In the age of Big Data, we face flood-like data every day and everywhere. It is not easy for us to
understand and digest
all the plain and raw data. How to present such data creatively and understandably thus becomes our
primary concern. We
followed the human-centered and iterative design process in our data visualization projects to make
the stories behind
being told clearly. Although there are still drawbacks in our visualizations, we learned from
feedback and from fixing
these issues and gradually made more progress.
There are three major reflections from our design:
We've been trying to combine aesthetic and more understandable types of charts in our three
assignments. Some succeed
but some are not. For example, in our Viz of Non-Domestic Violence Related Assaults, we did the
circular heat map to
present the density and seasonal distribution of crime cases. Although it's more aesthetic than some
basic charts such
as a line chart, the result we presented may mislead and scare our audiences. The boxes that
represent the crime
incidents in the outer circle (2020's data) are bigger than the boxes in the inner circle (2015's
data), but in reality,
the violence numbers are better.
Storytelling is one of the important skills. We can't only show the data without considering
meanings, audiences and
stories. In our Viz of Healthcare Spending and Life Expectancy in the United States, although we've
used text to show
main stories and aspects, we lack clear titles and legends to make the story obvious to audiences.
For example, the
money bag icon in the dumbbell chart doesn't provide hints - who is spending the money? The
government or person?
Our creativity and ideas may be restricted by the assignment's marking rubric, but in reality,
assignments provide a
chance for us to practice real-world skills. We must have the right attitude toward the assignment,
not just completing
a "job".
HONG XIONG , 490504828, hxio9342   |   WENLIN WANG, 500430866, wwan2311   |  
LUOHAN LIU, 490375642,
lliu9493
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