I’ve known for several months now that I wanted to work on a
project related to climate change during this course. Thwink.org
notes that, for difficult problems, one must take a structured
approach—addressing the challenge intuitively will fail (n.d.). This
is clearly the case when it comes to climate change—untangling
the impacts of this global phenomenon, never mind finding
solutions to adapt to or mitigate them, is the definition of
difficult. There are simply too many factors and non-linear
relationships among them to operate on an intuitive basis, and
to try would result in inappropriately narrowing the solution set
to what we think it should or could be. A colleague of mine
who is an expert is cognitive science has called on coworkers to
“resist the urge to converge” on the solution space, spending
more time in the divergent space, considering the problem from
multiple angles.
The CRISP-DM approach to problem solving is particularly
relevant here as the kind of structured approach to use. Meta
S. Brown describes the first phase (developing Business
Understanding) tasks as identifying goals, assessing the situation,
define data mining goals, and develop a plan; each of these
tasks has its own requirements, and this first step is critical
(2016). If we have the wrong business understanding, we have
no chance of coming up with the right solution, because we’ll
be solving the wrong problem. Essentially, Brown calls on us to
spend a good amount of time and energy on problem framing
and approach. Another one of our readings this week, by
Paloma Cantero-Gomez, expounds on this component, and
reminds me of a different phrase-go slow to go fast-that
underscores the importance of carefully and thoughtfully framing
the problem. Citing Albert Einstein, she notes that, with an hour
to solve a problem, 55 minutes ought to be spent thinking
about the problem, leaving just 5 for the solution (Cantero-
Gomez, 2019). I really appreciate her guidance in the 40-21-10-
5 rule, to consider the universe around the problem, but then
to break it down by narrowing the description from 40 words
to 5, getting to the very essential components, only: the root of
the problem (Cantero-Gomez, 2019).
Going on from there, Cantero-Gomez’s guidance is particularly
useful for data analysts who may not be subject matter experts
in the topic they are working on; we may have a business
partner, but it behooves us to gain a stronger appreciation for
the substantive context, implications, and consequences, to make
sure we’re offering solutions that solve the problem for the
business partner (2019). It’s important then, to be open to
reframing or refocusing the direction of the project; to
challenging assumptions and being clear about what is
established fact and what is not; and to engage in exercises
that explicitly force a change in perspective (Cantero-Gomez,
2019). I use a cognitive science technique called “zoom in, zoom
out” that gets at this idea. Cantero-Gomez tells us to frame the
problem in the form of a question, to encourage open-
mindedness (2019). I find that ensuring the question starts with
who, what, when, where, why, or how is a good way to do this.
One piece of advice that Cantero-Gomez offered was
particularly surprising to me: the importance of using positive
language to engage the brain’s ability to think of the big picture,
and to engage in active listening and problem solving (2019).
Putting all of this advice together, then, I went from my original
problem statement (actually, statements):
1. What is the relationship between climate change and
global health security?
2. Zoom in: How does/will climate change influence the
spread of vector-borne diseases, and what diseases are
particularly influenced by the climate?
3. Why does it matter? because: What populations are most
at risk of increased disease presence and spread?
4. My refined, not-quite-five-word data problem: What factors
govern the spread of vector-borne disease? aa
In doing my context gathering, I’ve learned that vector-borne
diseases have unique ideal conditions for spread (CVBD, n.d.).
Therefore, it will be important to keep that in mind in how I
design my model—for example, do I want to consider more than
one disease, or limit my study to the outlook for a particular
one, like malaria?
CRISP-DM will dictate my next steps (Brown, 2016):
• Investigating what suitable data are available. An initial
exploration suggests that reliable sources of information
may include the US Centers for Disease Control (CDC), the
World Health Organization, and the United Nations.
Socioeconomic datasets that would complement data about
the diseases may be found on the World Bank’s website.
In addition, I will need to find a reliable source of
projected climate change data (including temperature,
weather events, and humidity/aridity) and socioeconomic
data to make predictions about the future risk of disease.
• Data preparation, which will include figuring out how to
merge the datasets (i.e. finding the factor that will serve as
the key; in this case, most likely to be a regional
descriptor, like country), and determining a method for
evaluating data fidelity and dealing with missing or bad
data.
• Data exploration and modeling. What relationships exist
among the various factors? What can we see by graphing
the data? Which relationships are not obvious, but seem to
exist?
• Evaluate model performance—applying the model
developed in training to a test set of data.
• Integrate results: what recommendations can be made
based on the model’s findings? Which locales require more
resources to be invested in disease mitigation/health
adaptations? Where might we be able to reduce these
investments because the risk may decrease with climate
change?
References:
Brown, M. S. (2016, Mar 31). The right process for big data
analytics profit. Forbes.
https://www.forbes.com/sites/metabrown/2016/03/31/open-
standard-process-yields-best-big-data-analytics-
results/?sh=4ca287e43fae
Cantero-Gomez, P. (2019, April 10). How to frame a problem to
find the right solution. Forbes.
https://www.forbes.com/sites/palomacanterogomez/2019/04/10/h
ow-to-frame-a-problem-to-find-the-right-solution/#1282a1125993
CVBD (Companion Vector-Borne Diseases). (n.d.). Occurrence
maps. https://cvbd.elanco.com/cvbd-maps
Thwink.org (n.d.). What is an analytical approach?
http://www.thwink.org/sustain/articles/000_AnalyticalApproach/ind
ex.htm
