Problem-driven visualization design of health and pollution big data
We present the design of a big data visualization application aimed at investigating the potential relationships between air pollution and perinatal health. We integrated data from singleton pregnancies in Brazil over a seven-year period and records of fine particulate matter (PM2.5) levels. Our methodology combines Design by Immersion and co-design, engaging specialists from various domains in problem-driven visualization design. The interactive visualization allows exploratory queries and comparative analyses based on medical birth certificate data restructured according to estimated conception dates. The collaboration of various domain experts was essential for leveraging complex data for an informed decision-making visualization.
Authors
- Júlia Rabetti Giannella
- Doris Kosminsky
- Renato Campos Mauro (ORCID: https://orcid.org/0000-0002-4363-499X)
- Ximena Illarramendi (ORCID: https://orcid.org/0000-0003-1504-1553)
- Igor Falconieri (ORCID: https://orcid.org/0009-0008-9137-0539)
- Cláudio Esperança
- Lucas Barcellos Oliveira (ORCID: https://orcid.org/0000-0001-7282-9510)
Institutions
- Universidade Federal do Rio de Janeiro (BR)
- Federal Center for Technological Education Celso Suckow da Fonseca (BR)
- Universidade Federal Fluminense (BR)
Publication Details
- Journal
- InfoDesign - Revista Brasileira de Design da Informação
- Published
- 2024-10-07
- DOI
- https://doi.org/10.51358/id.v21i2.1131
- Primary Topic
- Big Data Technologies and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bill and Melinda Gates Foundation
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro