Cash & Culture: Who Gets Funded, Who Gets Visited

We invite you to explore the fascinating dynamic where public resources meet regional cultural appeal.This analysis maps the intricate relationship between state investment and cultural success across Italy.

Digital Map of Italy

About the Project

The project starts with the core question of whether regions are funded in areas with the highest visitor demand. We use three interactive lenses to evaluate the effectiveness of these policies from 2014 to 2024:

  • The Allocation Gap: Mapping the balance between public investment and private income.
  • The Self-Sufficiency Test: Tracking whether regions can turn government funds into financial independence.
  • The Bottleneck Indicator: Identifying culturally rich regions where poor transportation infrastructure might be the primary limit on tourism growth.

Research Questions

How have public investments in cultural heritage been distributed across Italian regions over the past decade, and what relationship can be observed between funding allocation and public participation in cultural heritage activities?

Research Question 1

How evenly have public funds been distributed across Italian regions, and does this distribution match visitor participation?

Research Question 2

Do current funding policies result in financial self-sufficiency across these regions?

Research Question 3

Does infrastructure accessibility act as a bottleneck for regions with high cultural appeal?

Highlights of Last Decade

0
Cultural Heritage Projects
0
% Developing Potential
0
Euros In Funding
0
Cultural Heritage Visitors

Workflow

Preparation

Identified the data sources, evaluated each for its relevance and feasibility, and selected the raw data for extraction.

Extraction

Ingested raw data, cleaned the rows of duplicate or empty values, normalized the variable names, and saved clean CSV files for visualization.

Visualization

Created different types of graphs and maps to visualize the data in accordance with the research questions.

Interpretation

Observed the graphs and interesting trends. Extracted useful information and summarize a takehome message.

Data Sources

Data Preparation

OpenCoesione is Italy’s open-government initiative on cohesion policies. The portal provides navigable data on planned resources and expenditures, locations, thematic areas, implementing bodies, timelines, progress, and payments for individual projects. The dataset “Projects on Culture and Tourism” includes all projects financed through cohesion policies in the areas of culture and tourism. It covers infrastructural interventions for the protection and conservation of cultural heritage, the improvement of tourism services, and the promotion and enhancement of natural resources. We downloaded the datasets corresponding to the 2014–2020 and 2021–2027 programming periods.

Data Extraction

Based on the metadata, we extracted the columns relating to project timelines like forecasted start date, actual start date, and actual end date (DATA_INIZIO_PREV_STUDIO_FATT, OC_DATA_INIZIO_PROGETTO, OC_DATA_FINE_PROGETTO_EFFETTIVA), location information (region and province), the amount of funding received (mainly public), and an internal identifier (CUP) to avoid duplicates. To select the records relevant to our project, we filtered the data using the column OC_DATA_INIZIO_PROGETTO, keeping only projects with an actual start date between 2014 and 2024, assuming that funding allocation becomes effective once the project begins. The filtered dataset is available on our GitHub under the name open_coesione.csv .

Data Preparation

The Statistics Office (Ufficio Statistica) of the Ministero della Cultura – as part of the National Statistical System (Sistan) – aims to provide accurate, timely, and freely accessible information on the Ministry’s institutions and activities through its statistical analyses and surveys. In the dataset “Visitatori e introiti di musei, monumenti e aree archeologiche statali”, visitor data for museums with an admission fee are recorded through ticket issuance. For free museums, data are estimated or collected through attendance registers or people-counting devices. Revenue data, derived from ticket sales, is provided both gross and net of the amounts due to ticketing-service concessionaires. The data are stored in separate yearly Excel files with consistent labelling. We selected Table 5, where each file contains two sheets – one for visitors and one for revenues – organized by region and by month.
Information for Sicilia, Valle d’Aosta, and Trentino-Alto Adige is missing.
Revenue values do not include additional services such as bookshops, merchandise sales, cafés and restaurants, guided tours, audio guides, or ticket-reservation services.

