Later Medieval France: The Polity

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D Nilson, Ben Cathedral Shrines of Medieval England. Pantin, W. Medieval Archaeology. Pounds, Norman John Greville The Medieval City. Westport, US: Greenwood Press. Prior, Stuart Reid, Stuart Castles and Tower Houses of the Scottish Clans, — Botley, UK: Osprey Publishing. Rotherham, Ian D. In Liddiard, Robert ed. The Medieval Park: New Perspectives. Steane, John The Archaeology of the Medieval English Monarchy. Aberth, John Airlie, Stuart The Medieval World. Alexander, James W. The Journal of British Studies. Aston, Margaret; Richmond, Colin In Aston, Margaret; Richmond, Colin eds.

Lollardy and the Gentry in the Later Middle Ages. Stroud, UK: Sutton. Bachrach, Bernard S. In Parker, Geoffrey ed. The Cambridge History Of Warfare. Bailey, Mark In Given-Wilson, Chris ed. Barlow, Frank Thomas Becket. Barron, Caroline Bevington, David Blanchard, Ian In Britnell, Richard; Hatcher, John eds. Burton, Janet E. Monastic and Religious Orders in Britain, — Cobban, Alan B. Coss, Peter The Presence of Feudalism.

Cowie, Jonathan Climate Change: Biological and Human Aspects. Crouch, David Harlow: Pearson. Danziger, Danny; Gillingham, John London: Hodder and Stoughton. Davidson, Hilda Ellis Davies, R. Driver, Martha W. In Driver, Martha W. Jefferson, US: McFarland. Duggan, Charles Bulletin of the Institute of Historical Research. Dyer, Christopher Everyday Life in Medieval England. London: Hambledon and London. Forey, Alan Basingstoke, UK: Macmillan. Geddes, Jane Getz, Faye Marie Hackett, Jeremiah In Hackett, Jeremiah ed.

Roger Bacon and the Sciences: Commemorative Essays. Halsall, Guy Warfare and Society in the Barbarian West, In Beadle, Richard ed. Hicks, Michael The Wars of the Roses. Hillaby, Joe In Skinner, Patricia ed. Hooper, Nicholas a. Hooper, Nicholas b. Hughes, Malcolm K. In Hughes, Malcolm K.

The Medieval Warm Period. Dordrecht, the Netherlands: Kluwer Academic Publishers. Humphrey, Chris In Humphrey, Chris; Ormrod, W. Time in the Medieval World. Johns, Susan M. Johnson, Matthew Agency in Archaeology. Jones, Dan Summer of Blood: The Peasants' Revolt of London: Harper Press. Jordan, William Chester Kessler, Herbert L. Seeing Medieval Art. Toronto, Canada: University of Toronto Press. Kowalski, Maryanne Lavelle, Ryan Morillo, Stephen Warfare Under the Anglo-Norman Kings — London: Pimlico Press.

Normore, Calvin G. In Spade, Paul Vincent ed. The Cambridge Companion to Ockham. Ortenberg, Veronica London: Hambledon Continuum. Page, Christopher In Knighton, Tess; Fallows, David eds. Companion to Medieval and Renaissance Music. Rahtz, Philip; Watts, Lorna In Carver, Martin ed. Ramsay, Nigel Rose, Susan Medieval Naval Warfare, — Sawyer, P.

Skinner, Patricia Spade, Paul Vincent Thomas, Hugh M. Timmons, Daniel In Clark, George; Timmons, Daniel eds. Turner, Hilary L. Town Defences in England and Wales. London: John Baker. Tyerman, Christopher England and the Crusades, — Webb, Diana Pilgrimage in Medieval England. London: Hambledon. Capetian vassals also retained formal control over political and economic policies, which played a key role in the success of pivotal urban commercial institutions like the Champagne fairs [ 10 , 52 ].

Likewise, the expansion of Capetian political and economic interests into central and southern France saw the rapid integration of these regions into the French urban system via the enfranchisement of existing urban centers and the establishment new, planned towns bastides [ 46 , 52 , 59 , — ].

