TY - JOUR
T1 - Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data
AU - Piaggesi, Simone
AU - Giurgola, Serena
AU - Karsai, Márton
AU - Mejova, Yelena
AU - Panisson, André
AU - Tizzoni, Michele
N1 - Publisher Copyright:
Copyright © 2022 Piaggesi, Giurgola, Karsai, Mejova, Panisson and Tizzoni.
PY - 2022/11/21
Y1 - 2022/11/21
N2 - 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.
AB - 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.
KW - advertising data
KW - bias
KW - poverty mapping
KW - social networks
KW - urban development
UR - http://www.scopus.com/inward/record.url?scp=85143332573&partnerID=8YFLogxK
U2 - 10.3389/fdata.2022.1006352
DO - 10.3389/fdata.2022.1006352
M3 - Article
AN - SCOPUS:85143332573
SN - 2624-909X
VL - 5
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 1006352
ER -