Identification and mapping of workplace accident hotspots in Brazil using machine learning and spatial analysis techniques
DOI:
https://doi.org/10.58951/dataset.2024.027Keywords:
Accident hotspots, Machine learning, Spatial analysis, Occupational safety, ClusteringAbstract
This study aims to identify and analyze workplace accident hotspots in Brazil, focusing on critical sectors such as construction, road transportation, mining, and electric energy. The adopted methodology involves applying machine learning algorithms, specifically K-means, DBSCAN, HDBSCAN, and Agglomerative Clustering, for clustering accident data provided by INSS. Additionally, spatial analysis techniques were used with GIS tools to map and visualize areas with the highest incidence of accidents. The results revealed that most accidents are concentrated in metropolitan regions, particularly in the Southeast and South of Brazil. The clustering algorithms identified risk patterns across different sectors, highlighting inadequate training and the non-use of personal protective equipment (PPE) as critical factors. Spatial analysis provided a clear visualization of hotspots, offering insights into the formulation of more effective and targeted safety policies. It is concluded that combining machine learning techniques with spatial analysis is a powerful approach for identifying workplace accident hotspots, significantly contributing to risk reduction and the promotion of safer work environments. The study opens possibilities for future research that integrates socioeconomic and cultural variables into workplace accident analysis.
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