Given the backdrop of Capetian military hegemony, cities across France neglected to enclose their rapidly expanding suburban sprawl [ 4 — 6 , 99 , , ]. By the late thirteenth century, the centralizing reforms of Phillip IV—which retracted city self-government charters, centralized urban administration under the aristocracy, and restricted the flow of trade through the Champagne fairs—further cemented the integration of the French urban system [ 4 — 6 , 10 , 46 , 52 , ].

Like the other neighbors of Capetian France, the cities of modern Belgium, specifically Flanders, Artois, Hainault, Brabant, and Wallonia, were increasingly integrated into the French urban network during the thirteenth century. To be sure, urban self-governance in the Low Countries played a major role in the development of urban capital and labor markets, extensive craft and textile industries, commercial institutions, and long-distance trade [ 4 , 18 , 53 , ]. But the Capetian monarchy repeatedly tried to impose vassalage and annexation on both free cities and seigneurial counties across Artois, Hainault, and Flanders from the late twelfth to the early fourteenth centuries.

These areas became increasingly dependent on French grain, access to the Champagne fairs, and French demand for urban manufactures. Indeed, the regional economic disruption caused by the reforms of Philip IV and his repeated invasions of, and compromises with Flanders only served to further integrate the region [ 4 , 5 , 10 , 46 , 53 , , ]. Because of the need for defense and to control trade, wealthier cities in the Low Countries were often characterized by extensive walled circuits—if they bothered to extend their walls at all—which enabled indefinite suburban growth [ 4 — 6 , 53 , , ].

To evaluate the the data against the predictions of the two models developed above, we compare OLS regression parameters of the log-transformed population and settled area estimates. More specifically, the log-linear OLS estimates for the two regression parameters are directly compared to the corresponding theoretical expectation. As noted above, the predictions and confidence intervals of the two models overlap, so we can only exclude the structured interactions model in the limit of strong segregation.

Although this inhibits statistical hypothesis testing, if hierarchical institutions have a dampening impact on social interaction, we would expect the scaling exponent of the urban system to be systematically closer to 1. We estimate scaling exponents and prefactors through ordinary least-squares OLS regression of the natural logarithm of areal extent against the natural logarithm of population size:. Gaussian white noise. Estimations were done using R version 3. Scatter plots for the dependent versus independent variables show linear relationships indicating that the model is not misspecified for either the regional urban systems Fig 3 or the pooled data Fig 4.

The regression results for four regional European urban settlement systems, and the pooled dataset, are given in Table 1. Notes: Estimations done using OLS with corrections made for heteroscedasticity. Standard errors are in parentheses, and confidence intervals are in brackets all scaling coefficients are sig. See text for details. Thus, medieval cities across Western Europe exhibit, on average, economies of scale with respect to spatial agglomeration such that larger cities were denser on average.

It is notable that the estimated prefactors cluster quite closely together, with point estimates for the prefactors varying only between 0. According to the models above, a should vary in accordance with differences in the relationship of interaction benefits to intra-settlement transportation costs across urban systems. Since transportation technology was relatively constant across medieval Europe, any differences in the scaling prefactor should reflect differences in the productivity of social interaction. In general, this suggests that average net benefits of social interaction were relatively constant across Western Europe ca.

More importantly, the scaling coefficients are quite similar across regional urban systems and ithe pooled data , with point estimates varying between 0. For this reason we cannot detect evidence for socioeconomic interaction-dampening caused by hierarchical institutions. Nevertheless, the consistent values of the estimated scaling exponents imply that average net socioeconomic interaction benefits fell within the modern range and were not dampened towards unity. This suggests the hierarchical institutions of medieval urban systems did not have a strongly-restrictive impact on urban socioeconomic interactions, at least along the lines predicted by the structured interactions model in the aggregate population-area relationship of these settlements.

In the S1 Appendix we demonstrate that variation in both estimated regression parameters is so low across groups that any differences between them are not statistically significant S1. For this reason, we refrain from interpreting the differences in estimated parameter values among urban systems.

