Case Study 01: Making Visualizations Resonate with How Researchers Think
By: Adrian Galvin
Tags: Space, Mars, Visualization, Data Visualization, Mars Rovers

Visual Solution

This view is simpler, it culls unnecessary detail from the representation in order to represent the answer to precisely what the researcher wants to understand. This has a downstream affect on the researcher’s workflow: the visualization more clearly tracks or resonates with how the researchers think about the sample and its formation history. In the previous visualization, researchers spent much of their cognitive effort on decoding the color blocks that they were examining. In this visualization, on each card the researcher can see precisely the location and distribution of each element or mineral.

Design Decisions

The interface that this visualization lives in is black with light type and bright visualizations. I took this from radiology interfaces that I have worked on in the past. Radiologists are visual pattern analysts who see subtle and intricate differences in shades of gray. These shades represent the evidence of disease in the human body and a radiologist is significantly responsible for how a disease will be treated, they must see carefully all of the tiny subtle differences in color from an x-ray or MRI, the patient’s health depends on this. In order to support this careful seeing, radiologic interfaces are dark, which removes excess light that would otherwise constrict the pupil. With their pupils open wide to catch all of the detail, the bright visualizations become the focus of their analytic attention. The UI for this visual tool takes a similar approach because the PIXL scientists are doing something very similar, grayscale investigation of subtle patterns which represent a complex story to be discovered.

Another critical decision was to remove the color from each element map. This color originally played the role of helping the scientists to distinguish each element in opacity overlays. Additionally the team developed a common chromatic language around each element: for example all scientists on the team agreed to represent iron with red, this helps in team cohesion and consensus building. Since we removed the opacity overlay technique, each element map could be reduced to grayscale, which is a more perceptually consistent representation. Because of the contingencies of human eyes and visual cortex perception, a dark blue pixel will appear darker than a yellow pixel even if they are at the same light value. By removing color from the map, we eliminated this challenge from the scientists daily workflow. In order to preserve the team chromatic language, each map card has a single color dot next to the element letters, allowing the team to maintain their same cognitive connection of element to color.

One further challenge is the difficulty recognizing features across element maps. To overcome this, we only allow horizontal spatial comparison of maps and intersections. This way all distinct features will be at the same Y height in space, supporting side-to-side scanning tasks. To further clarify features, we created a feature trace method which allows the scientist to trace a line across the feature in the context image. Once created, this line is propagated automatically across all element maps and intersections, solidifying the exact location of the same feature across all maps.

My Role

The primary transformation in this case study occurred when my team was able to understand the underlying scientific need or inquiry. The science team, partially because of their expert status, was unable to see that their thinking had been sculpted by the contingencies of their ecosystem of non-specialized tools. In identifying and helping the team to articulate precisely the line of inquiry that they wished to open, we were able to reveal a deeper need to fulfill through our visualization. In this case, the final form of the visualization is less important in and of itself, but reveals a novel form of inquiry, one which allows the scientists to focus on comprehending the sample as opposed to decoding imagery.