Widows in Venice

Introduction

Widows in historical societies often occupied a unique socio-economic position, navigating the dual challenges of loss and the constraints of societal structures. This project seeks to explore the socio-economic status and property dynamics of widows as recorded in two historical datasets: the Catastici and the Sommarioni. By comparing these records, it aims to uncover patterns in property ownership, tenancy, and rent payments among widows, shedding light on their economic mobility and societal roles.

The analysis is guided by several key questions. First, it seeks to identify and contextualize the widows recorded in both datasets, gathering demographic and economic details such as names, property statuses, and rent information. From this foundation, the study explores whether widows were more likely to be property owners (landlords) or tenants, offering insights into their relative economic standing.

Another critical focus is the comparison of rent dynamics. The project investigates whether widows paid different rents compared to other tenants for similar properties, exploring potential evidence of preferential treatment or discrimination. Additionally, by comparing widows’ ownership or tenancy statuses across the two datasets, the study examines changes in their economic positions over time—did widows acquire more properties or experience economic decline? Finally, broader patterns are analyzed to assess the extent to which socio-economic mobility varied among widows of different social classes.

Through this multi-faceted approach, the project aims to contribute to a deeper understanding of widows’ socio-economic roles and property dynamics, offering a nuanced perspective on gender, property, and economic mobility in historical contexts.

(example for introduction from chat - need to double check that it coincides with project and that it doesnt include things we didnt do)

Historical backdrop

Motivation

Project Plan and Milestones

The project is structured on a weekly basis, to ensure an even progression and workload. Each week has a clearly defined goal. The plan spans from the initial setup and data extraction through to final analysis and presentation, with clear milestones throughout.

The first phase of the project (07.10 - 13.10) is focused on defining the project's scope and structure. Here the focus was on creating a common understanding of the project to ensure good collaboration in the group. The following week data extraction of the widows in the two datasets started. In addition a review of historical papers on widows and Venice was done, providing the necessary context for the research (14.10 - 20.10). The analysis then shifted towards examining the widows mentioned in the Sommarioni and Catastici records. This stage involved comparative rent analysis and property ownership evaluation (8.10 - 03.11).

The mid-project milestones included a midterm presentation on 14.11, with further development of the analysis through the end of November (11.11 - 24.11). This phase focused on completing the property ownership and comparative rent analyses, as well as beginning to explore widow heritage and social aspects, such as the frequency of titles like "Vedova" and "Consorte" used in the records. These findings were progressively written into a shared wiki.

The final analysis phase, beginning 02.12, was dedicated to comparing the results of the previously conducted analyses, and identifying overarching trends related to widows in Venetian society. The last steps of the project (09.12 - 15.12) will involve finishing the wiki documentation and preparing the final presentation.

The project will conclude with the delivery of the GitHub repository and wiki on 18.12, followed by the final presentation on 19.12.

For a detailed overview of the workflow and corresponding milestones, see the table below.

Workflow

Week	Task
07.10 - 13.10	Define project and structure work
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	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
02.12 - 08.12	Compare all analyses to identify general trends for widows Interpret some of the results
09.12 - 15.12	Finish writing the wiki Prepare the presentation
16.12 - 22.12	Deliver GitHub + wiki on 18.12 Final presentation on 19.12

Dataset presentation

For this project the following two datasets will be used as a basis for conducting research.

• 

  Catastici

• 

  Sommarioni [3]

Catastici

Contains 32.123 records. Data for this register was collected walking door to door in a parish. By looking at the order of the transcribed data one can see which path was walked during the collection. The original Catastici contains the following five columns:

• Owner information
• Tenants
• Income from rent
• Place name
• Urban function

The entries in the Catastici were not written on a strict format, meaning the information given varies and can be very detailed but also lacking for some entries. Through standardisation of the data, additional columns where created to store information. These are for instance family name and owner title.

For the analysis the version "catastici_text_data_20240924.json" containing the transcription of the Catastici was mainly used. This set contains the original columns as well as the additional ones after standardisation.

Sommarioni

The 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 number, relates to a certain property
• Owner of the given property
• Quality: describing the function of the property

As with the Catastici, multiple columns have been added after digitalisation and standardisation of the cadaster to better store all the information. The Sommarioni does not contain any information on the tenants of properties being rented.

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

Heritage analysis

To explore inheritance patterns of widow-owned properties in Venetian records, two approaches where used:

Linking Catastici to Sommarioni Properties owned by widows in the Catastici were linked to entries in the Sommarioni through two primary methods:

• id_napo Matching: Directly relating id_napo (parcel numbers) from the Catastici to corresponding entries in the Sommarioni.
• Geometric Matching: Comparing spatial data where id_napo values were unavailable. This method was not applied in this project due to lack of time. But one could use the coordinates given in the Catastici and link it to parcels in the Sommarioni using the geojson.

