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Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
advertising data
bias
poverty mapping
social networks
urban development
Computer Science (miscellaneous)
Information Systems
Artificial Intelligence
bias
poverty mapping
social networks
urban development
Computer Science (miscellaneous)
Information Systems
Artificial Intelligence
URI
https://hdl.handle.net/20.500.14018/26522
Abstract
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2022-11-21
Language
ISBN
Identifiers
10.3389/fdata.2022.1006352