something about the character of the attribute. This is especially acute with toll stations, which is used as an umbrella term for all kinds of fees to be paid on the road. They can be levied on certain types of transport (land or water transport, waggons, cattle, or draught animals), specific types of cargo, at specific times of the year (market tolls) or related to specific places (e.g. bridge tolls). Also included under the toll attribute are stations of safe conduct (Geleit): although in theory these are not tolls, in practice they were toll-like fees extracted from passing travellers. [61] Moreover, in many cases more than one form is combined, and in other cases too little information is available for a toll station to categorise it, which is why we refrained from further specification in the database other than the full-text description field.

For fairs there exists a separate table with more elaborate information. Because fairs have a temporal component, this additional table includes information about the dates and duration of individual fairs. This enables analyses about the number and duration of fairs in a certain town (as far as this information is available in the sources).

Despite the unevenness of the data and interpretational problems sketched above, the data covers enough area to allow considerations of a methodological nature, especially within the ‘completed’ regions. The methodological explorations undertaken in the following sections aim at gaining a better understanding of what a large amount of data on central institutions like toll stations, staples and fairs can offer us when exploring the structure and patterns behind premodern exchange.

4 Mapping the Institutional Landscape: A First Analysis

Having presented the main institutions shaping premodern long-distance trade and the structure of the dataset, let us now explore the data compiled in the Viabundus database by means of geospatial visualisation. A first step in processing the information is done by presenting the institutions as (weighed) points on the map, whereby showing the density of points in the form of a heatmap (more intense colours indicating a higher density) makes it possible to identify geospatial clusters in the data. Moreover, the fact that the dataset defines the period of existence of attributes makes it possible to see temporal changes and developments in the selected attributes. We have selected the three years of 1350, 1500 and 1650 as the beginning, middle and end of the time period covered by the dataset to show these developments (Figs. 3-4). We created these maps with QGIS, as were all other maps that follow.

Fig. 3 Distribution of places with fairs in 1350, 1500 and 1650.
Fig. 3

Distribution of places with fairs in 1350, 1500 and 1650.

Fig. 4 Distribution of staple markets in 1350, 1500 and 1650.
Fig. 4

Distribution of staple markets in 1350, 1500 and 1650.

Fig. 5 Distribution of toll stations in 1350, 1500 and 1650.
Fig. 5

Distribution of toll stations in 1350, 1500 and 1650.

Several first observations can be made based on simple visualisation of our dataset: 1) the occurrence of fairs, staple markets and toll stations generally increases over time. Of course, this could be attributed to a source bias, since the quality and quantity of written information about these elements increases through the centuries, but more likely it reflects the general development of the commercial infrastructure and an increasing tendency to regulate and profit from increasing trade within the three centuries under discussion. 2) Clear clusters of condensed activity can be discerned, with a high correspondence between the fairs, staples, and tolls. The Rhine delta has a high density of these elements throughout the period, and a small cluster of staple privileges and toll stations develops around the mouth of the Lower Elbe (the region around Hamburg) between 1350 and 1500. A significant increase in these institutions can also be discerned in the area around Leipzig and Erfurt after 1500, which is probably related to the development of the Leipzig fairs as venues of international exchange and the accompanying establishment of staple regulations and a system of stations of toll and safe conduct.

A more advanced visualisation can be achieved when we weight the nodes on the map, which is possible with the fair data, since these also include information about duration and scope. In Fig. 6 we have selected all places with fairs active in 1500, for which we used the total number of fair days for all known fairs in a given place per year as the number of fair days. However, due to problems with the interpretation of sources, the number of fair days is not always certain; for this reason, we have calculated the mean out of the highest and the lowest number of fair days that is mentioned in the source material. Additionally, fairs in the database are classified into the three categories local, regional and interregional fairs, depending on the geographical range from which individual fairs attracted their visitors. [62]

Fig. 6 Places with fair privileges in 1500, weighted by duration and geographical scope (category).
Fig. 6

Places with fair privileges in 1500, weighted by duration and geographical scope (category).