Due to the limited amount of data avialable, only 16 entries with an id_napo in the Catastici, manual inspection was conducted to identify familial or functional connections.

Linking Sommarioni to Catastici Properties listed in the Sommarioni were traced back to the Catastici using parcel numbers and name similarity.

Using the parcel numbers from the Sommarioni, they were linked with the id_napo of the Catastici. To check for familiar relations between the owners a name similarity analysis was conducted. Here computational tools like difflib were used to compare widow names between datasets, accounting for spelling variations (e.g., "Bonvicini" vs. "Bonbicini"). A similarity threshold of 0.7 was applied.

This methodology allowed for a combination of qualitative and quantitative analysis, addressing historical inconsistencies while exploring inheritance patterns across records.

Results

Rent Analysis

What is the economical situation of widows in Venice in 1740 ?

When looking the entire city of Venice, we find only 120 properties involving widows from a total of more than 30k. This is about 0.36% of properties. We could expect this number to be closer to 15%. We are identifying the widows with keywords (consorte and vedova), so it is possible that some widows are mentioned with their name only, making them indistinguishable from unmarried women. Also, note that we cannot identify widows living at home or at a relative's place if they are not renting from them.

Most widows are probably able to own a house but not to have tenants, or they remarry, or they move to live with family members. This would explain why they don't appear in the Catastici. Also, widows in the Catastici are always either tenants or owners of a place.

We can therefore assume that there is three categories of widows :

• poor widows who have no choice but to rent a place
• supported widows who either own a home or have family support
• rich widows who own a property and are able to rent it

We will focus on the first and last categories, as we have no information about the middle one.

Charity

Not everyone is paying rent with money, or even paying rent at all. People can pay rent with money or goods (like sugar). However, no Widow owners was found receiving payment in goods and no widow tenants was found paying in goods. Properties with no rent paid (no good and no money) fall into three categories :

• charity, (for instance : "gratis": free , "per carità": per charity, "per grazia": per grace, "amore dei": for the love of God)
• refusal to pay, ("giurò non pagar affitto": swore not to pay rent)
• no comment

It is difficult to determine if no comment entries are mistakes and rent was actually paid or if they fall into charity or some sort of agreement between owner and tenants. But, if we only focus on entries for which we are sure that they are charity, it is clear that charity towards widow tenants is almost 7 times higher than charity in general. Widow owners were not found practicing charity, but remember that charity is rare and there is not a lot of widow owners, so this might not be a significant result.

Charity in Venice

	Total number of properties	Properties where no rent is paid (no good and no money)	Mentioned as charity
Venice	33,297 (100%)	3,115 (9.35%)	169 (0.50%)
Widow Owners	61 (100%)	10 (16.39%)	0 (0%)
Widow Tenants	59 (100%)	4 (6.78%)	2 (3.39%)

Rent in Venice [4]

The scale of Venice

If we now only consider properties for which we have quantifiable rents paid with money, we find that widows pay about 40 lirae less than median rent per property and when owning a place, they earn about 40 lirae more than median rent per property. Those difference represent a 30% difference to median rent.

A possible explanation for widows renting at lower price would be that they get discounts due to their social status. But, this type of positive discrimination towards widows wouldn't explain why widows own properties that are rented at a higher price. Considering the previous observation that charity is rare, this phenomena might be a consequence of our hypothesis : poor tenat widows live in small, cheap places while rich widow owners possess big, expensive houses.

But this asks further questions: Are those properties located in specific cheap or expensive neighborhoods ? Where are widows renting ? Where do they own properties ? And are we sure that what we observe at the macro level of the city still holds at more micro levels ?

Median Rent and Population in Venice

Median Rent	Widow Owners Median Rent Difference	Widow Tenants Median Rent Difference	Properties	Widow Owned Properties	Widow Rented Properties
124.0	+ 37.2	- 37.2	29999	48	55

Districts of Venice [5]

The scale of the districts

If we zoom in to the scale of districts, we can see that despite the high variations in rent between districts, the pattern of owners owning more than median rent and tenants renting at lower prices holds in almost every district.

