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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.