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

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 ap...

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Veröffentlicht in:	Quality & quantity 2024-08, Vol.58 (4), p.3435-3473
Hauptverfasser:	Ninivaggi, Federico, Cutrini, Eleonora
Format:	Artikel
Sprache:	eng
Schlagworte:	Big Data Economic activity Economic underdevelopment Emergency communications systems Forecasting GDP Gross Domestic Product Human capital Methodology of the Social Sciences Policy making Resilience Social Sciences Socioeconomic factors Vulnerability Well being
Online-Zugang:	Volltext
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