CH Impact on IT

Cultural heritage, tourism and local economies in Italy

This project explores how cultural heritage impacts tourism and economic performance across Italian regions by analyzing the relationship between cultural assets, tourism revenues, and employment indicators. Through interactive maps and data visualizations, the project provides insights to support more effective use of cultural heritage for sustainable economic growth and balanced regional development.

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Created by University of Bologna Students

Scenarios

Exploring the Economic Impact of Cultural Heritage Across Italian Regions

This project aligns data from various sources to analyze how cultural heritage, tourism, and economic development interact across Italy. By examining regional GDP, employment, tourism revenues, and household income, we aim to understand the contribution of cultural assets to local economies and reveal regional disparities.

Our investigation focuses on two key questions:

Key Research Areas

Correlation between cultural sites and tourism revenue
Impact of cultural heritage on employment and income

The results offer practical insights for policymakers, local authorities, tourism boards, and cultural institutions. The findings support strategic decisions to strengthen regional economies, enhance tourism potential, and advocate for increased investment in cultural heritage — promoting both cultural preservation and regional prosperity.

Our Goal

To provide data-driven insights that promote sustainable growth and balanced regional development through the better utilization of Italy's cultural heritage.

Datasets Overview

This project integrates original datasets and mashup data sources to analyze the interplay between tourism, cultural heritage, and economic development across Italian regions. Below is a summary of the key datasets utilized.

D1: Accommodation Infrastructure

Tourist establishments, bedrooms, and bed-places across NUTS 2 regions (Eurostat).

D2: Peer-to-Peer Stays

Guest nights via collaborative platforms (NUTS 3 regions, Eurostat Experimental Data).

D3: Monthly Occupancy Trends

Occupancy rates by type of tourist accommodation with guest origin breakdown (ISTAT).

D4 & D5: Arrivals and Overnight Stays

Regional arrivals and nights spent by tourists at accommodation establishments (Eurostat).

D6: Occupancy Metrics

Net occupancy rates of hotels and similar accommodations by NUTS 2 regions (Eurostat).

D7: Cultural Sites Inventory

Luoghi della cultura dataset from MiC catalog, detailing cultural heritage locations in Italy.

D8: GDP Breakdown

Italy's GDP composition by economic sector (agriculture, industry, services) from ISTAT.

D9: Employment by Sector

Regional employment distribution by economic sector based on 2010 classification (ISTAT).

D10: Household Income

Household net income by source, analyzing regional disparities and income structure (ISTAT).

M1: Mashup 1

Mashup dataset of all the tourism data, along with the cultural heritage institutions data.

M2: Mashup 2

Mashup dataset of all the economic data, along with the cultural heritage institutions data.

Quality Checkpoints

Detailed Dataset Evaluation

Overview of completeness, consistency, and reliability indicators per dataset.

Question (Criterion)	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Time-Series Completeness	100% (2019-2023)	100% (2019-2023)	100% (2019-2023)	100% (2019-2023)	100% (2019-2023)	100% (2019-2023)	0.04% missing	23.57% (2023)	23.26% (2023)	71% (2021/22)
Geographical Coverage	100% NUTS-2	100% NUTS-3	100% NUTS-2	100% NUTS-2	100% NUTS-2	100% NUTS-2	All regions present	All NUTS levels	All NUTS levels + NACE Rev.2	All NUTS levels
Completeness Eval	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Accuracy	✅ Syntactic	✅ Syntactic	✅ Syntactic	✅ Syntactic	✅ Syntactic	✅ Syntactic	✅ Syntactic	✅ Syntactic	✅ Syntactic	✅ Syntactic
Semantic Accuracy / Data Consistency	✅ Semantic	✅ Semantic	✅ Semantic	✅ Semantic	✅ Semantic	✅ Semantic	Province boundaries outdated	3 bil diff 2022/23	Cross-check Eurostat incomplete	4 thousand diff 2021/22
Coherence	✅ No contradictions	✅ No contradictions	12% violated rule	✅ No contradictions	✅ No contradictions	✅ No contradictions	✅ No contradictions	✅ No contradictions	✅ No contradictions	✅ No contradictions
Timeliness	✅	✅	✅	✅	✅	✅	Daily	Annual	Annual	Annual

Legal Checkpoints

Detailed Legal Compliance by Dataset

Assessment of privacy, intellectual property, licensing, and access limitations for each dataset.