Median Rent in the districts

Sestiere	Median Rent	Widow Owners Median Rent Difference	Widow Tenants Median Rent Difference	Properties	Widow Owned Properties	Widow Rented Properties Size
San Marco	210.8	+ 37.2	0.0	5697	5	4
Castello	124.0	+ 99.2	- 43.4	5774	14	8
Cannaregio	124.0	+ 55.8	- 6.2	6016	6	20
San Polo	142.6	+ 24.8	- 43.4	2930	3	3
Santa Croce	111.6	- 12.4	- 37.2	3218	5	10
Dorsoduro	86.8	+ 40.3	- 24.8	5835	4	5
Ghetto Novossimo	124.0	+ 24.8	- 62.0	529	11	5

Let's look in detail at each district. For each of them, we can identify the parishes in which widows are involved. Parishes represent local religious communities, but people don't always belong to the parish closest to where they live. In the following plots, parishes are represented by a line encircling all of it's members. Sometimes, non members happen to fall inside the parish's shape despite not belonging to it. To be able to visualize this case, properties not belonging to the widows parishes are shown with a smaller diameter.

Rent in San Marco [6]

San Marco

Parishes of San Marco

Parish	Median Rent	Widow Owners Rent Median Difference	Widow Tenants Rent Median Difference	Properties	Widow Owned Properties	Widow Rented Properties Size
San Bortolomio	223.2	+ 24.8	-	503	2	0
San Luca	161.2	-	-49.6	503	0	1
San Paternian	186.0	-	+ 124.0	178	0	1
San Salvador	272.8	5.2	-	521	1	0
Sant'Angelo	186.0	-62.0	-	506	1	0
Santa Maria Zobenigo	183.4	+ 126.6	+ 529.6	256	1	2

Rent in Castello [7]

Castello

Parishes of Castello

Parish	Median Rent	Widow Owners Rent Median Difference	Widow Tenants Rent Median Difference	Properties	Widow Owned Properties	Widow Rented Properties Size
San Giovanni in Bragora	124.0	+ 84.3	-52.7	399	2	4
San Martin	86.8	+ 49.6	-6.2	563	3	2
San Pietro di Castello	99.2	+ 86.8	+ 142.6	1495	2	1
Santa Maria Formosa	186.0	+ 62.0	+ 434.0	747	7	1

Rent in Cannaregio [8]

Cannaregio

Parishes of Cannaregio

Parish	Median Rent	Widow Owners Rent Median Difference	Widow Tenants Rent Median Difference	Properties	Widow Owned Properties	Widow Rented Properties
San Cancian	124.0	-	-62.0	629	0	1
San Felice	186.0	-	+ 434.0	351	0	1
San Geremia	99.2	+ 148.8	-49.6	1082	1	1
San Giovanni Grisostomo	186.0	-86.8	-	187	1	0
San Lunardo	148.8	-	-74.4	117	0	3
San Marcilian	124.0	-	+ 136.4	589	0	2
San Marcuola	117.8	-	+ 83.7	1432	0	6
Santa Fosca	124.0	+ 620.0	+ 347.2	163	1	1
Santa Maria Maddalena	124.0	-	-37.2	119	0	1
Santa Maria Nova	198.4	-	-99.2	183	0	2
Santa Sofia	136.4	-	0.0	546	0	1
Santi Apostoli	161.2	-49.6	-99.2	618	3	1

Rent in San Polo [9]

San Polo

Parishes of San Polo

Parish	Median Rent	Widow Owners Rent Median Difference	Widow Tenants Rent Median Difference	Properties	Widow Owned Properties	Widow Rented Properties
San Mattio	136.4	+ 31.0	-37.2	319	2	1
San Polo	161.2	-117.8	-	353	1	0
San Toma	136.4	-	+ 24.8	272	0	2

Rent in Santa Croce [10]

Santa Croce

Parishes of Santa Croce

Parish	Median Rent	Widow Owners Rent Median Difference	Widow Tenants Rent Median Difference	Properties	Widow Owned Properties	Widow Rented Properties
San Cassiano	186.0	-124.0	-	546	1	0
San Giacomo dall'Orio	99.2	-	-55.8	657	0	1
San Simeon Apostolo	99.2	-	-62.0	198	0	1
San Simeon Profeta	93.0	-	0.0	447	0	1
Santa Croce	111.6	-	-58.9	739	0	2
Santa Maria Mater Domini	124.0	+ 3.1	-31.0	152	4	5

Rent in Dorsoduro [11]

Dorsoduro

Parishes of Dorsoduro

Parish	Median Rent	Widow Owners Rent Median Difference	Widow Tenants Rent Median Difference	Properties	Widow Owned Properties	Widow Rented Properties
San Raffael	74.4	-	-43.4	772	0	3
San Barnaba	124.0	+ 93.0	-	904	1	0
San Basegio	74.4	-37.2	-	359	2	0
San Gregorio	86.8	-	-24.8	487	0	1
San Pantalon	111.6	-	+ 446.4	639	0	1
Santa Margherita	99.2	+ 148.8	-	483	1	0