Visualizing the distribution of fairs allows some observations on the role of fairs in the economic system beyond individual cases: In terms of geographical distribution, mapping the data shows (unsurprisingly) a greater number of fair days in the Low Countries than anywhere else. However, a ‘fair corridor’ moves from the Low Countries inland, where we note a higher number of fair days in today’s central Germany as well. Though the existence of a fair is not equal to economic importance, the widespread fair activities and no less than 43 places already in our database with ten or more fair days per year still highlights a whole fair system far beyond the few leading ‘international’ fairs in Frankfurt (Main), Antwerp [63] or Leipzig. Once again, it should be noted that especially for the economic and urban powerhouses Flanders and Brabant, the Rhineland, Westphalia and Hesse, data are still missing, which distorts the picture in the southwestern part of the map.

The visualisation shows that it was not so much the few large international fairs, but especially the many fairs with a regional scope that were responsible for a cumulation of fair days in a given region, promoting commerce via such ‘free markets’ and functioning as regional distribution points for (agricultural) produce. According to our dataset, only a few places hosted fairs of different categories. This suggests that fairs were markets specialised to a certain type of exchange, although of course we should keep in mind that the classification is an interpretation undertaken by the editors of the database, which might distort the image.

When mapping fairs, staple markets, and toll stations together, the correlation between these features quickly becomes apparent (Fig. 7). Especially the relation between a high density of fairs and a high density of toll stations is easy to discern, but also the appearance of staple markets seems to be in line with the other institutions, something that would be expected given the interwoven character of these institutions as sketched above. However, slight discrepancies between the attributes can be discerned on a small scale in some regions: For example, a high density of fairs and toll stations in southern Lower Saxony around Braunschweig is not backed by a staple market in that region. The same goes for the modern German-Dutch border region of Twente and Münsterland, a discrepancy that might even be underrepresented given that the data on toll stations and fairs is not complete yet for this region.

Fig. 7 Places with fairs, staple markets and tolls in 1500.
Fig. 7

Places with fairs, staple markets and tolls in 1500.

Here it is important to reflect on the geographical extent of the dataset. While the dataset can be considered reasonably complete when it comes to staple markets for the entire region, having been based on the extensive study of Gönnenwein, this is not the case for the other attributes. The large and dense cluster of toll stations in the Rhineland around Cologne, for example, can be attributed to Pfeiffer’s work on toll stations in that area, which has been entered into the database. However, since no systematic work on toll stations and fairs in the surrounding areas such as Brabant and Flanders, Westfalia and Hesse has been undertaken yet, and the information for the Netherlands is still incomplete, it is not possible to say at this point whether this cluster really stands out as it does or if the density of toll stations extended into its neighbouring regions. Likewise, the early cluster of staple markets around the current German-Polish border in 1350 can probably be attributed to the widespread establishment of German towns in that region. The question whether this was accompanied by the establishment of toll stations and fairs, although likely, cannot be answered with the data yet. It should therefore be kept in mind that the observations stated above are significant only for the advanced and completed regions.

5 Network Analysis: Centrality of Institutions

The network character of the Viabundus database allows us not only to visualise the nodes on a map, but to use methods of network analysis to analyse their role in the street network. A promising analytical value is the centrality, which is an indication of the role of the node – here a fair, toll, or staple – within the network. The betweenness centrality is calculated by tracing the least-cost path between each pair of nodes in the network, counting in the process which nodes are passed along the way. A node with a high count can therefore be considered more in between all the other nodes in the network than a node with a low count, and therefore more central to the network. We calculated these values using the algorithm of QGIS’ GRASS GIS plugin, with only the length of the path as parameter to calculate the cost values. The shortest path is not necessarily the cheapest or fastest path, as it leaves out parameters such as elevation, nor does it necessarily show the nodes and edges that received the most traffic. However, we believe that a calculation of betweenness centrality based on shortest path can provide us with insights about the structure of the network as a start, which can then be used to test against historical evidence for actual traffic in future research.