Question (Criterion)	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Is the dataset free of any personal data as defined in the Regulation (EU) 2016/679?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Is the dataset free of any indirect personal data that could be used for identifying the natural person?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Is the dataset free of any particular personal data (art. 9 GDPR)?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Is the dataset free of any information that combined with common data available in the web, could identify the person?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Is the dataset free of any information related to human rights (e.g., refugees, witness protection, etc.)?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you use a tool for calculating the range of the risk of deanonymization?	❌ (NOT APPLICABLE)	❌	❌	❌	❌	❌	❌	❌	❌	❌
Are you using geolocalization capabilities?	✅	✅	✅	✅	✅	✅	❌	✅	✅	✅
Did you check that the open data platform respect all the privacy regulations (registration of the end-user, profiling, cookies, analytics, etc.)?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Do you know who, in your open data platform, is the Controller and Processor of the privacy data of the system?	✅ Eurostat	✅ Eurostat	✅ ISTAT	✅ Eurostat	✅ Eurostat	✅ Eurostat	✅ Eurostat	✅ ISTAT	✅ ISTAT	✅ ISTAT
Have you checked the privacy regulation of the country where the datasets are physically stored?	✅	✅	✅	✅	✅	✅	❌	✅	✅	✅
Do you have non-personal data?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅

Question (Criterion)	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Have you created and generated the dataset?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Are you the owner of the dataset?	❌ Eurostat	❌ Eurostat	❌ ISTAT	❌ Eurostat	❌ Eurostat	❌ Eurostat	❌ MiC	❌ ISTAT	❌ ISTAT	❌ ISTAT
Are you sure not to use third party data without the proper authorization and license?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Have you checked if there are any limitations in your national legal system for releasing some kind of datasets with open license?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅

Question (Criterion)	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Did you release the dataset with an open data license?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you include the clause: "In any case the dataset can't be used for re-identifying the person"?	❌	❌	❌	❌	❌	❌	✅	❌	❌	❌
Did you release the API (in case you have it) with an open source license?	✅	✅	✅	✅	✅	✅	❌	✅	✅	✅
Have you checked that the open data/API platform license regime is in compliance with your IPR policy?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅

Question (Criterion)	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Did you check that the dataset concerns your institutional competences, scope and finality?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you check the limitations for the publication stated by your national legislation or by the EU directives?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you check if there are some limitations connected to the international relations, public security or national defence?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you check if there are some limitations concerning the public interest?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you check the international law limitations?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you check the INSPIRE law limitations for the spatial data?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A

Question (Criterion)	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Did you check that the dataset could be released for free?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you check if there are some agreements with some other partners in order to release the dataset with a reasonable price?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Did you check if the open data platform terms of service include a clause of “non liability agreement” regarding the dataset and API provided?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
In case you decide to release the dataset to a reasonable price did you check if the limitation imposed by the new directive 2019/1024/EU are respected?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
In case you decide to release the dataset to a reasonable price did you check the e-Commerce directive and regulation?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A

Question (Criterion)	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Do you have a temporary policy for updating the dataset?	✅ Annual	✅ Annual	✅ Monthly + Annual	✅ Annual	✅ Annual	✅ Annual	✅ Annual	✅ Annual	✅ Annual	✅ Annual
Do you have some mechanism for informing the end-user that the dataset is updated at a given time to avoid mis-usage and so potential risk of damage?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Did you check if the dataset for some reason cannot be indexed by the research engines (e.g., Google, Yahoo, etc.)?	✅	✅	✅	✅	✅	✅	❌	✅	✅	✅
In case of personal data, do you have a reasonable technical mechanism for collecting request of deletion (e.g., right to be forgotten)?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A

Ethical Checkpoints

Ethical Analysis by Dataset

Evaluation of human-centric principles, data control, and user empowerment aspects per dataset.

Question	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Is your data processing based on the fact that you borrow data from the users (not owner of their data)?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Do you ensure that the user’s rights are prioritised, rather than commercial or institutional interests?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you ensure that primarily users benefit from their own data – not just the organisation?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you use privacy-by-design principles, and can you describe them clearly and transparently?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅

Question	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Do you ensure that users’ data – as far as possible – is processed directly on the users’ own device(s)?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
When the processing of data is necessary other than on the user’s own devices, such as your server or a cloud solution, is collected data not related to an identifiable person?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you use profiling? If so, do you allow the user to influence and determine the values, rules and input that underlie the profiling?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Do you use data to predict individual-level behaviour or only patterns?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌

Question	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
In which country is your data stored?	Luxemburg	Luxemburg	Italy	Luxemburg	Luxemburg	Luxemburg	Italy	Italy, under ISTAT infrastructure	Italy, under ISTAT infrastructure	Italy, under ISTAT infrastructure
Where is the storage solutions provider headquartered?	Brussels/Lux.	Brussels/Lux.	Rome	Brussels/Lux.	Brussels/Lux.	Brussels/Lux.	N/A	ISTAT	ISTAT	ISTAT
Does the transmission of data go through countries outside of the EU?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Do you use machine learning / artificial intelligence? If so, can you explain the algorithms – the criteria and parameters?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Do you use personal data to influence user behaviour?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Do you ensure that it is transparent when the use of personal data may influence a user’s behaviour?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Do you ensure that the design does not create addiction and thus influences the person’s self-determination and empowerment?	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Do you operate with open source software, so others can use it and possibly develop it further?	✅ Eurostat publishes API code snippets	✅ Eurostat publishes API code snippets	❌	✅ Eurostat publishes API code snippets	✅ Eurostat publishes API code snippets	✅ Eurostat publishes API code snippets	✅	✅ the project uses open source tools (e.g., Python, Jupyter) and publishes code openly	✅ the project uses open source tools (e.g., Python, Jupyter) and publishes code openly	✅ the project uses open source tools (e.g., Python, Jupyter) and publishes code openly

Question	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
When do you anonymise personal data?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Do you use end-to-end encryption of data?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you minimise the use of metadata and explain how it is done?	✅	✅	✅	✅	✅	✅	❌	✅	✅	✅
Do you use zero knowledge as a design principle?	N/A	N/A	N/A	N/A	N/A	N/A	❌	N/A	N/A	N/A
Do you sell data to third parties?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Do you sell data as personal identifiable data?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Do you sell data as patterns on an aggregated level?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
If you sell data, are you making sure that it is fully anonymised information only describing patterns, not individuals?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Do you use third-party cookies?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Does this include SoMe (social media) cookies and SoMe logins?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Do you use Google Analytics or similar tracking tools?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
If you use third-party cookies, are your users fully aware that your cookie use leads to sharing of data about your users with third parties and do they agree with it?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Do you enrich data with external data, such as social media data, bought data or web scraping?	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Does this enrichment occur in response to, or in cooperation with, your users?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Do you have an individual or a department responsible for the ethical managing of data?	✅	✅	✅	✅	✅	✅	❌	✅	✅	✅
How is the work with data ethics embedded in the organisation?	Managed by Eurostat within the European Statistical System (ESS)	Identical framework to D1	Legislative Decree 322/1989 (Italian statistical law) and ISTAT’s Code of Conduct.	Identical framework to D1	Identical framework to D1	Identical framework to D1	N/A	ISTAT integrates ethics through institutional data policies, transparency rules and compliance checks	ISTAT integrates ethics through institutional data policies, transparency rules and compliance checks	ISTAT integrates ethics through institutional data policies, transparency rules and compliance checks
How do you ensure that your data ethics guidelines are respected?	automated disclosure-control software, dual sign-off workflows, audit trails, external peer reviews, and supervisory bodies	automated disclosure-control software, dual sign-off workflows, audit trails, external peer reviews, and supervisory bodies	version control & audit log	automated disclosure-control software, dual sign-off workflows, audit trails, external peer reviews, and supervisory bodies	automated disclosure-control software, dual sign-off workflows, audit trails, external peer reviews, and supervisory bodies	automated disclosure-control software, dual sign-off workflows, audit trails, external peer reviews, and supervisory bodies	N/A	Internal audits, legal compliance teams, and mandatory adherence to GDPR and open data principles	Internal audits, legal compliance teams, and mandatory adherence to GDPR and open data principles	Internal audits, legal compliance teams, and mandatory adherence to GDPR and open data principles
Can the processing of data be audited by an independent third party?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you require and control the data ethics of your subcontractors and partners?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅

Question	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Do you engage in dialogue with your users on a public platform?	✅	✅	✅	✅	✅	✅	❌	✅	✅	✅
Do you have guidelines for using the platform?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you moderate the platform in order to remove sensitive personal data?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	✅	✅	✅
If your services are offered to children, do you ensure parental consent?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Is data used to develop or train an algorithm?	❌	❌	❌	❌	❌	❌	N/A	❌	❌	❌
Do you ensure that the use of data does not lead to discrimination?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you ensure that the use of data does not expose the vulnerabilities of individuals?	✅	✅	✅	✅	✅	✅	N/A	✅	✅	✅
Do you ensure that the use of artificial intelligence / machine learning is to the benefit of the individual and does not cause physical, psychological, social or financial harm to the individual?	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A

Technical Checkpoints

Detailed Technical Analysis

Overview of formats, metadata, provenance, and accessibility details per dataset.

