Exploring local well-being and vulnerability through OpenStreetMap: the case of Italy

Federico Ninivaggi and Eleonora Cutrini ()
Additional contact information
Federico Ninivaggi: University of Macerata
Eleonora Cutrini: University of Macerata

Quality & Quantity: International Journal of Methodology, 2024, vol. 58, issue 4, No 18, 3435-3473

Abstract: Abstract This article investigates the potential of OpenStreetMap (OSM) data in predicting local well-being and resilience in Italy. The linear Least Absolute Shrinkage and Selection Operator (LASSO) is used to handle multicollinearity problems and select the most influential OSM features. The data-driven approach provides evidence that OSM information is highly correlated with several socioeconomic metrics at a provincial scale (NUTS-3 level). Moreover, it claims that some specific points of interest—e.g., bookmakers—can be used for a rapid territorial appraisal of vulnerable territories, i.e., areas that are affected by economic backwardness, poor institutions, low human capital and that, for these adverse conditions, deserve special attention by policymakers concerned with a reduction of regional disparities. While OSM can become a powerful source for policy planning, monitoring and evaluation, future works in the field should explore the scalability of the approach, its use for forecasting purposes, and the adoption of various models and tools such as machine learning techniques to grasp even non-linear relationships between variables.

Keywords: OpenStreetMap; Local well-being; Territorial vulnerability; LASSO; Italy; R10; R11; R123 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11135-023-01805-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:58:y:2024:i:4:d:10.1007_s11135-023-01805-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-023-01805-6

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().