Despite their many structural differences, and a temporal distance of years, medieval urban centers share at least one basic similarity with modern cities: larger settlements have higher population densities than their smaller counterparts within a given urban system. Overall, the data conform to the expectations of the social reactor model, as the population-area relation does not provide evidence for disappearing scaling effects.

Even though medieval cities were structured by hierarchical institutions that are ostensibly not so dominant today, we interpret this finding as excluding a strongly segregating role for medieval social institutions. This would suggest that the institutions of Western European urban systems ca.

We take these findings as an indication that the underlying micro-level social dynamics of medieval cities were fundamentally similar to those of contemporary cities. Notwithstanding their many structural and functional differences, and contrasting macro-level processes that influenced urbanization in each regional system, both medieval and modern cities appear to be characterized by social networks that become increasingly spatially-dense as they grow. The results presented here also have contextual implications for understanding urbanization in Western Europe ca.

In particular, the prevalence of strong spatial agglomeration across fairly diverse urban systems implies that inter-regional differences were limited in their consequences.

Later medieval France: the polity | University of Exeter

As noted above, Italian cities might have been expected to exhibit stronger densification with city size due to numerous capital cities, many centuries of organic urban growth, and circumscription by defensive walls. But the observation of statistically indistinguishable agglomeration effects in England, France and Belgium—with uninhibited suburbs, fewer political capitals, and a much larger proportion of relatively young cities—suggests that the common process of commercial integration and concomitant demographic nucleation was the primary driver of this trend. Even though larger cities had greater impulses toward suburbanization, larger cities were nevertheless increasingly dense.

As such, the forces that caused agglomeration outweighed those causing suburbanization and segregation. Based on the assumption that the observed demographic-spatial agglomeration was driven by socioeconomic network effects, it might therefore be argued that the net impact of medieval urban institutions was to increase per capita interaction rates in order to facilitate greater organizational efficiency, productivity, and functional diversity.

This emphasis on intra-urban interaction in no way negates the influence of macro-level urban system dynamics on the historical evolution of those urban systems. Rather, we argue that urban system dynamics emerge precisely because of the capacity of cities to facilitate greater social interaction and productivity [ 78 ].

Indeed, medieval European urban systems were central to the development of socioeconomic and political institutions that expanded the division and coordination of labor, centralized networks of commodity flows, and intensified political and economic organization [ 1 , 3 — 6 , 9 — 18 , 24 , 27 ]. These systemic processes are well documented in historical sources, and our analysis offers a quantitative model for the intra-urban causes of their emergence. The similar quantitative relationship between areal extent and population size exhibited by medieval European urban systems and contemporary urban systems also suggests that modern and medieval cities may share similar underlying social processes.

In this way, theories of contemporary urban processes e. But rather than assuming that modern theories either do or do not apply to the past, this approach forces us to consider how and why past social, economic, and political conditions impacted the structure and dynamics urban systems. By providing an analytical framework and a set of theoretical expectations, scaling analysis can make contributions to a wide variety of topics in social and economic history.

Indeed, settlement scaling theory operates in terms of quantities that are common to human settlements regardless of time and place, similar to rank-size analysis and the methods of central place theory. With larger and more comprehensive datasets it may be possible to compare scaling relations across smaller urban regions, which may shed light on important differences among them. Time-series data on the changing scaling relations of urban systems may also reveal crucial long-term patterns in their evolution.

Likewise, scaling analysis of large, quantitative urban morphology datasets might help assess general patterns in the intra-settlement dynamics of the built environment. If such analyses are successful in identifying patterned variation associated with specific historical contexts, the theoretical framework of settlement scaling will be able to produce useful historical contributions to the study of pre-modern urbanization.

It is our hope that scholars of history will engage with, cross-pollenate, and critique such research as we carry it into the future. The database was constructed by collecting population and settled area data on medieval settlements in Western and Central Europe from both the secondary literature and tertiary databases.

Bairoch et al. No primary historical or archaeological research was conducted in the construction of the database. Settled area estimates were either measured from published historical and archaeological maps using ImageJ software , or from scholarly estimates in the secondary literature.