Dataset	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Format	XLSX, TSV, CSV, XML, JSON	XLSX, TSV, CSV, XML, JSON	XLS, CSV, JSON	XLSX, TSV, CSV, XML, JSON	XLSX, TSV, CSV, XML, JSON	XLSX, TSV, CSV, XML, JSON	JSON-LD, RDF/TTL, RDF/XML	CSV	CSV	CSV

Dataset	D1	D2	D3	D4	D5	D6	D7	D8	D9	D10
Metadata	Covers: scope, purpose, variables, definitions, coverage, legal basis, statistical units, data sources	Same as for D1	SIQual, quality notes, scope, concepts, classifications	Same as for D1	Same as for D1	Same as for D1	Cultural site categorization, province/region breakdown, MiC updates	Sectoral & regional breakdown, national accounts alignment	Employment by sector, territorial detail, NACE/ATECO classification	Household income breakdown, source categories, regional aggregation

D1: Eurostat
D2: Eurostat
D3: ISTAT
D4: Eurostat
D5: Eurostat
D6: Eurostat
D7: MiC (Ministry of Culture)
D8: ISTAT
D9: ISTAT
D10: ISTAT

D1: Eurostat
D2: Eurostat
D3: ISTAT
D4: Eurostat
D5: Eurostat
D6: Eurostat
D7: MiC (Ministry of Culture)
D8: ISTAT
D9: ISTAT
D10: ISTAT

Serialization

RDF Serialization of Metadata

All datasets and the project catalogue have been described with metadata, following the DCAT Application profile for data portals in Europe (DCAT-AP).

Title	CH Impact on IT - Datasets Catalogue
Identifier	CHImpactonITCatalogue
Description	This catalog contains datasets about cultural heritage institutions, tourism activities and economic data in Italy, based on regions.
Publisher	Ezgi Çolakoğlu, Anna Pak, Sergei Slinkin
Issued	15/06/2025
Modified	06/07/2025
Datasets	M1, M2
Homepage	CH Impact on IT
Language	English
License	CC BY 4.0
RDF assertion of metadata	RDF Serialization

Visualizations

Visualizations to better explain the datasets and how they relate to each other in a visually engaging way.

Considerations	Mashup Used
What is the relationship between available tourist accomodations and occupancy rates? What can this imply about the presence of cultural heritage institutions?	Mashup 1
What is the relationship between tourist arrival numbers and the number of cultural heritage institutions present?	Mashup 1
What is the relationship between the number of nights spent per region? Is there any correlation between this and the number of cultural heritage institutions?	Mashup 1
What is the trend of total accomodation establishments versus cultural heritage institutions between 2021 and 2023?	Mashup 1
What is the overall correlation between all touristic data and the number of cultural heritage institutions?	Mashup 1
What is the overall average trend of the economic data between 2021 and 2023?	Mashup 2
What is the relationship between cultural heritage institutions and the economic information?	Mashup 2
What are the regional averages of economic data and are there any visible correlations with the number of cultural heritage institutions?	Mashup 2
What is the overall correlation between all economic data and the number of cultural heritage institutions?	Mashup 2

Discussion

Discussion of Findings

For the first resarch topic, as we intended to investigate the relationship between the presence of cultural heritage institutions and tourism activity, we looked at the number of available accomodation and their occupancy rates per region. From this comparison, it could be said that the regions with higher numbers of available accomodations do not necessarily have higher rates of occupancy (i.e. Veneto and Toscana regions). This could mean that the occupancy rate is not necessarily related to the total number of available places, which is supported by the low rate of correlation (r = 0.35).

In addition to the occupancy rate, we investigated the number of tourist arrivals against the number of cultural heritage institutions per region. There seems to be a reasonable relationship between these two parameters, as the higher number of arrivals are accompanied with the higher number of institutions, and the lower number of arrivals occur in the regions with lower number of institutions. Even though there are exceptions to this trend, such as Veneto and Trentino-Alto Adige regions, we believe it contributes to the initial hypothesis.

