Publishing Linked Open Statistical Data

The ‘Geolocalized Facilities‘ pilot exemplifies the importance of technical and semantic interoperability in data-driven decision-making, showcasing INTERSTAT’s power to integrate data across borders and domains, linking and geolocalizing information from France and Italy.

What does the pilot offer?

The GF pilot allows users to geolocalize various facilities and events based on their positions and preferences. By specifying their location, including Country, NUTS3 Region, and Municipality, users can explore nearby cultural and educational facilities on a map. For the French territory, facilities are categorized and displayed as points of interest, enabling easy navigation. Meanwhile, in Italy, users not only view cultural facilities on the map but can also access information about upcoming events scheduled in their selected Municipality. Additionally, the pilot provides a table that presents the distribution of the resident population by age group and gender for both countries.

The Data Pipeline: From CSV to RDF

The success of the GF pilot is underpinned by a well-designed data pipeline based on the ETL (Extract, Transform, and Load) pattern. This pipeline leverages Python procedures to generate RDF (Resource Description Framework) triples from CSV datasets. The advantage of using RDF is that it facilitates data integration and linking with other datasets, contributing to the pilot’s technical interoperability.

Advantages of the Approach

The GF pilot embraces several key advantages:

1. Openness: The pilot’s code has been developed using open tools, and it is made available in the INTERSTAT GitHub repository. This commitment to openness fosters collaboration and encourages further development in the community.
   
2. Maximal Automation: By leveraging automation in the data pipeline, manual interventions are minimized, saving time and effort while also improving traceability. 
3. Reproducibility: The combination of automation and comprehensive code documentation ensures that the results obtained from the pilot are easily reproducible, bolstering the credibility and reliability of the findings. 
4. Efficiency: Execution of the data pipeline in a distributed environment enhances the efficiency of data processing and analysis, enabling timely and insightful outcomes. 

Conclusion

The ‘Geolocalized Facilities’ pilot exemplifies the power of the INTERSTAT framework, seamlessly integrating data across domains and countries. It provides valuable insights into nearby facilities, events, and population statistics, while emphasizing openness, automation, reproducibility, and efficiency. A strong precedent for future data-driven applications.

For those interested in exploring the GF pilot, you can navigate it HERE !