Extracting and analyzing crime patterns in big cities is a challenging spatiotemporal problem. The hardness of the problem is linked to two main factors, the sparse nature of the crime activity and its spread in large spatial areas. Sparseness hampers most time series (crime time series) comparison methods from working properly, while the handling of large urban areas tends to render the computational costs of such methods impractical. Visualizing different patterns hidden in crime time series data is another issue in this context, mainly due to the number of patterns that can show up in the time series analysis. In this paper, we present a new methodology to deal with the issues above, enabling the analysis of spatiotemporal crime patterns in a street-level of detail. Our approach is made up of two main components designed to handle the spatial sparsity and spreading of crimes in large areas of the city. The first component relies on a stochastic mechanism from which one can visually analyze probable×intensive crime hotspots. Such analysis reveals important patterns that can not be observed in the typical intensity-based hotspot visualization. The second component builds upon a deep learning mechanism to embed crime time series in Cartesian space. From the embedding, one can identify spatial locations where the crime time series have similar behavior. The two components have been integrated into a web-based analytical tool called CriPAV (Crime Pattern Analysis and Visualization), which enables global as well as a street-level view of crime patterns. Developed in close collaboration with domain experts, CriPAV has been validated through a set of case studies with real crime data in S ão Paulo -Brazil. The provided experiments and case studies reveal the effectiveness of CriPAV in identifying patterns such as locations where crimes are not intense but highly probable to occur as well as locations that are far apart from each other but bear similar crime patterns.

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Visualization assisted crime analysis tools used by public security agencies are usually designed to explore large urban areas, relying on grid-based heatmaps to reveal spatial crime distribution in whole districts, regions, and neighborhoods. Therefore, those tools can hardly identify micro-scale patterns closely related to crime opportunity, whose understanding is fundamental to the planning of preventive actions. Enabling a combined analysis of spatial patterns and their evolution over time is another challenge faced by most crime analysis tools. In this paper, we present Mirante, a crime mapping visualization system that allows spatiotemporal analysis of crime patterns in a street-level scale. In contrast to conventional tools, Mirante builds upon street-level heatmaps and other visualization resources that enable spatial and temporal pattern analysis, uncovering fine-scale crime hotspots, seasonality, and dynamics over time. Mirante has been developed in close collaboration with domain experts, following rigid requirements as scalability and versatile to be implemented in large and medium-sized cities. We demonstrate the usefulness of Mirante throughout case studies run by domain experts using real data sets from cities with different characteristics. With the help of Mirante, the experts were capable of diagnosing how crime evolves in specific regions of the cities while still being able to raise hypotheses about why certain types of crime show up.

The prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and computational techniques based on machine learning. Student Dropout Prediction (SDP) is a challenging problem that can be addressed following various strategies. On the one hand, machine learning approaches formulate it as a classification task whose objective is to compute the probability of belonging to a class based on a specific feature vector that will help us to predict who will drop out. Alternatively, survival analysis techniques are applied in a time-varying context to predict when abandonment will occur. This work considered analytical mechanisms for supporting the decision-making process on higher education dropout. We evaluated different computational methods from both approaches for predicting who and when the dropout occurs and sought those with the most-consistent results. Moreover, our research employed a longitudinal dataset including demographic, socioeconomic, and academic information from six academic departments of a Latin American university over thirteen years. Finally, this study carried out an in-depth analysis, discusses how such variables influence estimating the level of risk of dropping out, and questions whether it occurs at the same magnitude or not according to the academic department, gender, socioeconomic group, and other variables.

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Studying and analyzing crime patterns in big cities is a challenging spatio-temporal problem. The difficulty of the problem is linked to different factors such as data modeling, unsophisticated hotspot detection techniques, spatio-temporal patterns, and study delimitation. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban activities in whole districts, regions, and neighborhoods. Those tools can hardly allow micro-scale crime analysis closely related to crime opportunity, whose understanding is fundamental for planning preventive actions. Given that, enabling a combined analysis of spatial patterns and the visualization of the different crime patterns hidden in their evolution over time is another challenge faced by most crime analysis tools. In this dissertation, we propose a set of approaches for interactive visual crime analysis. Relying on machine learning methods, statistical and mathematical mechanisms, and visualization, each proposed methodology focus on solving specific crime-related problems. These proposed tools to explore specific locations of the city turned out to be essential for domain experts to accomplish their analysis in a bottom-up fashion, revealing how urban features related to mobility, passerby behavior, and presence of public infrastructures (e.g., terminals of public transportation and schools) can influence the quantity and type of crimes. The effectiveness and usefulness of the proposed methodologies have been demonstrated with a comprehensive set of quantitative and qualitative analyses, as well as case studies performed by domain experts involving real data of different-sized cities. The experiments show the capability of our approaches in identifying different crime-related phenomena.

Automatic trajectory processing has multiple applications, mainly due to the wide availability of the data. Trajectory data have a significant practical value, making possible the modeling of various problems such as surveillance and tracking devices, detecting anomaly trajectories, identifying illegal and adverse activity. In this study, we show a comparative analysis of the performance of two descriptors to detect anomaly trajectories. We define Wavelet and Fourier transforms as trajectory descriptors to generate characteristics and subsequently detect anomalies. The experiments emphasize performance in the description in the coefficient feature space. For that, we used unsupervised learning, specifically clustering techniques, to generate subsets and identify which are irregular. The implications of the study demonstrate that it is possible to use descriptors in trajectories for automatic anomaly detection and the use of unsupervised learning methods that automatically segment the required information. The performance and comparative analysis of our study are demonstrated through experiments and case studies considering synthetic and real data sets that leave evidence of our contribution.

High and persistent dropout rates represent one of the biggest challenges for improving the efficiency of the educational system, particularly in underdeveloped countries. A range of features influence college dropouts, with some belonging to the educational field and others to non-educational fields. Understanding the interplay of these variables to identify a student as a potential dropout could help decision makers interpret the situation and decide what they should do next to reduce student dropout rates based on corrective actions. This paper presents SDA-Vis, a visualization system that supports counterfactual explanations for student dropout dynamics, considering various academic, social, and economic variables. In contrast to conventional systems, our approach provides information about feature-perturbed versions of a student using counterfactual explanations. SDA-Vis comprises a set of linked views that allow users to identify variables alteration to chance predefined students situations. This involves perturbing the variables of a dropout student to achieve synthetic non-dropout students. SDA-Vis has been developed under the guidance and supervision of domain experts, in line with some analytical objectives. We demonstrate the usefulness of SDA-Vis through case studies run in collaboration with domain experts, using a real data set from a Latin American university. The analysis reveals the effectiveness of SDA-Vis in identifying students at risk of dropping out and proposes corrective actions, even for particular cases that have not been shown to be at risk with the traditional tools that experts use.