arXiv subjects

Niklas Elmqvist

Publications and source records attributed to Niklas Elmqvist.

57 records · Page 4Linked to original sources

arXiv2018

DataSite: Proactive Visual Data Exploration with Computation of Insight-based Recommendations

Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.

cs.HC↗

arXiv2017

Overlaying Quantitative Measurement on Networks: An Evaluation of Three Positioning and Nine Visual Marker Techniques

We report results from an experiment on ranking visual markers and node positioning techniques for network visualizations. Inspired by prior ranking studies, we rethink the ranking when the dataset size increases and when the markers are distributed in space. Centrality indices are visualized as node attributes. Our experiment studies nine visual markers and three positioning methods. Our results suggest that direct encoding of quantities improves accuracy by about 20% compared to previous results. Of the three positioning techniques, circular was always in the top group, and matrix and projection switch orders depending on two factors: whether or not the tasks demand symmetry, or the nodes are within closely proximity. Among the most interesting results of ranking the visual markers for comparison tasks are that hue and area fall into the top groups for nearly all multi-scale comparison tasks; Shape (ordered by curvature) is perhaps not as scalable as we have thought and can support more accurate answers only when two quantities are compared; Lightness and slope are least accurate for quantitative comparisons regardless of scale of the comparison tasks. Our experiment is among the first to acquire a complete picture of ranking visual markers in different scales for comparison tasks.

cs.GR↗

arXiv2017

Gatherplots: Generalized Scatterplots for Nominal Data

Overplotting of data points is a common problem when visualizing large datasets in a scatterplot, particularly when mapping nominal dimensions to one of the scatterplot axes. Transparency, aggregation, and jittering have previously been suggested to address this issue, but these solutions all have drawbacks for assessing the data distribution in the plot. We propose gatherplots, an extension of scatterplots that eliminates overplotting, particularly for nominal variables. In gatherplots, every data point that maps to the same position coalesces to form a stacked entity, thereby making it easier to compare the absolute and relative sizes of data groupings. The size and aspect ratio of data points can also be changed dynamically to make it easier to compare the composition of different groups. Furthermore, several embedded interaction techniques support slicing and dicing the gatherplot by pivoting on particular dimensions, ranges, and values in the dataset. Our evaluation shows that gatherplots enable users from the general public to judge the relative portion of subgroups more quickly and more correctly than when using conventional scatterplots with jittering. Furthermore, a review conducted by a group of visualization experts evaluated and commented on the gatherplot design.

cs.HC↗