For a correct calculation of the centrality values in a network, the network as a whole has to be covered. Nodes at the edge of a network are much less likely to receive a high betweenness centrality than those in the centre. As we can see in Fig. 8, where we have calculated the betweenness centrality for fairs in the network based on shortest distance between the nodes, for example the important international fairs of Frankfurt (Main) are calculated with a relatively low betweenness centrality, but this can largely be attributed to its location at the very edge of the Viabundus dataset. As the fairs of Frankfurt (Main) radiated towards southwestern Germany and France, [64] regions which have not been included yet, an inclusion of these regions in the dataset will obviously boost the betweenness centrality of Frankfurt (Main). This observation is especially important for seaports: since sea routes are not yet included in Viabundus, port cities are much more likely to be located on the edge of the network, and will therefore receive a relatively low betweenness centrality that does not reflect their importance as hubs in the international trade.

Fig. 8 Betweenness centrality of fairs in 1500, based on shortest path. The size of the circles reflects the duration of the fairs.
Fig. 8

Betweenness centrality of fairs in 1500, based on shortest path. The size of the circles reflects the duration of the fairs.

For nodes within the core of the network it is safer to make observations of the economic structure based on the betweenness centrality. As we can see, most of the fairs are in places with a high betweenness centrality, which might indicate that fairs are normally organised in those places that are central to the network. On the other hand, there seems to be no clear correlation between the centrality of the node (indicating its importance within the street network) and the duration of the fairs within that place (indicating the economic importance of the fair). Striking outliers are the few large fairs with a very low betweenness centrality such as Kiel, Skanör and Haren (Ems). The large international herring fairs in Scania (Skanör) depended largely on the sea connections with the German towns on the Baltic Sea, [65] the sea routes of which have not been included. The same might partly be the case for Kiel, which was furthermore located on a ‘dead end’ street in the street network (Fig. 9), as is the case for Haren (Ems). The node located directly in front of Kiel (Vorstadt), where all traffic would pass to enter the city, has a betweenness centrality of 4,271,622, a value that would catapult the fair into the upper category of most ‘central’ fairs.

Fig. 9 The location of Kiel in the street network, as displayed on the Viabundus map. The road to the Northwest leads to Denmark, the road towards the South to Lübeck and Hamburg.
Fig. 9

The location of Kiel in the street network, as displayed on the Viabundus map. The road to the Northwest leads to Denmark, the road towards the South to Lübeck and Hamburg.

If we calculate the betweenness centrality for staple markets (Fig. 10), unsurprisingly a similar picture emerges: most staple markets are very central to the street network, except in the Rhine delta, where staple markets are more dependent on water routes and are located on the edge of the network. This was to be expected since staple markets would be difficult to enforce if they were located far away from the main traffic, and therefore were likely to emerge at important junctions in the water and land route network that were difficult to circumvent.

Fig. 10 Betweenness centrality of staple markets in 1500, calculated by shortest path.
Fig. 10

Betweenness centrality of staple markets in 1500, calculated by shortest path.

However, a quite different picture emerges if we calculate the betweenness centrality of toll stations (Fig. 11). Given the fact that most toll stations in this period were installed to profit from traffic and assuming that travelling merchants usually took the shortest route between two nodes, we can expect that most toll stations show a high betweenness centrality. Although some toll stations with a high betweenness centrality indeed line up neatly along important trade routes, such as the land route from Lübeck via Hamburg and Bremen to the Netherlands, or the route from Cologne towards Leipzig, many toll stations also were located at less centrally located nodes. This might have been caused by the existence of secondary stations of toll and safe conduct, which were installed on roads of secondary importance to prevent travellers from circumventing toll stations by taking a detour. Among others, we might suspect this being the case with the many toll stations with a low centrality around Cologne, Erfurt and Leipzig. On the other hand, it might also be an indication that the assumption is wrong that travelling merchants usually took the shortest route between two nodes. This would have to be tested with historical evidence for traffic on certain routes in future research.

