Hi, my name is Nil Tuzcu. I am a member
of the Growth Lab at the Harvard Kennedy
School. Today, I'm going to talk about
our case study. In this work, we
introduce a design process for
developing Metroverse, a data
visualization platform that communicates
economic data and growth opportunities
for cities.The web-
based database tool developed based on
the research at the Growth Lab. The tool
provides Interactive Data visualizations
about city economies. The
tool covers
around 1000 cities worldwide which a
re
displayed on a map. When the user opens
the websites, the user can either
browse the map or type in a specific
city name and once the city is
selected, the user is taken to the data
visualization pages, the user interface.
Includes a navigational arc on the
left that list available
questions, essential database,
graphic and interactive text on the
rights.Well, before
I discuss the challenges we face in this
project, I'd like to provide some
background information about our team.
The growth map is part of the Harvard
Kennedy School, which is a public policy
and government school. The lab has three
main themes. The academic team conducts
research on economic developments. The
policy team conducts applied research on
the fields they collaborate with
governments to help with effective
policy implementation, and
finally.Our team, the digital
design and development team, translate
research into engaging, visual,
interactive narratives that make
complex political and economic data
accessible to the public. As
designers within a government school
working with economy and policy
researchers, we face several unique
challenges that I like to list.
First, how can we present data
heavy scientific research in a user
friendly manner. How can we design a
tool that serves multiple
purposes including teaching
Advanced Research and data-driven policy
making?Finally, how
can we develop a tool that effectively
guards users to understand economic
drivers while also allowing them
to generate their own insights?
In the first phase of our work, we focus
on understanding the academic research
and core data sets that underlie our
visualization platform. We call this
phase two way knowledge sharing as we
work closely with the academic research
to understand their research theory,
data sources, methodology,
concepts, findings and insights.
During this process, we hold multiple
meetings per week to ensure effective
communication.Now we learn
from the academic team. We also
communicate our internal design and
development process. We explain what
it means to translate research into web
platform that will be accessible to the
public. Unlike traditional methods of
sharing academic research, which
typically targets the scholarly
community through publications,
presentations, and teaching, a website
is a public good with different use
cases and therefore our main
purpose in the two way knowledge
sharing process is to 1st
comprehend the research on datasets,
manage the expectations of the academic
team and guide them about the
potential use cases of the web platform
by non academic communities.
In the second step of the discovery
phase, we explore the datasets and
decide which data visualization to
include in our tool. We developed a
novel methodology to guide us through
this process.Before
creating any data visualization, our
team first ask three main
questions. What is the question that
this database tries to answer? What is
the purpose this question serves? And
finally, who is the intended
audience?To begin
with a research centric process, we
listen to the data to decide what kind
of questions it can answer. For example,
here we lay out all potential
questions. We then group these
questions based on their purposes, which
align with the researching sites.
And finally, for each
potential question, we examine the data
set based
on 4 dimensions, quality up to dateness, scope, and temporal coverage. This method resulted in
four questions to
include in our platform.
Well, now that we know the questions and
purposes, the next step is to
identify the audiences for these
visualizations. We identified
potential groups for this tool based on
its purposes, which are teaching
Advanced Research and data-driven policy
making.While we were familiar with
the teacher and researcher personas
and their use case of such tools in the
growth lab, we didn't have much insights
into potential users in the
policymaking world. To address
this, we put together a pilot user group
of people in government entities around
the world, working in various
roles.5
Personas to work with.
In these personas, we looked for
three key dimension. The first dimension
is data literacy level, the second
dimension is expertise and economic
knowledge, and final dimension
is their tendency
towards being opini- being
opinionated versus natural. Let
me explain what I mean by that. An
opinionated user will
look for a specific insight in the data
visualization.And while a
natural user would generally explore
explore the visualization, these
three dimensions were essential to
decide the information density that is
displayed on each page and to find
a good balance between exploratory and
descriptive visualization styles.
Well, our design process is always
highly iterative and involves many
interactive prototypes of data, graphics
through prototyping, vitrite, various
visual data encodings as well as
interaction design techniques. For
example, one of the visualization with
the highest information density is the
industry space graphic that simply
shows how industries are related to
each other.We built
25 prototypes to find the right way of
visually encoding the data as well as
to find the optimum solution for user
interactions.
Throughout the process, we conducted
multiple user tests and quantified some
of the feedback to establish a benchmark
system for ourselves.
Well, now that I briefly described
our process, I like to revisit the
challenges we face and summarize 2
key points in our process that's
basically helped us overcome them.
We found that there are a few important
aspects to consider when translating
complex research into a user friendly
tool with with multiple
purposes. First and foremost, it
was crucial to have a thorough
understanding of the research on data.
That's why we adopted the research
centered approach in our process rather
than a traditional user centered
approach.We started
by examining their academic research and
then decided to feature sets based on
the available core datasets.
Our discovery phase of exploring the
research and data continued until the
last day of the product developments.
And secondly, we found that using
personas effectively and creatively was
essential. For instance, we
encountered the challenge of creating a
tool that guides.Users to
understand economic drivers while
enabling them to generate their own
insights.To find the right
balance, we examined each
persona and decided whether
they would benefit from an exploratory
or a descriptive visualization.
We then designed the user interface for
user interface features that aim
to achieve this balance. And
finally, I like to point some
aspects of the projects that that did
not work well. Particularly, we
took two important.Lessons for future
projects first, we found that
having learning materials for a new tool
is as important as any feature of the
tool. Our interviews reveal
that current Metroverse users need
detailed introduction on the research
concepts and specific visualization
and seconds. Our observations and
learnings from the users suggested that
the tool needs an onboarding page.
That summarizes what the tool
offers, while in sum,
we learned that we need to develop a
holistic approach to onboarding and
teaching the users about the tool,
and this needs to be
simultaneous with the early design
decisions.