
Ask HN: Why isn't the Gapminder Software more popular? - classicsnoot
I am a huge fan of the late Hans Rosling. His presentations[1] of big data trends are informative and inspiring. I had high hopes for the software[2], but in attempting to use it i have found it is very clunky. Is this why i never see anyone using it? How does one contribute to making software more usable if one is not a programmer?<p>[1] https:&#x2F;&#x2F;www.ted.com&#x2F;playlists&#x2F;474&#x2F;the_best_hans_rosling_talks_yo<p>[2] https:&#x2F;&#x2F;www.gapminder.org&#x2F;tools&#x2F;#_locale_id=en;&amp;chart-type=bubbles
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PaulHoule
If you look at

[https://www.amazon.com/Visual-Display-Quantitative-
Informati...](https://www.amazon.com/Visual-Display-Quantitative-
Information/dp/0961392142)

you see that world class infographics require a "data artist", that is a lot
of curation. You have to set the parameters of the chart quite carefully to
produce a graphic which appears insightful. It is very easy to change those
parameters a little and wind up in a bad place, where you might overload the
tools with too much data, etc.

Data viz tools that are easy for a non "data artist" to use are an active area
for CS research, new software products, etc.

Another problem is that data is less interesting than it seems to be at first.
What action are you going to take on it? Data analysis delivers value when it
informs actions.

The history of international development efforts is that aid agencies often
have little understanding about conditions on the ground. For instance, they
would send big tractors to Chile and truck them at great trouble and expense
to get them into farming villages that could not use them, fuel them, fix
them, etc.

Thus reducing a country down to a few numbers could cause the illusion that
you know something when you really don't.

The numbers themselves are suspect. In a place like Rwanda, few people file
tax returns, much of the economy is farmers selling corn to their neighbors,
so national income numbers are often a wild-assed guess.

Also the life expectancy numbers are not based on a rigorous probability
analysis, but rather a simple model that takes the death rates of 15 year old
people in 2015 and 45 year old people in 2015 and treats that like a Markov
chain where you are pretending that the 15 year old in 2015 is going to die at
age 45 in 2045 at the same rate that that 45 year old people die in 2015,
which is just not true -- particularly if you consider exceptional events such
as war, famines, etc.

Thus those graphics are great for a talk, but they don't have the real depth
of knowledge you'd need if you want to sell something to those people, plan a
business, run an effective aid program, etc.