Population estimates were drawn from the secondary literature and tertiary databases. All population and area estimates were focused on the early fourteenth century, roughly spanning a 40 year period from ca. Quantitative data on medieval settlements are inherently contingent on philological interpretations of both population proxies, their geographical referents, secondary reconstructions, and constituent historiographical paradigms.

Moreover, the sources to which we had access were of variable quality and abundance across regions. In order to deal with these problems, we developed a data collection protocol that systematically compared and contextualized different quantitative estimates when possible. Rather than blindly assembling data through uncritical use of available sources, our method enabled us to evaluate the degree of confidence in each estimate. The core of this method involved the following steps:. We noted the major trends how were these identified?

We collected population and area estimates from works representing the most recent school of thought about each place, whenever possible;. We noted population and area estimates from major tertiary compilations of population and area data [ 58 , 59 , ]; and.

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We compared the available evidence for each case to arrive at a single provisional estimate suitable for scaling analysis. Conflicting population estimates of medieval settlements are common, and this makes careful source criticism especially important. In cases where recent secondary sources were available, we analyzed the context, methods and reasoning behind each population figure in order to inform our estimate.

This was possible for all English cases because the evidence exists on a national scale nationwide tax rolls , as well as many cases in France, Belgium and Italy. Secondary literature was available for only the largest German cases. Although population data are available from many sources for both England and Italy, we decided to use the well-researched datasets of Campbell [ 44 ] and Malanima [ , ], respectively, because of their high quality. These datasets matched our own independent estimates very closely for both regions see S2 Appendix for more details.

This involved giving preference to estimates made by recent and reliable secondary sources that were written by experts on a particular urban system e. The reliability of sources was gauged by how many well-cited research publications an author had on the particular subject and whether the cited reference was an in-depth analysis or a passing comment based on preliminary evidence or assumptions. If scholarly discussions were unavailable, we began to narrow down the set of possible estimates by removing estimates from older sources that stood apart from more recent sources.

We chose single estimates that seemed most accurate given the historical context. If multiple estimates could not be differentiated based on historical context, we took the mode or the average of all available estimates as our point estimate for the population of a given city ca. AD see S2 Appendix for more details. The same procedure was followed for the estimation of settled areas when conflicting estimates were encountered. Topographical map reconstructions of medieval European cities produced by archaeologists and historians are often scattered or embedded in case-specific studies of particular locales—making the compilation and measurement of settled area data a very time consuming endeavor.

While it would have been relatively simple to only measure the walled or fortified areas of medieval towns, many settlements had shifting extramural suburbs that extended beyond their walls, and others never filled their walled areas completely. Either way, these intramural and extramural areas are reflected in contemporary population counts [ 59 , — ], which necessitated that our settled area estimates explicitly take extramural suburbs and intramural vacant space into account. Accordingly, our method involved 1 conducting a survey of the archaeological and historiographical literature on medieval urban regions, 2 collecting maps, area estimates, and contextual specifications on each case, and 3 comparing the available evidence of each case to arrive at a single provisional estimate suitable for analysis.

When measuring maps for their settled areas, it was necessary to contextualize the map to understand the chronology of its features and weigh alternative area estimates. For example, we found a number of conflicting area estimates for Bristol ca. Kermode indicates that its walled area was 55 ha [ ], but Russell estimated that the settled area had to have been over 80 ha due to suburban sprawl ca.

The red line indicates the ha settled area we measured for the city, whereas the inner area circumscribed by walls and rivers measures only 55 ha. In other cases we had less information. For example, we collected two area estimates for Carcassone: 68 ha and 40 ha, without a map or detailed context to choose between them. Nevertheless, extramural suburban sprawl is known to have been a very common pattern of cities in the French Midi ca. These examples embody the general process of estimation for each of our cases see S2 Appendix for more details.

Spatially, medieval European cities were nucleated urban agglomerations that were qualitatively distinct from their rural hinterlands [ 4 — 7 , 97 , 98 , — ]. But while the urban-rural transition was often gradual, these settlements possessed definable spatial boundaries that differentiated them from associated rural hinterlands [ 6 , 8 ]—thus making it possible to measure the areal extent of urban spaces. Because of their spatial, political, economic, and social differences from the countryside, medieval European cities can also be said to have had definite resident populations.

Indeed, medieval writers recorded numbers of hearths, soldiers, taxpayers and citizens from distinct urban areas, and it is by extrapolating these figures that historians have estimated their total populations [ 59 , — ].

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These urban populations could fluctuate over time, and the empirical bases for these estimates vary in accuracy and precision across cities. Despite careful data collection, the match between settled area estimates and population estimates is not always precise. The walls of European towns were increasingly expanded over the course of the later middle ages, and it is well attested that the suburbs of medieval settlements fluctuated greatly, rapidly and asynchronously in accordance with economic and demographic trends [ 4 , 5 , 7 , 97 , 98 , , ].

We deliberately collected estimates pertaining to the turn of the fourteenth century ca. Fortunately, our focus here is not on individual estimates, but on the overall relationship between population and settled area within and between regions. Thus, so long as errors in existing estimates are unstructured they should be adequate for the purposes of this paper.

Not unlike settled area measurements from nineteenth- and early twentieth-century sources, this level of resolution is simply the reality of the available evidence. This appendix includes alternative delineations of medieval urban systems, the scaling analysis of these alternative urban systems, and ANOVA for all urban system groupings analyzed in the main text and in the S1 Appendix.

This appendix includes tables of all historical population and settled area estimates that went into the database, the methods and reasoning behind each data point, a brief overview of how to read these tables, and a comprehensive bibliography of the sources. This is a. We thank Bruce Campbell for sharing data and useful advice about population and area estimation. We also thank Paige Cesaretti and Janet Brooks for their help with data organization.

Scott Ortman's James S. McDonnell Foundation Grant grant ; www. National Center for Biotechnology Information , U. PLoS One. Published online Oct 5. Bettencourt , 3 Scott G. Ortman , 3, 4 and Michael E. Smith 1. Scott G. Michael E. Celine Rozenblat, Editor. Author information Article notes Copyright and License information Disclaimer.

Competing Interests: The authors have declared that no competing interests exist. Performed historiographical research: RC. Produced the dataset: RC SO. Wrote the appendices: RC. Received Oct 26; Accepted Aug This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article has been cited by other articles in PMC. S2 Appendix: Database Construction. Abstract Medieval European urbanization presents a line of continuity between earlier cities and modern European urban systems.

Introduction Scholars have long debated the role of the medieval city in the long-term economic development of Europe. Models of Social and Spatial Organization in Cities Medieval cities provide an opportunity to discuss two seemingly disparate views of cities. The Structured Social Interaction Model We now show how social groups and institutions can mediate social interactions and introduce further constraints to urban networks that can change basic scaling relations quantitatively.

Open in a separate window. Fig 1. Schematic Social Networks of Towns and Cities. Medieval European Urban Systems Networked Interactions in Medieval Cities By the early fourteenth century, the urban systems of Western Europe had undergone centuries of continuous socioeconomic and demographic development. The Impact of Networked Interactions on Spatial Density Having undergone roughly two centuries of continuous growth, the spatial morphology of mature medieval cities ca. Regional Urban Systems Historians have noted important differences among the cities of medieval Western European regions.

Fig 2. Map of Western European Settlements ca. Northern Italy The cities of Northern Italy formed a distinctive network of historically tethered and strongly interconnected city states.

England in the High Middle Ages

France and Belgium As in England, the cities of the vast Capetian French polity and its vassals experienced enormous growth and integration during thirteenth century. Results Model Implementation To evaluate the the data against the predictions of the two models developed above, we compare OLS regression parameters of the log-transformed population and settled area estimates.

Italy 30 0. Fig 3. Fig 4. Discussion Despite their many structural differences, and a temporal distance of years, medieval urban centers share at least one basic similarity with modern cities: larger settlements have higher population densities than their smaller counterparts within a given urban system. The core of this method involved the following steps: We noted the major trends how were these identified?

Population Estimation Conflicting population estimates of medieval settlements are common, and this makes careful source criticism especially important.