Rent in the Ghetto [12]

The Ghetto

Parishes of the Ghetto

Parish	Median Rent	Widow Owners Rent Median Difference	Widow Tenants Rent Median Difference	Properties	Widow Owned Properties	Widow Rented Properties
Ghetto Nuovissimo	148.8	-	+ 68.2	46	0	1
Ghetto Nuovo	124.0	+ 105.4	-55.8	207	5	2
Ghetto Vecchio	124.0	-6.2	-80.6	276	6	2

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.

Loredana Grimani Loredana Grimani is the widows holding the most properties in Venice in 1808. This exceptional case may indicate significant wealth, and further investigation into the Grimani-Morosini family could provide more context. From the presentation given on the different datasets <ref> (FDH2024-1-7-VeniceData), there is a graph from showing the distribution of family ownership - weighted by ownership portion. This graph is based on data from the Catastici, but it is clear by looking at it that both the Morosini family as well as the Grimani family hold a big portion of the properties in Venice during this time.

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

Properties anlysis comparison

When comparing the results of the different analysis of the Catastici and Sommarioni only the intersection of the columns from the two sets are possible to use. This is due to the datasets not containing entirely the same data. An example for something that falls outside this scope is aspect of the tenants, due to them not being mentioned in the Sommarioni.

aspects to compare:

• amount of widows found

• distribution of districts

• function of buildings

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.

Discussion, limitations and quality assessments

should we maybe summarize, since we probably have similar limitations?

Lets make a list first:

Limitations Methodological Limitations: Were there constraints in your methodology?

• Data: when looking at the heritage between the Catastici and the Sommarioni only 16 cases were identified using id_napo. This is too few to be able to say anything concrete about all widows.
• Not all columns have values, for instance for the heritage only 16 out of 61 entries had a id_napo.

• Tools: using difflib and searching for similarities in names

Data Limitations: Issues with data quality, availability, or representativeness.

• The standardised sections, like "owner_standardised" in the Sommarioni is not fully standardised. Still some different spellings of names or additional characters.

External Validity: Can your findings be generalized to other settings or populations?

• Sommarioni no tenants so cant really say anything about them. Also maybe the poorest widows not properly represented

Quality assessment

• Data Quality: Comment on the quality, completeness, and reliability of your data.: might be errors from person writign cadaster, but also through digitization, maybe the data also wrongly represents the reality of the situation for widows? But mention that it is mostly good

• Methodological Rigor: Methods might overlook some data, maybe too narrow

• Transparency and Reproducibility: Every analysis can be found in the GitHub

and also maybe all of this should be one coherent text and not discussion, limitations and qa each on its own

Conclusion and continuation

Conclusion

Continuation

This project has been an attempt to collect knowledge on the widows living in Venetian society between 1740 and 1808. There is still plenty to uncover about how life was for them and possible research areas are described followingly.

• look at the Tassini (explain what the Tassini is) and see if and how they mention widows

• try to do more on archetypes, maybe some qualitative analysis of familis

Deliverables

The main deliverables of the project is the results of the different analysis conducted during the span of the project and the tools used to extract the different data.

• should we go into more detail on what is to be found in the git?
• maybe mention which tools can be reused when conducting further analysis
• maybe talk again about resutls

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

References

• if for instance the figures of the Catastici is taken from the PP from the lecture https://www.dropbox.com/scl/fo/tu5waw0623hcp4537lx6u/AKx-eznaH6BRddo1goaF7OE?dl=0&e=1&preview=FDH2024-1-7-VeniceData.pdf&rlkey=jiewdfpk5ysyv92m1817sk5qc&st=01697apo, do we need to reference to it?

should include references to the historical sources used for the historical background

• how do we want to reference to slides from the lecture?

For our eyes only

TODO (guideline): -Project plan and milestones (5%) (>300 words) -Motivation and description of the deliverables (5%) (>300 words) - what did you do, Results of analysis, not only knowledge but oncly processes -Detailed description of the methods(5%) (>500 words) -Quality assessment and discussion of limitations (5%) (>300 words)

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

TODO later:

• methodology
• introduction + historical backround and motivation ( maybe Nathanael can talk briefly about the historic context?)
• conclusion and continuation
• deliverables

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 [13]

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 : [14]

this looks cool : [15]

Project Plan and Milestones

orginial goal for project: ( think we need to adjust it for the introduction) 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.

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?

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

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?