Data Extraction

The tables were split by topic and merged into two separate CSV files, with regions as columns and monthly chronological data as rows: mic_visitors.csv and mic_income.csv. For further analysis, we compared income data with funding allocation. We aggregated yearly funding from open_coesione.csv and merged it with the yearly totals of museum income, producing the file income&funding.csv, organized by region.

Data Preparation

IstatData is the new dissemination platform of the Istituto Nazionale di Statistica (Istat), offering aggregated statistical data enriched with charts, maps, and thematic summaries. In the dataset “Qualità dei servizi”, the section “Posti-km offerti dal Tpl” provides the product of the total kilometres travelled annually by public transport vehicles, weighted by their average capacity and related to the resident population (seat-km per inhabitant). The indicator refers to provincial capitals and includes buses, trams, trolleybuses, metro lines, funiculars or cable cars (including automated people-mover systems), and waterborne transport. This serves as an indicator of public transport efficiency and helps assess the accessibility of cultural institutions.
In the dataset “Paesaggio e Patrimonio Culturale”, the section “Densità e rilevanza del patrimonio museale” reports the number of permanent exhibition facilities per 100 km² (museums, archaeological areas, and monuments open to the public), weighted by visitor numbers. This provides a ranking of the cultural prominence of each region.

Data Extraction

Both datasets are provided with a multi-index based on geographical areas. We extracted the data at the regional level, cleaned the indices, and added one column per year starting from 2015 for the cultural index and from 2014 for the TPL indicator. The processed data were saved as tpl_efficiency.csv and index_cult.csv.

Visualization

Research Question 1

How were funds and visitors distributed among Italian regions?


To answer the first research question, the OpenCoesione and MiC datasets were utilized. For each region of Italy, the funding amount was extracted and displayed on the left Choropleth Map showing which regions received the higher funding through a darker color for the years 2014-2024.


The same was also done for the number of visitors for each region on the right map. This visualization allowed for a side-by-side comparison of regions. It provides a slider to select the year to be displayed on both maps.
It must be taken into account that for the funding map the actual start year of each project was selected (OC_DATA_INIZIO_PROGETTO).


Visualization 1: Regional Funding and Visitors

The side-by-side comparison of the funding map and the visitors map reveals a clear mismatch between financial investment and annual visitor numbers. Regions receiving high levels of project funding do not necessarily attract the most visitors.


The maps show that the largest share of funding was allocated between 2015 and 2019. Although the total number of visitors increased over the ten-year period, the relative proportions among regions remained stable. Lazio consistently recorded the highest number of visitors despite not receiving the largest amount of funding. Conversely, Campania obtained the highest funding but did not reach comparable visitor levels. Toscana experienced a similar rise in visitors as Campania, yet the funding it received was significantly lower.


Puglia and Sicilia received notable - though less consistent - amounts of funding in some years, but this investment did not translate into a proportional share of visitors.


It is important to note that, since our analysis considers only the actual starting year of each project, the expected outcomes of the investments - such as increased visitor numbers - may require additional time to appear in the data.

Research Question 2

Do current funding policies result in financial self-sufficiency across these regions?


To do a preliminary analysis on the income received through tickets a linegraph was plotted to establish the baseline revenue trend and visitor participation performance for each region. It is possible to filter the regions' visualizations from the legend of the graph, allowing for a complete comparison or a focused analysis of specific regions.

The heatmap visualizes the net financial return (Introiti - Fondi) for each Italian region over time, instantly revealing periods of self-sufficiency (blue) or dependency (red). The map is interactive, allowing users to hover the cells to see the exact values. A dropdown menu enables to switch from a yearly view to the average of the return value for each region over the ten years considered.



Visualization 2: Financial Performance

The total income of the regions Lazio, Campania, Toscana, Piemonte, and Lombardia are the highest with a consistent trend. But it cannot be interpreted as self-sufficiency. Financial self-sufficiency is defined as the relationship between income and cost. By only showing the income, the line graph provides half the financial story, preventing any measure of true success or dependency.
The Heatmap was necessary because it calculates and displays the core KPI required for RQ2: the Net Return (Introiti - Fondi).


