This article provides a panel dataset on four capital dimensions (economic, human, social and physical) to study and promote the attractiveness and resilience of Italian territories. The dataset is articulated at the provincial and municipal level for the period 2010–2022. Data have been sourced from different open data repositories or collected through scraping downloads and have been elaborated in order to generate novel territorial indicators. While traditional datasets are commonly available at the regional and provincial levels, territorial analyses necessitate more granular data. Hence, this dataset allows researchers to study territorial characteristics of Italy at the NUTS3 and municipal levels, granting different degrees of spatial granularity and potentially supporting policymakers in evaluating the effectiveness of territorial policies implemented over the years.

A set of multidimensional indicators to assess the resilience and attractiveness of Italian provinces and municipalities (2010–2022 panel data)

Amaddeo E.;Bergantino A. S.;Buongiorno A.;Clemente A.;Intini M.;
2024-01-01

Abstract

This article provides a panel dataset on four capital dimensions (economic, human, social and physical) to study and promote the attractiveness and resilience of Italian territories. The dataset is articulated at the provincial and municipal level for the period 2010–2022. Data have been sourced from different open data repositories or collected through scraping downloads and have been elaborated in order to generate novel territorial indicators. While traditional datasets are commonly available at the regional and provincial levels, territorial analyses necessitate more granular data. Hence, this dataset allows researchers to study territorial characteristics of Italy at the NUTS3 and municipal levels, granting different degrees of spatial granularity and potentially supporting policymakers in evaluating the effectiveness of territorial policies implemented over the years.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/521620
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