MedievalAvignon

Reconstruction of the map of Avignon during medieval times

• Copyright 2020-2024 Vincent Labatut & Margot Ferrand

MedievalAvignon is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation. For source availability and license information see licence.txt

• Lab site: http://lia.univ-avignon.fr/
• GitHub repo: https://github.com/CompNet/MedievalAvignon
• Data: https://doi.org/10.5281/zenodo.12804379
• Contact: Vincent Labatut vincent.labatut@univ-avignon.fr, Margot Ferrand margot.ferrand@alumni.univ-avignon.fr

If you use this source code or the associated dataset, please cite reference [FL'24]. bibtex @Article{Ferrand2025, author = {Ferrand, Margot and Labatut, Vincent}, title = {Approximating Spatial Distance Through Confront Networks: Application to the Segmentation of Medieval {A}vignon}, journal = {Journal of Complex Networks}, year = {2025}, volume = {13}, number = {1}, pages = {cnae046}, doi = {10.1093/comnet/cnae046}, }

Description

This set of R scripts aims at extracting and analyzing confront networks extracted from raw historical tables. It does the following: 1. Extracts various networks based on some tabular data built based on historical sources. 2. Computes a number of statistics and generates the corresponding plots. 3. Performs additional analysis of the networks.

Data

The raw dataset was manually constituted by Margot Ferrand during her PhD. The detail of her historical sources is given in her PhD [F'22]. The output files (graphs, plots, tables...) can be obtained by running the scripts, but they are also directly available on Zenodo.

Organization

Here are the folders composing the project: * Folder in: input data, including some actually used (subfolder analysis) and some currently not used (subfolder positioning). * Folder log: logs produced when running the scripts. * Folder out: contains the files produced by the R scripts. Subfolder social is not currently used (only subfolder estate is). Each subfolder contains the networks, stats, and plots for a specific extraction scheme. Its name indicates which parameters are used for the extraction: * split vs. whole: linear/surface vertices are split to match spatial spread vs. they are kept as extracted from historical sources. * ext vs. raw: additional edges complementing the raw historical sources (e.g. between streets) vs. no additional edge (only those explicitly appearing in the historical sources). * full: all types of vertices and edges are kept. * estate: keep only the vertices defined at spatial the level of the building (i.e. no long streets or other linear entities, surface entities such as villages). * flat_relations: keep only the edges representing flat relations (by opposition to hierarchical relations, such as "belonging to"). Also get read of long distance relationships (even if flat). * flat_minus: same as flat_relations, but certain objects are also removed (walls, rivers), as well as the longest streets. The latter are indicated by a numeric value x corresponding to the top x longest streets (with whole) or to a minimal length threshold (with split). * filtered: remove isolated vertices and small components. * Folder res: contains the R source code. * Folders lib and src: contains the Java libraries and source code, currently not used.

Installation

You first need to install the R language, as well as the required packages:

1. Install the R language
2. Download this project from GitHub and unzip.
3. Install the required packages:
   1. Open the R console.
   2. Set the unzipped directory as the working directory, using setwd("<my directory>").
   3. Run the install script res/_install.R (that may take a while).

Use

In order to extract the networks from the raw data, compute the statistics, and generate the plots:

1. Open the R console.
2. Set the current directory as the working directory, using setwd("<my directory>").
3. Run the main script res/main.R.

The scripts will produce a number of files in folder out/analysis/estate. They are grouped in subsubfolders, each one corresponding to a specific topological measure (degree, closeness, etc.).

Dependencies

Tested with R version 4.0.5, with the following packages: * CINNA: version 1.1.54. * DescTools: version 0.99.39. * future.apply: version 1.6.0. * ggplot2: version 3.3.3. * geometry: version 0.4.5. * GoodmanKruskal: version 0.0.3. * Hmisc: version 4.5.0. * igraph package: version 1.2.6. * pcaPP: version 2.0.1. * plotfunctions: version 1.4. * rPref: version 1.3. * SDMTools: version 1.1.221. * sjstats: version 0.18.0. * stringr: version 1.4.0. * viridis: version 0.6.0.

To-do List

• ...

References

• [F'22] M. Ferrand Usages et représentations de l'espace urbain médiéval : Approche interdisciplinaire et exploration de données géo-historiques d’Avignon à la fin du Moyen Âge, PhD. Thesis, Avignon University, 2022. Web Page
• [FL'24] M. Ferrand and V. Labatut. Approximating Spatial Distance Through Confront Networks: Application to the Segmentation of Medieval Avignon. Journal of Complex Networks, 13(1):cnae046, 2025. DOI: 10.1093/comnet/cnae046 - ⟨hal-04786705⟩