While investigating the nights spent based on the aggregated numbers from collaborative platforms and accomodation establishements per region, we found a trend similar to our second point, such that there seems to be a relationship between the higher number of cultural institutions and the higher number of nightly stays. The seperate correlation coefficients of r = 0.61 for the collaborative platforms data and r = 0.53 for the accomodation establishments seem to support the existence of a relationship.

In order to have an overall look, we used the biggest data we have for the tourism activity and we plotted the number of cultural institutions against it. As the graph suggests, there seems to be some correlation between the two data, although there is a bit more discrepancy in this point compared to other points. The correlation coefficient is r = 0.5, which seems to suggest the existince of a relationship, although it is not a strong one.

For our second research topic, we investigated if there is a relationship between the existence of cultural heritage institutions and economic data. For that, we looked at the trends between only the economic data before plotting it against the institution data. Overall, GDP and employment data seem to be highly related to each other, however as the household net income data has a large amount of missing data, based on the data we have, it seems to not have a visible relationship with the other categories of data.

According to our correlation matrix, the number of cultural heritage institutions seems to have a reasonable relation with GDP (r = 0.59) and a higher relation with the number of employment (r = 0.72).

Overall, there seems to be some overlap between tourism activity and the number of cultural heritage institutions, as well as the presence of cultural heritage institutions and their impact on the local economies. However, the data we currently have is not enough to make a solid conclusion, more data and more investigation of data is required based on the findings of our project.

Sustainability Checkpoints

Dataset Update Sustainability

Ensuring long-term relevance and reliability of datasets through institutional sources and transparent update cycles.

The sustainability of updating the datasets over time is ensured through the use of official, institutional data sources with established update cycles.

For datasets D1 to D6, provided by Eurostat and ISTAT, updates follow a predictable schedule, typically annual or quarterly, depending on the indicator. These updates are transparently published on official portals, accompanied by metadata that informs users about version changes and data currency.

For D7, maintained by the Italian Ministry of Culture (MiC), the dataset is part of the national DBUnico system, which is continuously managed and updated as new cultural sites are added or existing records change.

For economic datasets D8 to D10, ISTAT guarantees regular revisions aligned with national accounting practices, labour statistics releases, and household income surveys, ensuring data remains relevant for long-term research and policy analysis.

The reliance on standardized data sources, open data platforms, and clear versioning policies contributes to the long-term sustainability and reliability of these datasets for research, policy-making, and open-access projects.

Title	Establishments, bedrooms, and bed-places in tourist accommodation (NUTS 2 region)
Identifier	tour_cap_nuts2
Description	This dataset provides information on the number of tourist accommodation establishments, bedrooms, and bed places across different NUTS 2 regions in Italy.
Rights holder	Eurostat
Creator	Eurostat
Publisher	Eurostat
Issued	N/A
Modified	27/06/2025
Distribution	XLSX, TSV, CSV, XML, JSON
Language	English
License	CC BY 4.0
RDF assertion of metadata	RDF Serialization

Title	Guest nights spent at short-stay accommodations via collaborative economy platforms (NUTS 3 region)
Identifier	tour_ce_oan3
Description	This dataset presents the number of guest nights booked through short-term rental platforms (e.g., Airbnb, Booking.com).
Rights holder	Eurostat
Creator	Eurostat
Publisher	Eurostat
Issued	N/A
Modified	02/07/2025
Distribution	XLSX, TSV, CSV, XML, JSON
Language	English
License	CC BY 4.0
RDF assertion of metadata	RDF Serialization

Title	Occupancy in collective tourist accomodation by type of accomodation
Identifier	IT1,122_54_DF_DCSC_TUR_3,1.0
Description	This dataset provides detailed monthly occupancy statistics for different types of tourist accommodations across Italy, categorized by NACE rev. 2 industry codes.
Rights holder	ISTAT
Creator	ISTAT
Publisher	ISTAT
Issued	N/A
Modified	N/A
Distribution	XLS, CSV, JSON
Language	English
License	ISTAT Open Data Policy
RDF assertion of metadata	RDF Serialization

Title	Arrivals at tourist accommodation establishments by NUTS 2 region
Identifier	tour_occ_arn2
Description	This dataset tracks the number of tourist arrivals at accommodation establishments across NUTS 2 regions in Italy.
Rights holder	Eurostat
Creator	Eurostat
Publisher	Eurostat
Issued	N/A
Modified	27/06/2025
Distribution	XLSX, TSV, CSV, XML, JSON
Language	English
License	CC BY 4.0
RDF assertion of metadata	RDF Serialization

