After several rounds of brainstorming, ideation, and iteration, we came up with a total of
seven design ideas, two of which were multi-coordinated views and five of which were
“innovative” views. We will explain and discuss these ideas below.
Design 1: Dashboard
This is a traditional dashboard layout that many commercial products produce. It is useful
in that it shows a large amount of information at once and is familiar to anyone used to
analytics. The benefits of this is that it can be customized to exactly what the customer
desires to see while offering options to choose creative interfaces for certain views.
Brushing and linking is a common feature to use. However, because this type of standard
multi-coordinated visualization is very common, it lacks the novelty of an interesting
visualization. In addition, based on our conversation with Dr. LaBrot, Floating Doctors
currently uses Qlik to create similar dashboards; hence, this particular visualization
would not provide much additional value to the organization.
Design 2: Floating Dots
This design idea addresses two issues: it provides a new approach to the search function
across a dataset that has a large number of variables and it takes advantage of storytelling
to give the user potential interesting insights into the data.
Each data case is represented by a dot. Users can enter a name in the search bar or select
a specific group of people (e.g. all women, all people of this diagnoses, all people in this
community) and the corresponding dot(s) will be highlighted. Next, the system generates several
views which to view the dataset while keeping that initial individual or group highlighted
throughout the journey. The interface is scrollable; as the user scrolls, the dots will move
around into various other views. Each view can also be explored via clicking, hovering, or
brushing and linking to further give the user control over the data.
This design idea is fun for the user to navigate and given its storytelling element,
is helpful to the user in leading stakeholders through a summary of the progress made.
Because of the various different views possible, it incorporates a time factor where a
population can be tracked over a period of time. However, too few information is shown on each
screen which can be frustrating for users who may want to see various graphs on a single page.
There are a few ways to enhance this visualization; one way is to show more graphs on each screen.
To better track changes, we would need to add ways display trends. To enhance the storytelling element,
it was suggested that we could present a video clip and then highlight the data that relates to
the video clip and the specific scene.
Design 3: Community Bars
This diagram focuses on each community over a period of time. Each colored bar represents a
community and the x-axis is the range of time. Each pixel represented is a patient visitation
in the community at that moment in time. Filters on the right allow the user to call out certain
attributes of the dataset. Hovering over a single pixel brings up a detailed description of that visit.
This visualization allows a user to see how attributes of patients (e.g. diagnoses) change within
a community over a period of time. Each pixel may take advantage of shape or size to encode a bit
more information as well such as gender or age. However, several drawbacks of this design is the
use of the colored bars which do not inherently have a meaning other than to differentiate between
approximately thirteen communities visited in 2016. Because we expect that visits do not happen in
all communities at once, the visitation data may not look like the picture above but rather be
clustered as various “steps” as time progresses which leaves a lot of empty space.
This idea is similar to the concept of “semantic substrates” that may be of interest to us.
The placement of pixels on the y-axis can be rationalized as pixel jitter;
however, it may need to be more organized especially if many patients are visited during that
time. The positioning of pixels and the use of the y-axis still needs to resolved. It was
further suggested that we could use the circle size to show the number of patients.
Other feedback included adding a trend line or bar graph at the top and side of the chart to
show how a specific attribute of the dataset changed over a period of time.
Design 4: Bubble Diagram
This visualization groups patients into various circles. In this case, the circles can be all
the diagnoses in the dataset. The size of each circle corresponds to the number of occurrences of
that diagnosis. The user can zoom into various parts of the visualization to view more details.
In the second picture above, there are colored circles which corresponds to communities.
Each pixel within that colored circle is a patient with that diagnosis within that community.
Hovering over a pixel reveals detailed information about the patient as well as other occurrences
of that patient in other diagnoses circles. Lines between larger circles show the prevalence of
different diagnoses having a correlation with another. The thicker the line, the higher the correlation.
This visualization is fun and is more conducive to user exploration. The user can see the state of
diagnoses among patients at a moment in time. However, a possible drawback is that it doesn’t
necessarily show trends over a period of time without the use of video animation to show circles
shrinking or growing or pixels moving around.
Depending on the data, some communities might always
have a certain number of diagnoses simply because it is a larger community; hence, we would need
to consider normalizing our data. Furthermore, we discussed grouping
the circles by commonalities between communities as well.
Design 5: Pixel Clusters
This visualization involves throwing each patient data into a canvas. Each patient is represented
by a pixel. The user can use their finger (on a tablet) or mouse to “lasso” any number of pixels
and cluster them based on some attribute. In the pictures above, for example, the user may lasso the
entire dataset and cluster based on gender. Then, they may choose to cluster the males by community
or by a specific diagnosis.
One possible suggestion is to use “smart-clustering” techniques to group the pixels initially based
on how the system decides to cluster it. This design is very exploratory and is extendable to a tablet
format. In addition, it allows the user to see various proportions of the population and see the details as
well as the big picture. However, it does not show the time factor of a diagnosis or treatment over a
period of time nor does it show relationships between data attributes.
Design 6: Ray Scanner
This visualization involves an ordinary scatterplot of patient data where each patient is captured
as a pixel. The user is able to set different x and y axis values after which the pixels will
reshuffle accordingly. The most prominent feature of this design is the use of “light ray” filters
to illuminate areas of the dataset on the graph. The user can click and drag a section of either axes,
select a specific filter (e.g. community, diagnoses, treatment), and view the pixels in that range
which match that filter. Multiple filters can be overlaid on each other.
This allows the user to explore more of the data using the filters much like a flashlight, illuminating
various hidden gems. It is an innovative view but by itself, it may not offer much that a traditional
dashboard would not provide. One criticism is why the user wouldn’t just illuminate the whole
axes every time? Perhaps it would be possible to combine this visualization with the multi-view
or one of the previous concepts such as Floating Dots. In addition, we could introduce a time variable into
this visualization, which would allow this visualization to track changes and monitor progress over time.
Design 7: Interactive Radial Lines
This visualization shows a radial edge of diagnoses on the left and a radial edge of treatments
on the right. The center is a plot of the number of patient visits for certain dates and communities.
The red vertical lines correspond to female visits and blue vertical lines correspond to male visits.
Each line is constructed with individual pixels which are the individual patients. By hovering over a
patient, straight lines are illuminated that connect to the diagnoses edge and the treatment edge which
shows the diagnosis and treatment that corresponds to that particular patient. The user may also hover
over items in the diagnosis and treatment edges to show all the patients with that diagnosis or treatment
One consideration is that while the Floating Doctors organization is familiar with the countries and cities
they visit, supporters may not be. Therefore, we could include a map of each country with consultation
locations. When a user hovers over a location, the map indicates where that community is located.
This view allows the user to explore how all the diagnoses and treatments are intertwined between
individuals. It is more conducive to exploring than other visualizations and the center graph can
be modified using filters to support different views, such as age, while keeping the diagnoses and
treatment edges the same. Further features could include the capability
to select a data point, diagnosis, or treatment to use as filters, increasing the width of the bars
so the cases are easier to select, and including the capability to sort from high to low, or to sort from
most popular diagnosis to least, etc.
However, this does not show patient data over a period of time. Though there is a time scale involved,
it is not quite the same as simply seeing a trend line. Although we can overlay a thicker and darker
trend line on the center plot, doing this may make the entire visualization harder to read since a
critique of this visualization is that it would have potentially too many lines.