By transforming the data into a single, color-coded matrix, the analysis instantly moves from observing simple revenue trends to providing a sophisticated evaluation of net financial success. The Red-Blue Colormap, anchored at zero, bypasses the confusion of comparing two lines and directly shows, for every region and every year, whether public funds successfully fostered financial autonomy (blue) or created financial dependency (red).


The yellow cells are indicators that the info either on funds or income was missing.


Research Question 3

Does infrastructure accessibility act as a bottleneck for regions with high cultural appeal?


The ranked horizontal barchart displays the TPL Efficiency Index for all Italian regions, ordered from highest to lowest. The data of the different years is accessible through a dropdown menu.
To cross-reference the TPL Index with the Cultural Index, we created a scatterplot using the average values of both indicators over the years they share, removing the temporal dimension. In this plot, the y-axis represents the Cultural Index - that is, the weighted number of visitors relative to the number of museum areas in each region - providing a measure of public interest in cultural heritage institutions. The exact values are accessible by hovering the points on the graph, labeled with the region names.


Regional Classification of TPL Efficiency per Year

Visualization 3: Accessibility of Museal Areas

By tracking the regional TPL index over the decade, the chart reveals the structural stability of transport efficiency. High TPL scores (indicating better public transport accessibility) are often concentrated in structurally strong Northern regions, which tend to maintain their high rankings consistently. Highly visited regions that frequently rank mid-low (such as Toscana or Campania) highlight a potential infrastructure disparity. This lagging performance in TPL for regions with historically high cultural appeal suggests that inadequate transport infrastructure may indeed be acting as a bottleneck, hindering their ability to efficiently handle tourist flow and fully capitalize on their cultural assets.


In the scatterplot regions are categorized into quadrants that reveal their strategic challenges or strengths: those falling into the Top-Left quadrant (High Cultural Index / Low TPL Index) are immediately identified as areas where infrastructure acts as a critical bottleneck. This quadrant is the area of potential development; assuming these regions could improve their TPL efficiency we can expect them to witness a growth also on the Cultural Index, moving them to the Top-Right quadrant, like Lazio, where a well connected local transportation is facilitating high affluences of tourism, confirming our hypothesis.

Interpretation

The analysis performed across financial inputs, cultural engagement, and infrastructure factors reveals a lack of synchronization among regional policy outcomes, requiring a shift toward strategic planning rooted in structural reality.


  • Disparity in Funding Allocation and Visitor Engagement

    The side-by-side comparison of project funding and visitor numbers reveals a definitive mismatch in allocation. Funding distribution does not simply mirror visitor demand: High Visitation without High Funding: Lazio consistently records the highest visitor numbers but does not receive the largest share of project funding. High Funding without High Visitation: Conversely, Campania receives the highest total project funding but does not reach comparable visitor levels.

    This disparity indicates that allocation is often driven by factors other than immediate visitor appeal, such as structural development needs, legacy policy or delays in funding allocation and utilization.


  • Contextualizing Financial Performance: Beyond Pure Profitability

    Despite the investment of public funds, the analysis confirms that financial sustainability remains elusive for most regions. The Net Return Heatmap shows that the majority operate under a state of financial dependency, consistently generating less income (Introiti) than the project funding (Fondi) received. However, this lack of total autonomy must be interpreted through the lens of cultural heritage management, where the primary objective is public value - preservation and access - rather than commercial gain. Consequently, negative returns are often a structural necessity for maintaining non-profit assets in less central areas, rather than a sign of failure.

