[Note: for all the visualizations, you can get larger views by clicking on the images.]

I have never found culture to be a particularly difficult or painful topic to teach in introduction to sociology classes. There is though a tendency in my students to perceive the relationships between technology and culture to be a one way street: technology -> culture and behavior. One of the challenges is to explore the other way around in that relationship: culture -> technology -> behavior or culture -> behavior -> technology. And that is even before you start discussing the social structures of technological production and the social institutions involved in the process (education, government, military, etc.). And that is before you even begin to discuss the fact that technology access and use is never homogeneously distributed within and between population (e.g. the digital divide), and that layers of social stratification are crucial in that respect.

But anyhoo, it is always interesting to trying to explore some data on technology as part of a larger discussion on other non-material aspects of culture, as they are all interrelated. This is my attempt at doing so (I’ll deal with values and norms in another post). I got all the data from Gapminder. The maps and bar charts were done in Tableau (sorry I still cannot embed my workbooks here, so, links to Tableau Public will have to do). The Gapminder screengrabs are what I did when I combined technology variables with GDP per capita (as a rough measure of economic development). Click on the screen grabs to go to the Gapminder interactive graphs. At the same time, because the technologies I an discussing below are relatively recent, the time series are not as crucial as they might be for other variables.

Anyhoo, feel free to use these visualizations as part of exercises to get students to think about data, patterns and technology.

So, one easy way to start is with personal computers.

Personal computer per 100 people (interactive world map and rankings via Tableau)

Static map:

PC per 100 map

It is not hard to see the big gap between North America / Western Europe and pretty much everybody else. This is more visible in the rankings.

Static partial rankings:

PC per 100 - Partial rankings

Personal computer per 100 people correlated with GDP per capital (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

PC per person and GDP

I would think that 2006 data for PCs are already pretty old data, but that was the most recent on Gapminder, so there you have it. As you can see though, coloring the bubbles based on GDP per capita helps identify a pattern of higher income -> higher availability of technology, but with a very steep curve (therefore, a bigger gap between high PC users / high GDP per capita versus the rest). One interesting question is whether this clear pattern will persist across other related technologies.

Like cell phones:

Cell phone rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Mobiles per 100 people - Map

The rates are markedly different than those for PCs (and the data a bit more recent). Anyone who travels to the Global South has to notice the omnipresence of mobile phones. It looks like a lot of countries of the Global South practically bypassed the landline stage and went straight to mobile. The rates reflect that, even though stratification patterns are still obvious.

Partial rankings – static image:

Mobiles per 100 people - Partial rankings

Cell phone rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Cell phones oer 100 and GDP

As you can see, this is a very different bubble chart than the preceding one. No steep, highly stratified curve here. There is definitely a positive correlation between GDP per capita and mobile phones subscriptions, as reflected by the color gradient, but the curve is more regular and linear.

Internet use rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Internet per 100 map

Regional patterns are rather clear on this one. Of course, the data only measure internet use, but not the quality of connections and networks.

Static partial rankings:

Internet per 100 - Partial rankings

One can clearly in which countries full access is available and achieved.

Internet user rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Internet per 100 and GDP

Here, we get a third different pattern: steep and linear, with the same stratification.

So, we know that Internet use is pretty widespread, except for the poorest countries in Sub-Saharan Africa, but as I mentioned above, Internet use does not measure quality of connection. So, let’s look at broadband.

Broadband rates per 100 people (interactive world map and rankings via Tableau)

Static image for map:

Broadband per 100 people - Map

Static partial rankings:

Broadband per 100 - partial rankings

The rates are overall much lower, across the board (with two top outliers), so one can see that broadband penetration still has a way to go, even in the richer countries.

Broadband rates per 100 correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Broadband per 100 and GDP

And here is a fourth different pattern: a lot of countries at the bottom of the graph, close to 0, with a very flat pattern, a slow rise as we get towards the high GDP per capita countries, a very smooth and slow upward curve (with our two outliers in the top right corner).

So, again, even though the different technologies I have visualized here are related to each other, one can see that their access varies widely, by technology, by regions, and by levels of development. At the same time, comparative graphs also show different patterns of global penetration of these technology, from brutally stratified curves (PC), to almost perfectly linear (mobile subscriptions), to steep curve (Internet), to long left-tailed curve (broadband). The trick would be to try to find explanations for these different patterns.

And, of course, we focused only on the technologies themselves here. Not on what people do with them and the different forms of usage. That is for another post.

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