We present results from an experiment aimed at using logs of interactions with a visual analytics application to better understand how interactions lead to insight generation. We performed an insight-based user study of a visual analytics application and ran post hoc quantitative analyses of participants' measured insight metrics and interaction logs. The quantitative analyses identified features of interaction that were correlated with insight characteristics, and we confirmed these findings using a qualitative analysis of video captured during the user study. Results of the experiment include design guidelines for the visual analytics application aimed at supporting insight generation. Furthermore, we demonstrated an analysis method using interaction logs that identified which interaction patterns led to insights, going beyond insight-based evaluations that only quantify insight characteristics. We also discuss choices and pitfalls encountered when applying this analysis method, such as the benefits and costs of applying an abstraction framework to application-specific actions before further analysis. Our method can be applied to evaluations of other visualization tools to inform the design of insight-promoting interactions and to better understand analyst behaviors.

When visualizing data with uncertainty, a common approach is to treat uncertainty as an additional dimension and encode it using a visual variable. The effectiveness of this approach depends on how the visual variables chosen for representing uncertainty and other attributes interact to influence the user's perception of each variable. We report a user study on the perception of graph edge attributes when uncertainty associated with each edge and the main edge attribute are visualized simultaneously using two separate visual variables. The study covers four visual variables that are commonly used for visualizing uncertainty on line graphical primitives: lightness, grain, fuzziness, and transparency. We select width, hue, and saturation for visualizing the main edge attribute and hypothesize that we can observe interference between the visual variable chosen to encode the main edge attribute and that to encode uncertainty, as suggested by the concept of dimensional integrality. Grouping the seven visual variables as color-based, focus-based, or geometry-based, we further hypothesize that the degree of interference is affected by the groups to which the two visual variables belong. We consider two further factors in the study: discriminability level for each visual variable as a factor intrinsic to the visual variables and graph-task type (visual search versus comparison) as a factor extrinsic to the visual variables. Our results show that the effectiveness of a visual variable in depicting uncertainty is strongly mediated by all the factors examined here. Focus-based visual variables (fuzziness, grain, and transparency) are robust to the choice of visual variables for encoding the main edge attribute, though fuzziness has stronger negative impact on the perception of width and transparency has stronger negative impact on the perception of hue than the other uncertainty visual variables. We found that interference between hue and lightness is much greater than that between saturation and lightness, though all three are color-based visual variables. We also found a compound relationship between discriminability level and the degree of dimensional integrality. We discuss the generalizability and limitation of the results and conclude with design considerations for visualizing graph uncertainty derived from these results, including recommended choices of visual variables when the relative importance of data attributes and graph tasks is known.

The purpose of this study was to evaluate the efficacy and safety of transarterial embolization (TAE) for iatrogenic renal arterial pseudoaneurysm and arteriovenous fistula at our center.Our retrospective analysis included 27 patients who received TAE for iatrogenic renal arterial pseudoaneurysm and arteriovenous fistula between January 2006 and January 2016. Data on demographics, type of minimally invasive renal procedures, clinical manifestation, imaging features, embolization procedure, and perioperative details were collected. The technical and clinical success rates were analyzed. Furthermore, the changes in serum creatinine and eGFR before and after embolization were recorded and compared by t test.The median time between iatrogenic renal injury and TAE was 3 days (range, 0–110 days), with most patients (24/27, 88.9%) receiving TAE within 14 days. Only 1 patient was diagnosed with renal artery pseudoaneurysm 110 days after laproscopic partial nephrectomy. The technical and clinical success rates were 100% and 96.3%, respectively, with 1 patient requiring a second embolotherapy at the third postoperative day. No other patient required additional endovascular or surgical intervention due to recurrent hemorrhage. The mean serum creatinine before TAE was 92.8 ± 25.3 μmol/L and after TAE, 96.1 ± 27.7 μmol/L (P = .095). The eGFR of pre- and postembolization was 75.2 ± 26.5 mL/min/1.73 m2 and 72.5 ± 26.2 mL/min/1.73 m2 (P = .16). No severe complications were observed during follow-up.This retrospective review demonstrated that TAE for the treatment of iatrogenic renal artery pseudoaneurysm and/or arteriovenous fistula was safe and associated with high technical and clinical success rate.