    Furthermore, the calculated "return" is almost certainly an underestimation of actual financial performance. The dataset used strictly tracks ticket sales, omitting critical revenue streams standard in modern museum management, such as guided tours, merchandising, and venue rentals. This suggests that the actual degree of self-sufficiency is higher than reported. As shown in the breakdown below, while outliers like Lazio and Toscana demonstrate that commercial success is possible, the data also highlights significant reporting gaps, particularly in autonomous regions where "zero income" suggests missing data rather than a complete lack of revenue. The percentage is calculated on the regional average of return and funding.


Self-Suffiency Ratio Leader Value (Approx.) Interpretation
High Lazio 343% Lazio generates over three times the revenue it receives in public funding, demonstrating exceptional financial self-sufficiency.
Breakeven Toscana 100.4% Toscana is the only other large region to successfully break even, proving its investments are self-sustaining.
Low Molise, Basilicata 2% Among the majority of regions with high financial dependency, these regions show extreme results.

Sicilia, Trentino-Alto Adige, Valle d'Aosta: These regions show no recorded income against multi-million euro funding totals, highlighting either severe dependency or significant data gaps that must be investigated.


  • The Constraint of Infrastructure Accessibility
  • The structural reality of transport efficiency presents a constraint that influences both visitor potential and financial outcomes. The scatterplot identifies some regional clusters, including historically significant tourist destinations like Campania and Toscana, that fall into the Bottleneck Quadrant (Top-Left). The mid-low performance in transport efficiency in these high-appeal areas suggests a structural constraint that may inhibit the flow of visitors and limit the economic scaling of cultural assets. This structural reality makes it more challenging for projects in these regions to achieve the financial autonomy necessary for self-sufficiency.


    In the Synergy Quadrant (Top-Right) of this analysis we find Lazio, that is the "Star Performer": a region that has successfully balanced cultural value with modern infrastructure. This positioning indicates a strong, sustainable synergy where excellent accessibility supports and amplifies high public interest. This feature represents the benchmark for successful policy integration and should act as a model for the other regions' policies.


    At the Bottom-Right Lombardia falls into the Potential Quadrant. This region is highly efficient in its logistics and infrastructure, but its cultural offering or the public interest in it are relatively low. It possesses high growth potential, meaning the infrastructure is already in place. Policy efforts here should utilize this infrastructure to boost cultural heritage tourism, shifting the region toward the Synergy Quadrant.


    The same type of analysis can be applied on a smaller scale to most regions. Out of the 20 total, 16 fall into the Lagging quadrant (Bottom-Left), where improvements in the TPL Index tend to coincide with increases in the Cultural Index. Within this group, clear sub-clusters emerge: Friuli-Venezia Giulia, Veneto, Trentino-Alto Adige, Liguria, and Piemonte combine well-developed TPL with a Cultural Index between 1 and 2, showing how good transport supports steady visitor flows. Valle d’Aosta, Sicilia, Umbria, Marche, and Emilia-Romagna maintain similar cultural levels despite weaker TPL performance, suggesting latent potential that improved mobility could unlock. At the other end, Molise, Basilicata, Abruzzo, Puglia, Calabria, and Sardegna score low on both indicators and thus face the greatest structural limitations.
    Taken together, these patterns suggest a partial causal role for transport: while cultural appeal creates the demand, transport efficiency determines how much of that demand converts into actual visitation - regions with comparable cultural value achieve markedly different outcomes depending on their TPL performance.


This analysis reveals that cultural funding and visitor engagement rarely align perfectly. Funding distribution follows non-commercial policies, leading to a landscape where financial dependency is the norm. However, even with substantial project funding, accessibility remains a critical variable in visiting count. The data shows that regions with high cultural appeal but low efficiency on public transportation struggle to convert potential interest into actual visits. This suggests that bridging the gap between getting funded and getting visited requires more than just heritage investment; it demands a holistic approach that integrates cultural assets with the transport networks needed to reach them.

Team

Mohamed Iheb Ouerghi

Data Visualization
Web Communication

Anna Nicoletti

Data Analysis
Web Communication

Nazanin Fakharian

Data Visualization
Web Communication