Widows in Venice

Project Plan and Milestones

Workflow

Week	Task	Status
07.10 - 13.10	Define project and structure work	Done
14.10 - 20.10	Write code to extract widow data Read historical papers on widows and Venice	Done
21.10 - 27.10	Autumn vacation
28.10 - 03.11	Comparative rent analysis (catastici) Property ownership Heritage
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
18.11 - 24.11	Finish property ownership analysis - Sommarioni & Catastici Finish comparative rent analysis - Catastici
25.11 - 01.12	Start widow heritage analysis Social aspect: see how many times words/titles are used to describe different groups of people (‘Vedova’, ‘Consorte’, …) Start writing down findings in the wiki
02.12 - 08.12	Compare all the different analyses done and see if one can extract general trends for widows or interpret some of the results
09.12 - 15.12	Finish writing wiki Prepare presentation
16.12 - 22.12	Deliver GitHub + wiki on 18.12 Final presentation on 19.12

Methodology

Property ownership analysis

For the porperty ownership analysis for the widows mentioned in the Catastici and Sommarioni a similar approach was used. First the widows where located using the keyword "vedova", meaning widow, and "consorte", meaning wife of dead husband. After filtering the datasets using row-wise text matching for these keywords, the entries of the widows where saved. These new data sets where then used as the basis of further analysis.

How specific should we be here? for every analysis give the exact mehtod or no

Results

Sommarioni property analysis

Using the methods described in #Property ownership analysis, the study identified 659 entries related to widows out of a total of 23,400 entries in the Sommarioni. Since this dataset includes only property owners and excludes tenants, no conclusions can be drawn about the number of widows renting properties. Similarly, due to some widows owning multiple properties, it is impossible to determine the total number of widows living in Venice during this period.

When looking at how much property one widow holds, its important to ensure that it's the same widow. When comparing the data it appears that in the 'owner' category there are 443 unique owners, whilst in the 'owner_standardised' there are only 360 unique widows. This means that there must be some typos and errors in the way the widows are written in the 'owner' section compared to the cleaned and standardised section, which is as expected. When looking at the new list of widows, it is still possible to see the same widows, but written differently and further refinement is therefor necessary. After looking for similarities in the names, there are around 246 unique widows.

Most widows own a single property, as illustrated in the histogram below, which shows an exponential decrease in ownership frequency with increasing property counts.

From the data:

• The majority of widows own one property.
• The graph shows similarities to an exponential decay.
• The maximum observed ownership is 25 properties, held by Loredana Grimani, wife of Giovanni Morosini. This exceptional case may indicate significant wealth, and further investigation into the Grimani-Morosini family could provide more context.

The following graph compares the proportion of properties owned by widows to those owned by the general population in each district:

This comparison reveals significant regional differences:

• In Cannaregio, widows own a disproportionately large share of properties compared to the general population.
• In Castello, widow property ownership is notably lower than that of the general population.
• In Dorsoduro, San Marco, and San Paolo, widows own slightly more properties than average, while in Santa Croce, widows own slightly fewer properties.

These findings suggest that socio-economic and demographic factors may influence the distribution of widow property ownership across districts.

The graph below shows the normalized average area of properties owned by widows in each district, compared to the average property size in that district:

The graph below shows what the average area of a property owned by a widows in a given district is, normalized by the average area of the properties in that district. This might give an indication of the wealth of the different districts. Though it has to be said, that the area given in the Sommarioni is likely computed from the vectorization available in the GeoJSON file.

Key observations include:

• In Castello, widow-owned properties are approximately 40% larger than the average, a notable finding considering the low number of widows holding property there. This discrepancy may reflect wealth concentration among widows in Castello.
• In Dorsoduro, the average property size for widows is comparable to the district average.
• In other districts, widow-owned properties are generally smaller than the average, suggesting a relatively worse economic situation for widows in these areas.

The final aspect of the analysis focuses on the types and functions of widow-owned properties. The graph below shows the distribution of properties by the number of distinct functions they serve:

From this data:

• Most properties serve a single function, while over 100 properties serve two functions.
• A smaller number of properties have three or four functions, which may reflect detailed notations in the Sommarioni or unique uses of these properties.
• Of the 659 widow-owned properties, 555 are rented (partially or fully), while 104 are not rented at all. The non-rented properties primarily include vegetable gardens (orto) and covered walkways (sottoportico).
• Only nine widows are listed as living in the properties they own, an unexpectedly low number that may merit further investigation.

Comparison

Heritage analysis

The inheritance of property by widows in Venice offers insight into historical family dynamics and property ownership structures. This study examines links between property records in the Catastici and Sommarioni to identify patterns of inheritance. The analysis focuses on widows who owned property, as tenants are not mentioned in the Sommarioni.

Catastici to Sommarioni

Of the 61 widow-owned properties in the Catastici, only 16 contained valid id_napo values, enabling direct comparison. Manual inspection of these entries yielded the following results.

For seven of the entries there was no apparent relationship between the widow-owned properties in the Catastici and corresponding entries in the Sommarioni. For example, the property linked to id_napo 4270 (Catastici: Gerolema; Sommarioni: DA' RIVA Giovanni Battista) showed no familial or functional connection.