Fig. 11 Betweenness centrality of toll stations in 1500, calculated by shortest path.
Fig. 11

Betweenness centrality of toll stations in 1500, calculated by shortest path.

Another striking feature is the low centrality of many toll stations along the big rivers, especially the Rhine and Weser, although tolls on river traffic were seen as having an especially high influence on commercial traffic, both by contemporaries and in modern historiography. [66] The question arises if this is caused by the method of calculating the betweenness centrality based on shortest distance alone. After all, the meandering waterway was often significantly longer than the more direct land route. For example, the distance between the staple markets Magdeburg and Hamburg via the land route was about a third shorter than via the river Elbe (226.4 km vs. 329.8 km), the same goes for the distance (Hann.) Münden-Bremen over land vs. via the river Weser (243.8 km vs. 371.2 km). [67] As a consequence, a calculation based on shortest route as the only parameter usually favours land over river traffic. However, river transport has often been considered significantly cheaper than land transport, especially for heavy transports and bulk goods moving downstream, and therefore the primary choice of transport where it was available. Based on English 14th-century sheriff records, Masschaele has calculated that land transport was twice as expensive as river transport, although he does not distinguish the costs for downstream and upstream water transport. [68] If we apply this observation to our calculations by doubling the cost for land routes, we see that the betweenness centrality for toll stations along the rivers significantly increases, at least for the river Weser (Fig. 12). Eight of the ten most ”central” toll stations are even toll stations located on rivers, five of them on the Elbe (Table 1). The effect is less strong with regards to the Rhine toll stations, which retain a low betweenness centrality. This might be related to the fact that data in this region is not yet complete. More importantly, it is probable that not all toll stations are correctly connected to the edge representing the river, since nodes and edges are edited separately and therefore the person entering information for water toll stations related to a settlement is not checking whether those settlements are actually connected to the waterway segments.

Fig. 12 Betweenness centrality of toll stations in 1500, calculated by least-cost path.
Fig. 12

Betweenness centrality of toll stations in 1500, calculated by least-cost path.

Tab. 1

The ten toll stations with the highest betweenness centrality in 1500, based on the least-cost path.

Number Name Betweenness centrality
1 [Gorzów Wielkopolski] (Warta) [69] 13,622,711
2 Frankfurt (Oder) 12,078,304
3 Hitzacker (Elbe) 11,642,052
4 [Santok] 10,840,061
5 Wehningen (Elbe) 10,140,261
6 Stadersand [Stade] (Elbe) 9,124,059
7 Magdeburg (Elbe) 8,681,444
8 [Plaue] (Havel) 7,975,673
9 [Boizenburg] (Elbe) 7,804,662
10 Paderborn 7,278,513

6 What GIS Can Teach Us: Observations and First Methodological Conclusions

In this article we have explored the visualisation and analysis of premodern economic infrastructure data using GIS. Digital methods offer a way of processing larger amounts of data into digestible visual formats, namely maps, that can go far beyond locating places for a better orientation of readers. Based on a growing dataset on fairs, staple market and transport-related fees, we exemplified how such geocoded data may be employed to show patterns of market exchange and concentration of commercial activity. Fairs and staples are two specialised types of premodern markets that channel traffic flows and thus give structure to the system of exchange. A larger dataset recording their presence in the premodern economy – here in the period 1350 to 1650 – effectively shows where economic activity concentrated. Although negative effects have been highlighted especially for staple markets, they arguably not only forced traffic, but were also promoting exchange as central markets and offered specialised services. Even toll and safe conduct stations, at first glance disadvantageous for merchants, can be employed as indicators for the maintenance and development of infrastructure that was fundamental for the functioning of the economic system. Systematic data collection for and mapping of such institutions allows researchers to explore larger patterns of systems of exchange.

