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Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data
Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress toward such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic indicators are necessary. When it comes to socioeconomic devel...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719970/ https://www.ncbi.nlm.nih.gov/pubmed/36479588 http://dx.doi.org/10.3389/fdata.2022.1006352 |
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author | Piaggesi, Simone Giurgola, Serena Karsai, Márton Mejova, Yelena Panisson, André Tizzoni, Michele |
author_facet | Piaggesi, Simone Giurgola, Serena Karsai, Márton Mejova, Yelena Panisson, André Tizzoni, Michele |
author_sort | Piaggesi, Simone |
collection | PubMed |
description | Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress toward such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic indicators are necessary. When it comes to socioeconomic development, novel digital traces can provide a complementary data source to overcome the limits of traditional data collection methods, which are often not regularly updated and lack adequate spatial resolution. In this study, we collect publicly available and anonymous advertising audience estimates from Facebook to predict socioeconomic conditions of urban residents, at a fine spatial granularity, in four large urban areas: Atlanta (USA), Bogotá (Colombia), Santiago (Chile), and Casablanca (Morocco). We find that behavioral attributes inferred from the Facebook marketing platform can accurately map the socioeconomic status of residential areas within cities, and that predictive performance is comparable in both high and low-resource settings. Our work provides additional evidence of the value of social advertising media data to measure human development and it also shows the limitations in generalizing the use of these data to make predictions across countries. |
format | Online Article Text |
id | pubmed-9719970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97199702022-12-06 Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data Piaggesi, Simone Giurgola, Serena Karsai, Márton Mejova, Yelena Panisson, André Tizzoni, Michele Front Big Data Big Data Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress toward such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic indicators are necessary. When it comes to socioeconomic development, novel digital traces can provide a complementary data source to overcome the limits of traditional data collection methods, which are often not regularly updated and lack adequate spatial resolution. In this study, we collect publicly available and anonymous advertising audience estimates from Facebook to predict socioeconomic conditions of urban residents, at a fine spatial granularity, in four large urban areas: Atlanta (USA), Bogotá (Colombia), Santiago (Chile), and Casablanca (Morocco). We find that behavioral attributes inferred from the Facebook marketing platform can accurately map the socioeconomic status of residential areas within cities, and that predictive performance is comparable in both high and low-resource settings. Our work provides additional evidence of the value of social advertising media data to measure human development and it also shows the limitations in generalizing the use of these data to make predictions across countries. Frontiers Media S.A. 2022-11-21 /pmc/articles/PMC9719970/ /pubmed/36479588 http://dx.doi.org/10.3389/fdata.2022.1006352 Text en Copyright © 2022 Piaggesi, Giurgola, Karsai, Mejova, Panisson and Tizzoni. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Piaggesi, Simone Giurgola, Serena Karsai, Márton Mejova, Yelena Panisson, André Tizzoni, Michele Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data |
title | Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data |
title_full | Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data |
title_fullStr | Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data |
title_full_unstemmed | Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data |
title_short | Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data |
title_sort | mapping urban socioeconomic inequalities in developing countries through facebook advertising data |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719970/ https://www.ncbi.nlm.nih.gov/pubmed/36479588 http://dx.doi.org/10.3389/fdata.2022.1006352 |
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