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In the field of information visualization, the concept of “tasks” is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this article, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization articles and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations. These abstracted tasks can benefit visualization researchers and practitioners as they design evaluation studies or compare their analytical tasks with ones previously studied in the literature to make informed decisions about their design. We also reflect on our novel methodology and advocate more broadly for the creation of task-based knowledge repositories for different types of visualizations. The Supplemental Material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2021.3064037 , will be maintained on OSF: https://osf.io/u5ehs/. [ABSTRACT FROM AUTHOR]

SUMMARY: Being able to predict future temperatures on the wall lining is key when controlling and scheduling maintenance for large industrial smelting furnaces. In this paper, we propose and test a machine learning approach for predicting lining temperatures in a ferronickel smelting furnace. This approach was deployed and evaluated in a real‐world scenario, i.e., in one of Cerro Matoso S.A.'s (CMSA) industrial plant furnaces. Different techniques were tested, and finally, a multitarget regression (MTR) model showed the best performance. Previous state of the art focused on predicting only one target sensor; in contrast, our model is capable of predicting up to 12 targets. Two MTR models were tested: the incremental structured output prediction tree (iSOUP‐Tree) and the stacked single‐target Hoeffding tree regressor (SST‐HT). The SST‐HT method had the best behavior in terms of the average mean absolute error (AMAE) and average root mean square error (ARMSE). The results indicate that the developed MTR models can accurately predict the measured temperature on multiple point sensors. Results of this work are expected to help the process of structural control and health monitoring of the furnace linings located at CMSA's plant. [ABSTRACT FROM AUTHOR]

The built environment of cities is complex and influences social and environmental determinants of health. In this study we, 1) identified city profiles based on the built landscape and street design characteristics of cities in Latin America and 2) evaluated the associations of city profiles with social determinants of health and air pollution. Landscape and street design profiles of 370 cities were identified using finite mixture modeling. For landscape, we measured fragmentation, isolation, and shape. For street design, we measured street connectivity, street length, and directness. We fitted a two-level linear mixed model to assess the association of social and environmental determinants of health with the profiles. We identified four profiles for landscape and four for the street design domain. The most common landscape profile was the "proximate stones" characterized by moderate fragmentation, isolation and patch size, and irregular shape. The most common street design profile was the "semi-hyperbolic grid" characterized by moderate connectivity, street length, and directness. The "semi-hyperbolic grid", "spiderweb" and "hyperbolic grid" profiles were positively associated with higher access to piped water and less overcrowding. The "semi-hyperbolic grid" and "spiderweb" profiles were associated with higher air pollution. The "proximate stones" and "proximate inkblots" profiles were associated with higher congestion. In conclusion, there is substantial heterogeneity in the urban landscape and street design profiles of Latin American cities. While we did not find a specific built environment profile that was consistently associated with lower air pollution and better social conditions, the different configurations of the built environments of cities should be considered when planning healthy and sustainable cities in Latin America. [ABSTRACT FROM AUTHOR]

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large health-care data sets. Each healthcare data set is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph-analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure. The visualization interface, known as the network explorer, provides a good overview of data and enables users to filter, select, and zoom into network details on demand. The system has been deployed on multiple sites and data sets, both government and commercial, and identified many overpayments with a potential value of several million dollars per month. [ABSTRACT FROM AUTHOR]