How multiple coordinated views influence cognitive load?

Guideline: Guidelines for Using Multiple Views in Information Visualization
Source: [1] Scherr, M. (2009). Multiple and Coordinated Views in Information Visualization.
[2]: Baldonado, Michelle Q. Wang et al. “Guidelines for Using Multiple Views in Information Visualization.” Advanced Visual Interfaces (2000).

I wonder about your opinion about whether multiple coordinated views (MCV) affect human’s cognitive load. What is the optimal design strategies for geospatial data while creating the MCV system? Do you think that this approach leads to increased cognitive user load? Would you prefer to use different strategies like glyph based design, etc. rather than MCV? Regarding the reference paper, if used properly, multiple views can minimize cognitive load compared to only a single view[1]. On the other hand, The wrong use of multiple views can have quite the opposite effect[2].

Thank you in advance!

Hi there,

I see the reason for your interest in this topic completely. In my opinion you are absolutely right when you say that a Multiple Coordinated View (MCV) can have a negative or/and a positive effect on cognitive user load. I read the paper and I think that these rules which are mentioned by Scherr, M. (2009) are very helpful to decide if and how a MCV is put in place. These rules, which have been formulated by Baldonado and Wang et al (2000), concentrate on several different work steps. Firstly, we want to ask ourselves, is it necessary to use a MCV at all. For me this is an extremely important question. Depending on the level of knowledge the user has and the complexity of the data, a MCV can be more or less effective. When the data is complex, it can be useful to visualize it on different views. One should remember when thinking about the target audience, that the massive load of information which is visualized can be even more confusing when the datasets behind it are completely unknown to the user. Multiple views and different types of graphs, where different ranges of the dataset can be selected, will only make the task to get to know the data, even harder. But if the person has already some knowledge about what the dataset is about and how the dataset has been acquired, it might generate additional benefit for the application user to see certain correlation between data points.

Further they talk about how to set up the MCV. This is also a possible step, especially when talking about geospatial data. I think the great advantage of geospatial data are their georeferenced locations. Personally, I think this is an important characteristic to keep in the visualization. Here the rules of Attention Management and of Resource optimization rules by Baldonado and Wang et al (2000) are key points for me. Attention Management is crucial with geospatial data because the geo-location on a world map draws the intention of the user the most in my opinion and should be treated as the main view. Smaller graphs should give information on areas or points on the map but should not be the main visualization. In my opinion the biggest advantage of geospatial data is their location on a map. This helps the user to get a spatial understanding of the data. Overall, I enjoyed MCV quite a lot in my projects. It gave me the opportunity to get an overview over my data rather quickly and it is a compact way to visualize relationships and features of datasets.