Widows in Venice

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Motivation

This is not really a motivation of the project, but rather a project description.

Goal: The goal of this project is to analyze the socio-economic status and property dynamics of widows as recorded in two historical datasets, the Catastici and the Sommarioni. By comparing these datasets, the project aims to uncover patterns in property ownership, tenancy, and rent payments among widows and to investigate changes in their living conditions over time.

Who are the widows recorded in the Catastici and Sommarioni datasets? This phase will involve locating and identifying widows in both datasets, gathering key demographic information (such as name, property status, and rent details), and understanding their distribution.

Are widows more likely to be property owners (landlords) or tenants? This question focuses on determining the proportion of widows who owned properties versus those who rented them. Understanding this ratio will provide insights into their economic standing.

Do widows pay different rents compared to other tenants for similar types of properties? Here, the project will investigate whether widows receive any preferential treatment or experience discrimination in terms of rent compared to other demographic groups in the datasets.

How does the ownership or tenancy of widows change between the two datasets (Catastici and Sommarioni)? By comparing property ownership or tenancy records between the two datasets, this question aims to explore whether widows' economic positions (through property ownership or rent status) improve or decline over time. For example, do they acquire more properties, or is there evidence of downsizing?

What patterns emerge from comparing widows across the two years? By analyzing widows’ presence in both datasets, this phase will search for trends regarding widowhood and economic mobility. For instance, did certain social classes of widows fare better or worse in retaining or expanding their property holdings?

Extra: If time look at the Tassini and see if and how they mention widows

Historical background

Where do we want to find this information? What are good sources to use? --> Google Scolar?

When reading about the history, add knowledge here with references to the sources used.

Venice is part of the Venetian Republic. It falls to Napoleon in 1796. Venice is given to the Austrian Monarchy by the French Republic as part of the Treaty of Campo Formio. Then it became French again and then Austrian.

https://www.napoleon-series.org/research/government/diplomatic/c_campoformio1.html

There is no major Palgue Epidemy during our period of focus [1]

The Venetian Society has strong gender roles and has a class system:

- Patricians (there names are probably in the libro d'Oro - Citizens (Popolani) - Commoners

I wish I had access to : [2]

this looks cool : [3]

Project Plan and Milestones

Workflow

Week	Task	Status
07.10 - 13.10	Define project and structure work	In process
14.10 - 20.10	Write code to extract widow data Read historical papers on widows and Venice ||
21.10 - 27.10	Autumn vacation
28.10 - 03.11	Who are the widows recorded in the Catastici and Sommarioni datasets?
04.11 - 10.11	Analysis: Property ownership Comparative rent analysis Prepare for the presentation ||
11.11 - 17.11	Midterm presentation on 14.11 Continue analysis (details to be specified) ||
18.11 - 24.11	To be determined
25.11 - 01.12	To be determined
02.12 - 08.12	To be determined
09.12 - 15.12	Finish writing wiki and prepare presentation
16.12 - 22.12	Deliver GitHub + wiki on 18.12 and final presentation on 19.12

DataFrame Column Descriptions

Descriptions of DataFrame Columns

Column Name	Description
uidx	Unique identifier for each record in the DataFrame.
id	A general identifier that may represent a specific entity or object.
owner_name	The name of the owner associated with the record.
owner_code	A unique code assigned to the owner for identification purposes.
owner_count	The number of owners associated with the entity.
owner_count_remark	Additional remarks regarding the count of owners.
owner_entity	The type or category of entity that the owner represents.
owner_entity_group	A classification grouping for similar owner entities.
owner_first_name	The first name of the owner.
owner_family_name	The family name (surname) of the owner.
owner_family_group	A grouping based on family relations or associations.
owner_title	The title held by the owner (e.g., Mr., Mrs., Dr.).
owner_title_std	Standardized version of the owner's title for consistency.
owner_mestiere	The profession or occupation of the owner.
owner_mestiere_std	Standardized version of the owner's profession for consistency.
ten_name	Name associated with a specific tenement or property.
function	The role or function of the owner within the entity or organization.
an_rendi	Year of rendering or reporting associated with the record.
id_napo	Identifier related to a specific geographic or administrative area (Napoli).
quantity_income	The total quantity of income reported or associated with the entity.
quality_income	Qualitative assessment of income, indicating its nature or source.
author	The individual or entity responsible for creating or maintaining the record.
place	Geographical location related to the record.
sestiere	A subdivision or district within a city, often used in urban contexts (especially Venice).
uid	Unique identifier used across different datasets for cross-referencing.
path_img	File path or URL to an image associated with the record.
geometry	Geospatial data representing the physical location or boundaries of an entity.
parish_std	Standardized name of the parish related to the record for consistency.

Results

Should include graphs and plots of the data/ just some data visualisation

Interpretation of the data

it's essential to contextualize the findings within the broader political and economic shifts in Venice (e.g., Napoleonic occupation, decline of the Republic). How did these events impact inheritance patterns or gender roles in property ownership?

Deliverables

The GitHub page of this project can be found at the following address: Rich Widows

Limitations

what limitations did our analysis have, was the data lacking. How sure are we that the results presented are true?

Credits

Course: Foundation of Digital Humanities (DH-405), EPFL

Professor: Frédéric Kaplan

Supervisor:

Authors: Eglantine Vialaneix, Nathanaël Lambert, Lisa Marie Njå

Date: 18.12.2024