In this work, we propose a novel use for neural networks to build socioeconomic indicators, encoding a possible large information set, within single or multiple synthetic indexes, we call this proposal AutoSynth. In particular, we encode such information using an autoencoder, a neural network method to represent in a lower dimensionality space a matrix of features. We apply such a method to the evaluation of socio-conomic developments of suburban areas in Florence, and we test the performance of our model against some golden standard methods using a stress test.

AutoSynth Index: A Synthetic Indicator for Socio-Economic Development Based on Autoencoders / Giulio Grossi, Emilia Rocco. - ELETTRONICO. - (2023), pp. 507-510. (Intervento presentato al convegno Cladag 2023 tenutosi a Salerno nel September 11-13).

AutoSynth Index: A Synthetic Indicator for Socio-Economic Development Based on Autoencoders

Giulio Grossi
;
Emilia Rocco
2023

Abstract

In this work, we propose a novel use for neural networks to build socioeconomic indicators, encoding a possible large information set, within single or multiple synthetic indexes, we call this proposal AutoSynth. In particular, we encode such information using an autoencoder, a neural network method to represent in a lower dimensionality space a matrix of features. We apply such a method to the evaluation of socio-conomic developments of suburban areas in Florence, and we test the performance of our model against some golden standard methods using a stress test.
2023
BOOK OF ABSTRACTS AND SHORT PAPERS 14th Scienti c Meeting of the Classiffication and DataAnalysis Group
Cladag 2023
Salerno
September 11-13
Giulio Grossi, Emilia Rocco
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1327031
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