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Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent. 

Quantifying user performance with metrics such as time and accuracy does not show the whole picture when researchers evaluate complex, interactive visualization tools. In such systems, performance is often influenced by different analysis strategies that statistical analysis methods cannot account for. To remedy this lack of nuance, we propose a novel analysis methodology for evaluating complex interactive visualizations at scale. We implement our analysis methods in reVISit, which enables analysts to explore participant interaction performance metrics and responses in the context of users' analysis strategies. Replays of participant sessions can aid in identifying usability problems during pilot studies and make individual analysis processes salient. To demonstrate the applicability of reVISit to visualization studies, we analyze participant data from two published crowdsourced studies. Our findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions. 

Widely used in news, business, and educational media, infographics are handcrafted to effectively communicate messages about complex and often abstract topics including `ways to conserve the environment' and `coronavirus prevention'. The computational understanding of infographics required for future applications like automatic captioning, summarization, search, and question-answering, will depend on being able to parse the visual and textual elements contained within. However, being composed of stylistically and semantically diverse visual and textual elements, infographics pose challenges for current A.I. systems. While automatic text extraction works reasonably well on infographics, standard object detection algorithms fail to identify the stand-alone visual elements in infographics that we refer to as `icons'. In this paper, we propose a novel approach to train an object detector using synthetically-generated data, and show that it succeeds at generalizing to detecting icons within in-the-wild infographics. We further pair our icon detection approach with an icon classifier and a state-of-the-art text detector to demonstrate three demo applications: topic prediction, multi-modal summarization, and multi-modal search. Parsing the visual and textual elements within infographics provides us with the first steps towards automatic infographic understanding. 

Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking. 

Networks are a natural way of thinking about many datasets. The data on which a network is based, however, is rarely collected in a form that suits the analysis process, making it necessary to create and reshape networks. Data wrangling is widely acknowledged to be a critical part of the data analysis pipeline, yet interactive network wrangling has received little attention in the visualization research community. In this paper, we discuss a set of operations that are important for wrangling network datasets and introduce a visual data wrangling tool, Origraph, that enables analysts to apply these operations to their datasets. Key operations include creating a network from source data such as tables, reshaping a network by introducing new node or edge classes, filtering nodes or edges, and deriving new node or edge attributes. Our tool, Origraph, enables analysts to execute these operations with little to no programming, and to immediately visualize the results. Origraph provides views to investigate the network model, a sample of the network, and node and edge attributes. In addition, we introduce interfaces designed to aid analysts in specifying arguments for sensible network wrangling operations. We demonstrate the usefulness of Origraph in two Use Cases: first, we investigate gender bias in the film industry, and then the influence of money on the political support for the war in Yemen. 

Data from a late spring survey of the northeast Chukchi Sea are used to investigate various aspects of newly ventilated winter water (NVWW). More than 96% of the water sampled on the shelf was NVWW, the saltiest (densest) of which tended to be in the main flow pathways on the shelf. Nearly all of the hydrographic profiles on the shelf displayed a two‐layer structure, with a surface mixed layer and bottom boundary layer separated by a weak density interface (on the order of 0.02 kg/m³). Using a polynya model to drive a one‐dimensional mixing model, it was demonstrated that, on average, the profiles would become completely homogenized within 14–25 hr when subjected to the March and April heat fluxes. A subset of the profiles would become homogenized when subjected to the May heat fluxes. Since the study domain contained numerous leads within the pack ice—many of them refreezing—and since some of the measured profiles were vertically uniform in density, this suggests that NVWW is formed throughout the Chukchi shelf via convection within small openings in the ice. This is consistent with the result that the salinity signals of the NVWW along the central shelf pathway cannot be explained solely by advection from Bering Strait or via modification within large polynyas. The local convection would be expected to stir nutrients into the water column from the sediments, which explains the high nitrate concentrations observed throughout the shelf. This provides a favorable initial condition for phytoplankton growth on the Chukchi shelf.