A strategy for visual structural data analysis of labor accident data

• Ano de defesa: 2019
• Autor(a) principal: Rodrigues, Mateus Pinto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Ciência da Computação
• Palavras-chave em Português: Acidente de Trabalho; Dados Governamentais; Visualização da Informação; Labor Accident; Governmental Data; Information Visualization; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufu.br/handle/123456789/28282; http://doi.org/10.14393/ufu.di.2019.2578

Labor accidents are a serious social problem that results in damages to employees, employers, and governments, also consuming a significant portion of the World's GDP. In Brazil, The Brazilian Federal Labor Prosecution Office is the institutional service responsible for the defense of worker rights, and among its functions is the supervision and control of labor health and safety. They collect data on labor accidents in Brazilian territory and provide an anonymized version of this data publicly. This process generates a large volume of data containing important strategical information, which is often not straightforward to be extracted with manual analysis. Information visualization is a research area that studies the creation of visual representations for abstract structured or non-structured data, aiming to help people execute tasks more effectively. We propose a computational strategy employing a combination of Information Visualization techniques to perform a visual analysis of labor accident data, while not being restricted to this scenario. We developed a system that implements our strategy, and is comprised of two complementary visualizations, i) a multidimensional projection layout + a political map, and ii) a treemap layout + a parallel sets layout. We performed several exploratory analysis, in order to exploit the visualizations' complementary capacities in providing simultaneous analysis of different data aspects. We obtained interesting results, identifying profiles associated with small/large geographical areas, similarities among geographically distant localities, occurrence patterns related to cities' size and economic development, the frequency distribution of labor accident types in Brazil, and characterized labor accidents in terms of occupation type, gender differences, causer agent, among other aspects. We believe that the proposed strategy facilitates and enhances the analysis of labor accident data, providing effective and efficient means to help governments to evaluate current public policies and foment the creation of new ones to reduce labor accidents and grant safety to employees, and also encourage transparency in governments and citizen participation.