The analysis of massive databases is a key issue for most applications today and the use of parallel computing techniques is one of the suitable approaches for that. Apache Spark is a widely employed tool within this context, aiming at processing large amounts of data in a distributed way. For the Statistics community, R is one of the preferred tools. Despite its growth in the last years, it still has limitations for processing large volumes of data in single local machines. In general, the data analysis community has difficulty to handle a massive amount of data on local machines, often requiring high-performance computing servers. One way to perform statistical analyzes over massive databases is combining both tools (Spark and R) via the sparklyr package, which allows for an R application to use Spark. This paper presents an analysis of Brazilian public data from the Bolsa Família Programme (BFP—conditional cash transfer), comprising a large data set with 1.26 billion observations. Our goal was to understand how this social program acts in different cities, as well as to identify potentially important variables reflecting its utilization rate. Statistical modeling was performed using random forest to predict the utilization rated of BFP. Variable selection was performed through a recent method based on the importance and interpretation of variables in the random forest model. Among the 89 variables initially considered, the final model presented a high predictive performance capacity with 17 selected variables, as well as indicated high importance of some variables for the observed utilization rate in income, education, job informality, and inactive youth, namely: family income, education, occupation and density of people in the homes. In this work, using a local machine, we highlighted the potential of aggregating Spark and R for analysis of a large database of 111.6 GB. This can serve as proof of concept or reference for other similar works within the Statistics community, as well as our case study can provide important evidence for further analysis of this important social support programme.

Cartographic resources play a crucial role in facilitating communication across various sectors, including research projects focused on low-income communities. Despite this, some researchers still adhere to colonialist and exploitative approaches. This study aims to promote equitable university–community interaction though cartographic resources, aid academic and vulnerable community users in choosing a better platform for their work, and provide insights to developers for improving the platforms to better serve the user profiles of community members. To achieve this, we examined the use of cartographic resources in five projects within low-income communities (commonly referred to as favelas or so-called “slums”) in three Brazilian cities, all guided by equitable principles. The study unfolds in four stages: (i) data collection from documents and interviews; (ii) systematization into seven analytical categories—cartographic resources, data, personnel, processes, equipment, general objectives, and specific objectives; (iii) analysis of eight cartographic resources; and (iv) a critical examination of the outcomes. The synthesis of the collected information identified 65 characteristics/demands, with 17 common to all projects, including vector feature creation, thematic map design, printed map usage, and satellite imagery. We also identified 53 geographic information system (GIS) functionalities required for the projects, predominantly related to vector data generation and editing. The outcomes demonstrate the benefits of project methodologies, contributing to a decolonial university–community praxis. Additionally, they underscore the potential of digital cartographic resources, functioning not solely as data collection tools but also as powerful instruments that empower slum residents to advocate for improvements and foster local development.

Introduction: The intense urbanization process has resulted in the reduction of forested areas, which poses an additional risk to public health. The aim of this study was to identify environmental variables in an urban community associated with the chances of injuries (bites/scratches) in humans caused by bats. Methodology: The study community was the Historic Center District of Salvador, Bahia, Brazil. The cases were the official records of households that reported injuries in humans caused by bats along the historical series from 2012 to 2015. Controls were selected from households near the cases without records of injuries involving bats. Univariate analysis was performed for the data using the chi-square and Fisher's exact test. Significant variables (p < 0.05) were included in the logistic regression models. Results: The variable for bats having access to households via defective/broken windows showed an association with the cases in the final model (OR = 45.14, CI = 6.08-335.10). The variables presence of domiciled dogs (OR = 6.04, CI = 1.44-25.92) and exposed fruit (OR = 4.41, CI 95% = 1.15-16.9) were significant factors. Conclusion: The results shows that access to the residence and supply of food that can be used by bats are factors that increase the chances of injuries in humans caused by these animals possibly increasing the risk of infectious diseases.