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session3_mar22/README.md

@@ -11,7 +11,9 @@ Inscription (gratuite mais obligatoire) : [formulaire d'inscription](https://fra
 
 ### Inventaire, localisation et cartographie des lieux cités dans l'Encyclopédie de Diderot et d'Alembert.
 
-T. Joliveau (U. St-Etienne/EVS),
+Thierry Joliveau (U. St-Etienne/EVS)
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+[<img src="https://projet.liris.cnrs.fr/geode/seminaires-ixxi/session3_mar22/seminaire-session3-presentation1.png" width="650" />](https://projet.liris.cnrs.fr/geode/seminaires-ixxi/session3_mar22/seminaire-session3-presentation1.mp4 "Enregistrement présentation 1")
 
 Dans le cadre du projet interdisciplinaire [GEODE](https://geode-project.github.io) nous nous intéressons à comprendre comment l’Encyclopédie rend compte de la science géographique et des connaissances géographiques de son époque. Un volet de ce travail est de rendre compte de la manière dont les Encyclopédistes nomment, définissent, classent, décrivent et situent les objets géographiques dont ils parlent. La localisation des articles consacrés spécifiquement aux lieux par l'Encyclopédie de Diderot et d'Alembert est évidemment une question intéressante. Quels sont les lieux que cite l'Encyclopédie et ceux qu'elle ignore ? Le mode de la localisation et sa précision varient-ils en fonction de l'espace géographique, le continent par exemple ? Dans l'Encyclopédie, la localisation se fait soit de manière absolue, par des coordonnées en latitude et longitude, soit de manière relative en précisant une distance et une direction ou une relation topologique à un autre lieu.
 
@@ -21,11 +23,12 @@ Ce premier travail exploratoire ouvre le chantier d'un projet combinant Système
 
 
 
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 ### The COMMUNES project: a geospatial data infrastructure for France 1750 to the present
 
 Alexis Litvine (U. of Cambridge/CAMPOP), T. Thévenin (Univ. Bourgogne/ThéMA), I. Séguy (INED)
 
+[<img src="https://projet.liris.cnrs.fr/geode/seminaires-ixxi/session3_mar22/seminaire-session3-presentation2.png" width="650" />](https://projet.liris.cnrs.fr/geode/seminaires-ixxi/session3_mar22/seminaire-session3-presentation2.mp4 "Enregistrement présentation 2")
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 Establishing how many people inhabited a particular area and being able to define precisely its boundaries is not just an academic exercise. It is the cornerstone of historical, economic, and social scientific research. This exercise comes with several challenges, not least the significant landscape and boundary changes over the last two centuries.
  
 This session will cover the background and preliminary results of the [COMMUNES project](https://anrcommunes.hypotheses.org/le-projet-communes), which aims to do this in the long run for France. Combining modern GIS techniques and large historical databases it now possible to represent and analyse the spatial dimension of demographic and socioeconomic phenomena at an unprecedented level of accuracy. Municipal-level Historical GIS (H-GIS) data now exist for several countries, but France, despite the existence of all the necessary records, is clearly lagging in this respect. The COMMUNES project aims: i) to produce the first H-GIS providing annualised boundary data for all French municipalities from the Revolution to the present day; ii) to link each spatial unit to historical population data and metrics of access to networks of transport; iii) to develop a multimodal model of transport to allow the dynamic analysis of the relationship between economic and demographic changes and accessibility from the end of the 18th century to the present day; iv) to represent and analyse on a very fine scale some key demographic and socio-economic phenomena. In this paper we will present our method for reconstructing historic administrative boundaries combining the study of historical and cartographic sources, as well as other yet unexplored documents from local administrative archives and cadastral surveys and link them to existing census data. I will also introduce recent exploratory work aimed at creating population estimates for ancien régime parishes for the early 1780s. In this talk I will i) present our method to link ancient parishes to post-revolutionary communes, ii) present the historical sources used in this work, and iii) discuss the usefulness and potential pitfalls associated to using BMD data to reconstruct population estimates. 
@@ -35,3 +38,4 @@ Producing high-resolution, spatially disaggregated data for economic history and
 Reviving economic geography through spatially linked population, occupational and environmental data. Today, the scale of a region and the spatial relationships between economic activities, infrastructure and institutions are widely understood to be essential to the course of economic development. Yet nearly all existing historical information derives either from extremely localised case studies or has been estimated at the ‘national’ level on the basis of non-standardised proxies. This project brings together demographers, historians, geographers, spatial analysts, and economists to provide a solid empirical bases for historical study. 
 French economic development through population density: We explore reconstructing and mapping population density at a very fine level as a proxy for early shifts in occupational structure. Spatially disaggregated population data are crucial to the aims of our project: i) population data are typically available with more complete spatial coverage than most other types of data, are often available at finer levels of spatial disaggregation, and with higher frequency. ii) The distribution of population fundamentally reflects the spatial patterning of economic activities. iii) Population data is required as a denominator for the calculation of sectoral shares, participation rates, and as a check on the reliability of occupational datasets. Without precise population data all these other metrics are based on questionable assumptions. iv) Population data provide direct evidence of urbanisation, and indirect evidence of migration patterns. v) Population counts can be used to inform the collection of other types of data, where budgets are limited. For example, information on population geography can provide a basis for rational sampling for occupational data collection, as it can be digitised for a fraction of the cost of occupational data given the relative simplicity of the information required. 
 The structuring effect of transportation networks: Our data will offer major new opportunities to analyse the relationship between space, transport network, patterns of population distribution and economic development. The “structuring effect” of transport infrastructures on space is a popular theme in the literature yet it remains mostly an unverified phenomenon. Our geo-historical approach will allow us to both verify the role of population density on the architecture of transport networks and, conversely, the role of transportation infrastructure on population dynamics, and quantify rigorously the substitution effects from road and waterways to the railways.
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