Title	Nights spent at tourist accommodation establishments by NUTS 2 region
Identifier	tour_occ_nin2
Description	This dataset reports the total number of nights spent by tourists in accommodations across NUTS 2 regions.
Rights holder	Eurostat
Creator	Eurostat
Publisher	Eurostat
Issued	N/A
Modified	27/06/2025
Distribution	XLSX, TSV, CSV, XML, JSON
Language	English
License	CC BY 4.0
RDF assertion of metadata	RDF Serialization

Title	Occupancy in tourist accommodation, by NUTS 2 region
Identifier	tour_occ_anor2
Description	This dataset provides net occupancy rates of bed places and bedrooms in hotels and similar accommodations (NACE Rev. 2 activity I55.1) at the NUTS 2 regional level.
Rights holder	Eurostat
Creator	Eurostat
Publisher	Eurostat
Issued	N/A
Modified	27/06/2025
Distribution	XLSX, TSV, CSV, XML, JSON
Language	English
License	CC BY 4.0
RDF assertion of metadata	RDF Serialization

Title	Luoghi della cultura
Identifier	dg-org_luoghi
Description	Data from DBUnico 2.0 describing state and non-state cultural sites, managed and updated by the General Directorate for Organization.
Rights holder	MiC – Direzione generale Organizzazione
Creator	MiC – Direzione generale Organizzazione
Publisher	MiC – Direzione generale Organizzazione
Issued	07/04/2016
Modified	27/06/2025
Distribution	JSON-LD, RDF/TTL, RDF/XML
Language	Italian
License	CC BY 4.0
RDF assertion of metadata	RDF Serialization

Title	Gross domestic product supply side
Identifier	IT1,93_498_DF_DCCN_PILT_1,1.0
Description	This dataset provides a breakdown of Italy's GDP by analyzing the value added by different economic sectors, such as agriculture, industry, and services.
Rights holder	ISTAT
Creator	ISTAT
Publisher	ISTAT
Issued	N/A
Modified	N/A
Distribution	XLSX, CSV, SDMX, JSON
Language	English
License	ISTAT Open Data Policy
RDF assertion of metadata	RDF Serialization

Title	Employment by Economic Sector (2010 Classification)
Identifier	IT1,93_379_DF_DCCN_OCCTSEC2010_2,1.0
Description	This dataset provides employment data categorized by economic sector based on the 2010 classification.
Rights holder	ISTAT
Creator	ISTAT
Publisher	ISTAT
Issued	N/A
Modified	N/A
Distribution	XLSX, CSV, SDMX, JSON
Language	English
License	ISTAT Open Data Policy
RDF assertion of metadata	RDF Serialization

Title	Household Net Income by Source of Income
Identifier	IT1,32_292_DF_DCCV_REDNETFAMFONTERED_9,1.0
Description	This dataset provides information on household net income, broken down by different sources of income (e.g., wages, pensions, business income) at various territorial levels.
Rights holder	ISTAT
Creator	ISTAT
Publisher	ISTAT
Issued	N/A
Modified	N/A
Distribution	XLSX, CSV, SDMX, JSON
Language	English
License	ISTAT Open Data Policy
RDF assertion of metadata	RDF Serialization

Cultural heritage, tourism and local economies in Italy

Exploring the Economic Impact of Cultural Heritage Across Italian Regions

Key Research Areas

Our Goal

Datasets Overview

D1: Accommodation Infrastructure

D2: Peer-to-Peer Stays

D3: Monthly Occupancy Trends

D4 & D5: Arrivals and Overnight Stays

D6: Occupancy Metrics

D7: Cultural Sites Inventory

D8: GDP Breakdown

D9: Employment by Sector

D10: Household Income

M1: Mashup 1

M2: Mashup 2

Detailed Dataset Evaluation

📂 Dataset Quality – Breakdown

Detailed Legal Compliance by Dataset

Privacy Issues

Intellectual Property Rights

Licences

Limitations on Public Access

Economical Conditions

Temporal Aspects

Ethical Analysis by Dataset

The human being at the centre

Individual Data Control

Transparency

Accountability

Equality

Detailed Technical Analysis

Format

Metadata

URI

Provenance

RDF Serialization of Metadata

Visualizations

Discussion of Findings

Dataset Update Sustainability