Population 2022 (Millions)
2021 (Max. 1)
“Open Algorithm (OPAL) for Public Data and Good” seeks to merge different “privacy enhancing techniques” (PETs), such as federated learning, differential privacy, and negative databases, to allow trusted third parties such as researchers or official institutions to analyze censuses or national surveys’ microdata produced by national statistical offices (NSOs), as well as other administrative records, to derive indicators using these data, while avoiding privacy risks. A pilot is expected to be conducted in Mexico, and DPA plans to expand to additional NSOs and other public data holders in the future.
In partnership with the United Nations Economic Commission for Latin America and the Caribbean (ECLAC), DPA offered a series of workshops particularly focused on Big Data and the Digital Economy in the Latin American and the Caribbean region designed for development practitioners, policymakers, and researchers. Five editions were delivered in: Santiago de Chile (March 2016), São Paulo (September 2017) —in partnership with Cetic.br—, Mexico City (October 2017) —in collaboration with the National Digital Strategy (EDN) program and the MIT Sloan School of Management—, Santo Domingo (April 2019), and Bogotá (May 2019) —in partnership with DANE.
EmpoderaData builds upon the success of the “Quantitative Step” (Q-Step) program, which was developed as a strategic response to the shortage of quantitatively-skilled social science graduates in the United Kingdom. Together, University of Manchester and Data-Pop Alliance expanded upon the program’s excellent results, exploring this model in the Global South as the “EmpoderaData Project”. The project aimed to promote a virtuous cycle of social transformation by fostering data literacy skills applied to addressing our society’s most pressing issues in the framework of the Sustainable Development Goals (SDGs).
“Parallel Worlds” is a project developed by the Data-Pop Alliance and Oxfam México, with the purpose to analyze inequality in Mexico City, using mobility data provided by Cuebiq’s Data for Good program. The project aimed to inform and influence public policy actors in making decisions that contribute to reducing social and economic segregation based on the privilege and marginalization associated with certain spaces in the city. More specifically, DPA analyzed urban inequality in Mexico City through the mapping of movement patterns in the city, using mobile data to identify segregation patterns, in terms of where people live, work, and consume. The report analyzes three dimensions of inequality: i) in access to education, ii) the right to the city, by analyzing exclusive spaces, and iii) in access to culture. A version of this paper was published in English by Projections, the Journal of the MIT Department of Urban Studies and Planning.
A study published by the Open Society Foundations has reported numerous “atrocity crimes” perpetrated in Mexico against the civilian population since 2006. Against this backdrop, with United Nations Office on Drugs and Crime (UNODC) support, DPA sought to gain better insights into organized and interpersonal crime, by undertaking a scoping study to build a comparative research study in two cities: one heavily affected by organized crime violence and another with low organized crime rates, where violence is mostly interpersonal. To this end, this scoping study aimed at building a research proposal for two cities, according to data availability (i.e. traditional and Big Data sources) and crime dynamics; suggested methodology, and potential partners.