The ongoing Viabundus project provided the data for our methodological considerations. It is important to acknowledge the limitations of any dataset, such as the variation in data accuracy and overall quality. Though in principle data on fairs, staples and tolls are available across Europe, any dataset with as large a coverage as the Viabundus database will vary in data quality – not least due to different contexts of data collection and editors. However, datasets and digital methods applied to it are not intended to reproduce a historical reality, but to model the functioning of pre-modern market systems in approximation. The advantages of such an approximation outweigh the disadvantages. Even visualisation through mapping, a relatively simple method, brings significant added value, namely a data presentation that would otherwise not be possible. For example, it allows us to observe that the occurrence of fairs, staple markets and toll stations generally increases over time.

Visualization of geocoded economic data as a methodological approach is equally important as it points to avenues of further research. Mapping institutions or their concentration at fairs and staples allows pattern recognition that leads to hypotheses on the functioning of the economic system. Mapping fairs as a broader phenomenon puts into perspective the importance of international fairs in favour of fairs with a mostly regional reach, forming a ‘fair corridor’ moving inland from the Low Countries. This might highlight the strong interregional character of premodern exchange, with fairs mainly facilitating regional distribution and supplying their region with goods from elsewhere. Whether these fairs truly offer the same advantages as the well-studied top-level fairs requires further study. Part of a more comprehensive investigation of fairs in premodern economic history would also explain the distribution patterns that – based on the current dataset – suggest a stronger presence in trade from West to East with a much lesser density around the Hanseatic ports along the North Sea and Baltic Sea coasts.

Exploring the occurrence of fairs, staples and tolls in combination gives us an impression of the system in which premodern agents acted. Some observations derived from the dataset, like a high density of fairs going hand in hand with a high density of toll stations, are not surprising, but could hitherto not be mapped on a large scale. Others, such as the common occurrence of fair and staple rights in one place, challenge the implicit or explicit juxtaposition of fairs and staple markets as opposing organisational forms for markets. Other observations, like the high density of fairs and toll stations in the region around Braunschweig without the presence of a staple market invite further investigation both on a larger scale and on a regional level. Geospatial methods certainly don’t explain economic systems and their change with the push of a button, but offer new hypotheses and impulses for the study of the premodern economy.

The network character of the Viabundus database also allows the use of methods of network analysis. We have explored centrality as an analytical value that promises to inform us of the role of a node – here a fair or toll station, or staple – within the trade network. Whereas a first analysis confirms that most fairs and staples have a high betweenness centrality, meaning they are at places that are central to the network, many toll stations curiously show a low betweenness centrality. This can lead us to different conclusions or assumptions: for example, that customs stations were more widely scattered to minimise circumvention strategies; or that the shortest (and therefore implicitly cheapest) route is not the route economic agents necessarily used most. It is therefore necessary to look into the relationship between calculated routing and centrality and case studies of actual mobility in the future. Only by testing digital tools against snapshots of historical reality documented in the source material will historians be able to properly judge and improve their usefulness.

We hope to have shown that a comprehensive dataset on major features in the organisation of premodern trade and its geographical visualisation can add to our knowledge on premodern exchange patterns. However, our exploratory analysis also highlights the amount, quality and coverage of data needed to truly draw conclusions. This calls for an extension and verification of our data specifically. It is an advantage of digital datasets that they can be continuously expanded, but still, they rely on existing historical research that is not available for all regions with the same density. Harmonization of data is therefore always needed in compiling datasets and must be accepted as a trade-off to be able to make observations on a more abstract level. Digital methods in economic history must be aware of the limitations of the underlying historical evidence. Bearing this in mind, structured data and digital methods such as geographical visualisation can give us new perspectives for the study of economic developments.

About the authors

(Dr.), Bart Holterman

received his PhD in 2019 for his thesis on German trade with Iceland, Shetland and the Faroes in the late 15th and 16th century. Aside from his ongoing work on premodern north Atlantic trade, he is one of the founding members of the Viabundus project (2019-today).

(Dr.), Angela Huang

works on different aspects of the history of the German Hanse, premodern textile trade and capital markets. Since 2017 she has been the leader of the Research Centre for Hanse and Baltic History in Lübeck.

Published Online: 2023-04-15
Published in Print: 2023-05-25

© 2023 Bart Holterman/Angela Huang, published by De Gruyter

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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