Research Data for "The Waqf Engine: How an Islamic Institution Converted Urban Deficits into Permanent Urban Morphology in Ottoman Algiers (1580–1830)
Description
This dataset contains primary and processed data supporting the morphological and quantitative analysis presented in the article entitled "[Insert Full Article Title]". It is organised into three main components: Cartographic and morphological data: High-resolution scans and digitised layers derived from the 1831 Plan Filhon, alongside reconstructed maps of Ottoman Algiers used for Conzenian town-plan analysis. Quantitative waqf data: Statistical tables extracted from the 1837 waqf census, covering founder social categories, property distribution across 17 humas (urban quarters), and temporal records of institutional maintenance between 1580 and 1830. Methodological reproducibility: All datasets are structured to enable replication of both the morphological mapping and the quantitative assessment of the waqf "conversion mechanism" model proposed in the paper. Keywords: Ottoman Algiers; Waqf; Urban Morphology; Conzenian Analysis; Historical GIS; Islamic Urbanism
Files
Steps to reproduce
Steps to Reproduce: Download all dataset files from this repository. Open the folder containing the map files (Figures 1–6). These are high-resolution scans and digitised overlays of the 1831 Plan Filhon and reconstructed maps of Ottoman Algiers. Use any standard GIS software (e.g., QGIS or ArcGIS) or image viewer to examine the morphological layers. Open the Word document titled "Supplementary_Tables.docx". This file contains: Table 1: Social categories of waqf founders (1837 census). Table 2: Property distribution across the 17 humas of Ottoman Algiers. Table 3: Chronological records of institutional maintenance (1580–1830). To replicate the Conzenian town-plan analysis: Overlay the digitised map layers in GIS software. Apply the three-fold Conzenian framework (streets, plots, buildings) to identify morphological periods. To replicate the quantitative "conversion mechanism" model: Extract the numerical data from Tables 1–3. Use any statistical software (Excel, SPSS, R, or Python) to run the frequency and distribution analyses described in the methodology section of the article. All processed data are provided in raw and cleaned formats to ensure full reproducibility.