Developing a Synthetic Socio-Economic Index through Autoencoders

Evidence from Florence’s Suburban Areas

Datos Bibliográficos

ID21510285
AutoresGiulio Grossi (0000-0003-1747-0570), Grossi G (University of Florence, autor de correspondencia), Emilia Rocco (0000-0003-4267-0734), Rocco E (University of Florence)
Año2026
Volumen183
Número3
Fecha de publicación2026-07-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSocial Indicators Research (JOURNAL)
Identificadores de la revistaISSN: 0303-8300 • E-ISSN: 1573-0921
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11205-026-03850-8
OpenAlexW7165046535
IdiomaEN
Referencias citadas50

The interest in summarizing complex and multidimensional phenomena often related to one or more specific sectors (social, economic, environmental, political, etc.) to make them easily understandable even to non-experts is far from waning. A widely adopted approach for this purpose is the use of composite indices, statistical measures that aggregate multiple indicators into a single comprehensive measure. In this paper, we present a novel methodology called AutoSynth, designed to condense potentially extensive datasets into a single synthetic index or a hierarchy of such indices. AutoSynth leverages an Autoencoder, a neural network technique, to represent a matrix of features in a lower-dimensional space. Although this approach is not limited to the creation of a particular composite index and can be applied broadly across various sectors, the motivation behind this work arises from a real-world need. Specifically, we aim to assess the vulnerability of the Italian city of Florence at the suburban level across three dimensions: economic, demographic, and social. To demonstrate the methodology’s effectiveness, it is also applied to estimate a vulnerability index using a rich, publicly available dataset on U.S. counties and validated through a simulation study

Artificial neural network · Composite index · Hierarchy · Vulnerability assessment · Regional Economics and Spatial Analysis · Regional resilience and development · Spatial and Panel Data Analysis