For another seven of the entries there is a possible relationship between the two datasets. Several cases suggested familial inheritance, often indicated by shared last names between the Catastici and Sommarioni entries. An example of this is id_napo 4896, where in the Catastici the owner of a house with a shop is called Elena Vianol (widow of Ferigo Renier). In the Sommarioni the owner is called Renier Bernardino, which is likely a family member.

Other recurring patterns seen in this analysis is that Elena Vianol (widow of Ferigo Renier) appeared in multiple instances where properties were inherited by individuals with the surname Renier. Paolina Mocenigo (widow of Michiel Morosini) showed a similar trend, with properties inherited by Morosini Elisabetta.

For some properties, the presence of both the widow's and her husband's names in different entries suggests a time lag in documentation, with the husband potentially recorded before his death and the widow afterward.

Sommarioni to Catastici

When attempting to trace properties from the Sommarioni back to the Catastici, 388 potential links were identified based on matching parcel numbers. Due to the large number of matches, computational methods were employed to identify connections.

The general idea would be to look if there is a similarity in their names. A problem here is that the names are sometimes written differently ( ex. Bonvicini and Bonbicini) even though its the same familyname.

The analysis revealed clear inheritance patterns in several cases, particularly among prominent families like the Renier and Morosini. These findings suggest that property often stayed within family lines, with widows playing a transitional role in ownership. However, discrepancies in documentation and name variation posed challenges, underscoring the need for refined computational methods and historical context in future studies.

Deliverables

Our Github Repository

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

For our eyes only

Draft of the report plan:

TODO (guideline): -Project plan and milestones (5%) (>300 words) -Motivation and description of the deliverables (5%) (>300 words) -Detailed description of the methods (5%) (>500 words) -Quality assessment and discussion of limitations (5%) (>300 words)

TODO: - redo the plan table (nicely) - is it nice enough? -

TASK SPILT: - everyone write down their own results, discussion and limitations (merge the common ones) - we later split the rest of common sections

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	Done
14.10 - 20.10	Write code to extract widow data || Read historical papers on widows and Venice || Done
21.10 - 27.10	Autumn vacation
28.10 - 03.11	Comparative rent analysis ( catastici) Property ownership Heritage ||
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	Finish property ownership analysis - Sommarioni & Catastici Finish comparative rent analysis - Catastici
25.11 - 01.12	- Start widow heritage analysis - Social aspect: see how many times words/titles are used to describe different groups of people ( ‘Vedova’, ‘Consorte’, …) - Start writing down findings in the wiki
02.12 - 08.12	Compare all the different analysis done and see if one can extract general trends for widows or interpret some of the results
09.12 - 15.12	- Finish writing wiki - Prepare presentation
16.12 - 22.12	Deliver GitHub + wiki on 18.12 and final presentation on 19.12

Methodology

Data

Catastici

Descriptions of the Columns

Column Name	Description	coverage
uidx	?
id	?
owner_name	The name of the owner associated with the record.
owner_code	code of owner type
owner_count	number of owners (type int)
owner_count_remark	remark for owner count if exact number is not applicable (e.g. fratelli)
owner_entity	owner entity name (blank if owner is not an entity)
owner_entity_group	owner entity group standardisation (blank if owner is not an entity)
owner_first_name	owner first name (blank if owner is not an person)
owner_family_name	owner last name (blank if owner is not an person)
owner_family_group	owner family group standardisation (blank if owner is not an person)
owner_title	owner title (blank if owner has not title)
owner_title_std	standardisation or propagation of owner title
owner_mestiere	owner mestiere (blank if owner has no mestiere)
owner_mestiere_std	standardisation of owner title mestiere
ten_name	Name of the tenants.
function	?
an_rendi	?
id_napo	whenever found, the correspondingparcel in the Sommarioni 1808
quantity_income	?
quality_income	?
author	?
place	?
sestiere	the sestiere (district) of the entry as an acronym. Possible values are SM (San Marco), CS (Castello), CC/CN (Cannaregio), SP (San Polo), SC (Santa Croce) and DD (Dorsoduro)
uid	?
path_img	?
geometry	single point coordinate where the parcel is located in CRS84 format
parish_std	standardized name of the parish

Dictionary of useful words and idioms

Italian	English	Description
vedova	widow	-
mestiere	profession	-
parrocchia	parish	Parishes in Venice were local ecclesiastical districts, each centered around a parish church. Every house in Venice belonged to a specific parish, creating a network of smaller communities within the larger city.
sestiere	district of Venice	The name given to the districts of Venice: San Marco, San Polo, Santa Croce, Dorsoduro (which includes the island of Giudecca), Castello, Cannaregio.
frateli	brothers	plural of fratello
sorelle	sisters	plural of sorella
ved	widow of	short for vedova
quondam	son/daughter of	literraly : previously
fratelli quondam	this person + his brothers from the the father	-
fu di	of the late man	like "quondam" but the father is dead
q.m	abrevation of quondam
sudett-o/-a/-i	part of an other place	in the table sometimes multiple rows in a row belong to one place
consorte	married with	husband is dead
della fu	of the late woman

Sommarioni

Is a cadaster from 1808 showing the different properties in Venice (parcels) and their assigned parcel number. The information is given in a tabular form. Information includes: - parcel numbers: relates to a certain property - owner of the given property -...

Analysis

What data is comparable between the two datasets

• function of property
  • Catastici it is mentioned as function
  • Sommarioni it is mentioned as quality

• owners

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