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arXiv · 2401.13370

New accessibility measures based on unconventional big data sources

G. Arbia·V. Nardelli·N. Salvini·I. Valentini
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Abstract

In health econometric studies we are often interested in quantifying aspects related to the accessibility to medical infrastructures. The increasing availability of data automatically collected through unconventional sources (such as webscraping, crowdsourcing or internet of things) recently opened previously unconceivable opportunities to researchers interested in measuring accessibility and to use it as a tool for real-time monitoring, surveillance and health policies definition. This paper contributes to this strand of literature proposing new accessibility measures that can be continuously feeded by automatic data collection. We present new measures of accessibility and we illustrate their use to study the territorial impact of supply-side shocks of health facilities. We also illustrate the potential of our proposal with a case study based on a huge set of data (related to the Emergency Departments in Milan, Italy) that have been webscraped for the purpose of this paper every 5 minutes since November 2021 to March 2022, amounting to approximately 5 million observations.

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G. Arbia, V. Nardelli, N. Salvini, I. Valentini. 2024-01-24. New accessibility measures based on unconventional big data sources. https://arxiv.org/abs/2401.13370

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