Rosario Delgado

Objectives: We analyze street-segment-level property crime trends in Barcelona from 2010 to 2018 using group-based trajectory modeling (GBTM), with two main goals: to assess the applicability of the law of crime concentration in a European context, and to investigate the temporal stability of hot, cool, and cold spots. Methods: GBTM is implemented using a Zero-Inflated Poisson (ZIP) model to account for over-dispersion, identifying latent trajectory groups. A post-hoc validation is conducted through: (a) comparison with time-series clustering to assess alignment, and (b) an analysis of posterior probability distributions to evaluate classification robustness. Results: Nine distinct crime trajectory groups are identified. Three of these groups, which together include just 1% of street segments, account for nearly 30% of total property crimes. While fluctuations occur among groups, the dominance of a small fraction of high-crime street segments remains, confirming the concentration of crime in specific areas. Notably, some hot and cool spots exhibit increasing trends, pointing to the emergence of new crime areas. A strong nested relationship between GBTM groups and time-series clusters further reinforces the validity of the classification. Posterior probability distributions also show clear separation among groups. Conclusions: This study supports the law of crime concentration in Barcelona while revealing specific local dynamics that differ from those found in North American cities. It demonstrates the effectiveness of combining GBTM with post-hoc analysis to examine micro-level crime patterns, offering a transferable methodological framework for other urban contexts. These findings have important implications for crime prevention and urban policy in European settings.

Ordinal classification addresses prediction tasks where class labels have a natural order but are not necessarily equally spaced. While traditional approaches typically assume symmetric misclassification costs, many real-world applications exhibit asymmetric, label-dependent penalties. This paper extends previous work on Bayes-optimal decision rules for ordinal classification under symmetric loss (Delgado, 2025) to this more general, cost-sensitive setting. Within a unified decision-theoretic framework, we formalize the interplay between three fundamental components of classification: the loss function , which encodes misclassification severity; the scoring rule , used to evaluate probabilitic predictions and shown here to satisfy regularity and properness; and the decision criterion that maps predictive distributions to class labels. We prove that the proposed scoring rule coincides with the expected loss up to a change of sign –a result of independent interest– and we explicitly characterize sufficient structural conditions under which the resulting decision criterion is well defined and Bayes-optimal. Special attention is given to the interval-scale case, where class distances are explicitly incorporated into the loss, the score, and the decision rule. We show that, depending on the structure of the loss function, Bayes optimality may hold either globally or locally in the space of predictive probability distributions. Empirical results on two real-world datasets, covering both interval-scale and fully ordinal cost-sensitive scenarios and different classifier families, illustrate the practical implications of the proposed approach.

In the field of supervised machine learning, accurate evaluation of classification models is a critical factor for assessing their performance and guiding model selection. This paper delves into the domain of ordinal classification and raises the question of adapting ordinal metrics to the interval scale. In scenarios where measurements are recorded at intervals, not only the order but also their length assume significance, and this promotes the adoption of novel performance metrics. Initially, we revisit two existing confusion matrix-based ordinal metrics and introduce a normalization technique to render them comparable and enhance their practical utility. We extend our focus to classification by intervals, proposing a robust framework for adapting ordinal metrics to the interval scale, and applying it to the aforementioned ordinal metrics. We address the challenge of unbounded rightmost intervals, a common issue in practical applications, from both theoretical and simulation perspectives, by providing a solution that enhances the applicability of the proposed metrics. To further explore practical implications, we conducted experiments on real-world datasets. The results reveal a promising trend in the use of interval-scale metrics to guide hyper-parameter tuning for improving model performance.

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification. This methodology dynamically adjusts class probabilities using misclassification costs, making it highly effective in imbalanced datasets. Our approach is further enhanced by integrating the simplicity, transparency, and effectiveness of Bayesian networks to create a robust predictive model. Using patient admission data, the model accurately identifies key risk and protective factors for COVID-19 outcomes. Our findings indicate that certain patient characteristics, such as high Charlson Index and pre-existing conditions, significantly influence the risk of ICU admission and mortality. Moreover, we introduce an explanatory model that elucidates the interrelationships among these factors, demonstrating the influence of therapeutic limits on the overall risk assessment of COVID-19 patients. Overall, our research provides a significant contribution to the field of Machine Learning by offering a novel solution for multiclass classification in the context of imbalanced datasets. This model not only enhances predictive accuracy but also supports critical decision-making processes in healthcare, potentially improving patient outcomes and optimizing clinical resource allocation.

This paper presents an innovative methodology to study the application ofseasonality(the existence of cyclical patterns) to help predict the level of crime. This methodology combines the simplicity of entropy-based metrics that describe temporal patterns of a phenomenon, on the one hand, and the predictive power of machine learning on the other. First, the classical Colwell’s metricsPredictabilityandContingencyare used to measure different aspects ofseasonalityin a geographical unit. Second, if those metrics turn out to be significantly different from zero, supervised machine learning classification algorithms are built, validated and compared, to predict the level of crime based on the time unit. The methodology is applied to a case study in Barcelona (Spain), withmonthas the unit of time, andmunicipal districtas the geographical unit, the city being divided into 10 of them, from a set of property crime data covering the period 2010-2018. The results show that (a) Colwell’s metrics are significantly different from zero in all municipal districts, (b) the month of the year is a good predictor of the level of crime, and (c) Naive Bayes is the most competitive classifier, among those who have been tested. The districts can be ordered using the Naive Bayes, based on the strength of the month as a predictor for each of them. Surprisingly, this order coincides with that obtained usingContingency. This fact is very revealing, given the apparent disconnection between entropy-based metrics and machine learning classifiers.

The present contribution focuses on investigating the interaction of people and environment in small-scale farming societies. Our study is centred on the particular way settlement location constraints economic strategy when technology is limited, and social division of work is not fully developed. Our intention is to investigate prehistoric socioeconomic organisation when farming began in the Old World along the Levant shores of Iberian Peninsula, the Neolithic phenomenon. We approach this subject extracting relevant information from a big set of ethnographic and ethnoarchaeological cases using Machine Learning methods. This paper explores the use of Bayesian networks as explanatory models of the independent variables–the environment- and dependent variables–social decisions-, and also as predictive models. The study highlights how subsistence strategies are modified by ecological and topographical variables of the settlement location and their relationship with social organisation. It also establishes the role of Bayesian networks as a suitable supervised Machine Learning methodology for investigating socio-ecological systems, introducing their use to build useful data-driven models to address relevant archaeological and anthropological questions.

The detection of target species is of paramount importance in ecological studies, with implications for environmental management and natural resource conservation planning. This is usually done by sampling the area: the species is detected if the presence of at least one individual is detected in the samples. Green & Young (Green & Young 1993 Sampling to detectrare species. Ecol. Appl . 3 , 351–356. ( doi:10.2307/1941837 ) introduce two models to determine the minimum number of samples n to ensure that the probability of failing to detect the species from them, if the species is actually present in the area, does not exceed a fixed threshold: based on the Poisson and the Negative Binomial distributions. We generalize them to two scenarios, one considering the area size N to be finite, and the other allowing detectability errors, with probability δ . The results in Green & Young are recovered by taking N → ∞ and δ = 0. Not taking into consideration the finite size of the area, if known, leads to an overestimation of n , which is vital to avoid if sampling is expensive or difficult, while assuming that there are no detectability errors, if they really exist, produces an undesirable bias. Our approximation manages to skirt both problems, for the Poisson and the Negative Binomial.