Portugal has the sixth highest road fatality rate among European Union members. This is a problem of different dimensions with serious consequences in people’s lives. This study analyses daily data from police and government authorities on road traffic accidents that occurred between 2016 and 2019 in a district of Portugal. This paper looks for the determinants that contribute to the existence of victims in road traffic accidents, as well as the determinants for fatalities and/or serious injuries in accidents with victims. We use logistic regression models, and the results are compared to the machine-learning model results. For the severity model, where the response variable indicates whether only property damage or casualties resulted in the traffic accident, we used a large sample with a small imbalance. For the serious injuries model, where the response variable indicates whether or not there were victims with serious injuries and/or fatalities in the traffic accident with victims, we used a small sample with very imbalanced data. Empirical analysis supports the conclusion that, with a small sample of imbalanced data, machine-learning models generally do not perform better than statistical models; however, they perform similarly when the sample is large and has a small imbalance.

Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main factors associated with a RTA. The present study aims to contribute to the identification of the determinants for the type of RTA: collision, crash, or pedestrian running-over. These factors are essential for identifying specific countermeasures because there is a relevant relationship between the type of RTA and its severity. Daily RTA data from 2016 to 2019 in a district of Portugal were analyzed. A statistical multinomial logit model was fitted. The identified determinants for the type of RTA were geographical (municipality, location, and parking areas), meteorological (air temperature and weather), time of the day (hour, day of the week, and month), driver’s characteristics (gender and age), vehicle’s features (type and age) and road characteristics (road layout and type). The multinomial model results were compared with several machine learning algorithms, since the original data of the type of RTA are severely imbalanced. All models showed poor performance. However, when combining these models with ROSE for class balancing, their performance improved considerably, with the random forest algorithm showing the best performance.

Old and abandoned mines are testimonials of ancient industrial activities, and as such, they are able to convey environmental concerns. A multidisciplinary approach combining ground measurements—i.e., portable X-ray fluorescence and optical spectroradiometry—with airborne multispectral images—i.e., Sentinel-2 and unmanned aerial vehicles—was conducted to define a baseline for the characterization of areas that constitute environmental burdens. The Mostardeira mine, an old copper mine located in the Portuguese Ossa-Morena Zone, was selected as a case study. The results reveal that the soils have toxic metals—e.g., As (mean = 1239 ppm) and Cu (mean = 435 ppm)—above the defined health thresholds. The spectroradiometry provided insights into the soil characterization using data from the vis-NIR spectral region, allowing us to distinguish agricultural soil, mine waste, and bare soils. The spectra obtained are comparable with the USGS soil spectra standards, namely Clinozoisite Epidote HS299, Hematitic Alt. Tuff CU91-223, and Sand DWO-3-DEL2ar1 no oil. The airborne images considered through the lens of principal components analysis and supervised and unsupervised machine learning techniques (random forest and K-means) are found to be effective tools in creating cartographic representations of the contaminated soils. The collected data are used to construct a baseline for characterizing these environmentally challenging areas, whereas the methodological approach is revealed to be successful for tackling the posed environmental problems, allowing us to map the old mine environment passives.

Road traffic accidents are a major concern for modern society with a high toll on human life and involve hard to account economic consequences. New knowledge can be obtained from combining GIS tools with machine learning and artificial intelligence, developing what is, in this work, identified as spatial intelligence. This approach is tested in a case study of Setúbal district, Portugal, for the period of 2016 to 2019. Departing from a heatmap analysis, and applying kernel density estimation, new spatial approaches were used, namely DBSCAN and Getis-Ord. The results obtained allowed the identification of novel meaningful locations of road traffic accidents. Consequently, the knowledge built from the underlying patterns is considered the key to developing new strategies to solve this modern social curse. The methodology proposed in this study demonstrates that the combination of expertise built from the different spatial analyses can provide a better understanding of the determinants of road traffic accidents. This approach is expected to be valuable for data analysts and decision-makers, contributing to diminishing human losses related to road traffic accidents.

Road Traffic Accidents (RTA) cause human losses and irreparable physical and psychological damage to many of the victims. They also involve a very relevant economic dimension. It is urgent to improve the management of human and material resources for more effective prevention. This work makes an important contribution by presenting a methodology that allowed for achieving a predictive model for the occurrence of RTA on a road with a high RTA rate. The prediction is obtained for each road segment for a given time and day and combines results from statistical methods, spatial analysis, and artificial intelligence models. The performance of three Machine Learning (ML) models (Random Forest, C5.0 and Logistic Regression) is compared using different approaches for imbalanced data (random sampling, directional sampling, and Random Over-Sampling Examples (ROSE)) and using different segment lengths (500 m and 2000 m). This study used RTA data from 2016–2019 (training) and from May 2021–June 2022 (test). The most effective model was an ML logistic regression with the ROSE approach, using segments length 500 m (sensitivity = 87%, specificity = 60%, AUC = 0.82). The model was implemented in a digital application, and a Portuguese security force is already using it.

Seagrass beds and salt marshes play a crucial role in preserving and restoring estuarine ecosystems, providing essential services such as carbon sequestration, water filtration, and habitat for diverse marine life. Mapping these areas, particularly in remote locations, is challenging but essential for effective conservation efforts. This study explores the use of drones equipped with multispectral cameras to map seagrass meadows and salt marshes in estuarine environments. The methodology involved composing images into a multilayer multidimensional dataset, performing exploratory data analysis and principal component analysis, and utilizing Random Forest supervised classification to generate detailed maps. Applied to a case study of the Mira River in southern Portugal, this approach achieved mapping accuracies as high as 94% with kappa values of 0.91. These results demonstrate the efficacy of drone-based multispectral imaging and advanced analytical techniques in accurately mapping and monitoring critical estuarine habitats, offering a valuable tool for conservation and restoration initiatives.