The influence of domain expertise on visual overviews of spatiotemporal data

Overviews of spatiotemporal data are acknowledged to play an important role in visualization in initiating and supporting geovisualization and exploratory data analysis (EDA). However, relatively little research has …

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Abstract

Overviews of spatiotemporal data are acknowledged to play an important role in visualization in initiating and supporting geovisualization and exploratory data analysis (EDA). However, relatively little research has focused on the visual overviews themselves, and their potential impacts on EDA outcomes. In a user study, we evaluated the influence of different levels of domain knowledge on the usefulness of four distinct types of static visual overview of spatiotemporal data. Beyond simply orienting users, our results indicate that visual overviews can be important in gaining insights into a data set, for example, in learning about metadata. Although subjects without domain knowledge struggled to judge the quality of their findings, they were as successful at identifying interesting patterns in the data as those with domain expertise. Our results suggest that detailed background knowledge of a data set can actively hinder EDA. Being already familiar with their own data sets, our results highlight the tendency of data experts to disregard findings that do not match their pre-existing domain knowledge. Based on these findings, our conclusions identify a range of potential avenues for future work, including the use of visual overviews that deliberately do not, from first view, reveal the context of the data they show. This later approach could help in cases where domain experts need to see their data with ‘fresh eyes’, and detect interesting patterns in spatiotemporal data before relating the findings to specific knowledge about the data sets and the domain.

Figures

Visualization V2 (minimized to fit page width, zoomed-in inset) showing fish movement (transparent gray lines) between river zones (y-axis) over time (x-axis).

Schematic map of the monitored part of the Murray River and its tributaries. The river is partitioned into 24 different river zones (a–x) by 18 logging towers (dots). Zone n is the dammed Lake Mulwala.

Visualization V1 (minimized to fit page width, zoomed-in inset) showing location (colorcoded river zones) of all fish (randomly ordered fish IDs on y-axis) through time (x-axis).

Visualization V3 (minimized to fit page width, zoomed-in inset) shows the number of fish (aggregated over time) moving from a specific river zone to any of the other river zones through star plots. The arm lengths of the star plots indicate numbers of fish, while the colors relate to the river zone the fish are moving to.

Visualization V4 (minimized to fit page width) shows the number of fish (aggregated over time) moving from a specific river zone to any of the other river zones in an OD matrix. The y-axis labels the river zones where fish movement originates from; the x-axis shows the destination river zones.

Indication of the average numbers of relevant statements per participant in each participant group (DE1–ND2) to gauge discussion intensity. The data experts (DE1) discussed the visualizations most intensely, followed by the domain experts (DE2). The PhD students (ND1) and senior researchers (ND2) discussed least intensely in terms of average number of statements.

Comparison of relative numbers of statements per visualization type (V1–V4) and participant group (DE1–ND2). The differences between the number of statements of the different participant groups are significant at the 99% confidence level (χ2 = 75.73, df = 12, p-value < .001).

BibTeX

@article{bleisch_influenceOfDomainExpertiseOnSpatTempOverviews_2017,
 abstract = {Overviews of spatiotemporal data are acknowledged to play an important role in visualization in initiating and supporting geovisualization and exploratory data analysis (EDA). However, relatively little research has focused on the visual overviews themselves, and their potential impacts on EDA outcomes. In a user study, we evaluated the influence of different levels of domain knowledge on the usefulness of four distinct types of static visual overview of spatiotemporal data. Beyond simply orienting users, our results indicate that visual overviews can be important in gaining insights into a data set, for example, in learning about metadata. Although subjects without domain knowledge struggled to judge the quality of their findings, they were as successful at identifying interesting patterns in the data as those with domain expertise. Our results suggest that detailed background knowledge of a data set can actively hinder EDA. Being already familiar with their own data sets, our results highlight the tendency of data experts to disregard findings that do not match their pre-existing domain knowledge. Based on these findings, our conclusions identify a range of potential avenues for future work, including the use of visual overviews that deliberately do not, from first view, reveal the context of the data they show. This later approach could help in cases where domain experts need to see their data with ‘fresh eyes’, and detect interesting patterns in spatiotemporal data before relating the findings to specific knowledge about the data sets and the domain.},
 author = {Bleisch, Susanne and Duckham, Matt and Pettit, Chris},
 doi = {10.1080/23729333.2017.1294820},
 journal = {International Journal of Cartography},
 number = {2},
 pages = {166--186},
 title = {The influence of domain expertise on visual overviews of spatiotemporal data},
 url = {https://www.tandfonline.com/doi/full/10.1080/23729333.2017.1294820},
 volume = {3},
 year = {2017}
}