All the tea in China

As part of our ongoing research into the online geographies of mind-altering substances, we present in this post an analysis of caffeinated beverages in China and Taiwan. In these first two maps we compare references to tea and coffee in both Chinese and English. You see that mainland China is almost entirely dominated by references to tea in both languages. Taiwan, in contrast, is mirrored by many more references to coffee in both languages. Somewhat surprising is the fact that there are some small pockets of coffee references on the mainland in English (notably Shanghai and a few in Beijing).

Is this an indicator of a move towards more Western types of consumption (i.e. coffee) in Taiwan and Shanghai? Or perhaps it reflects areas of with larger populations of coffee-drinking expatriates? Or -- in a completely unwarrented, speculative and highly alarmist vein -- it could be a sign of an impending trade war (reminiscent of the Opium Wars) in which Caramel Frappuccinos are used to balance the western (largely American) trade deficit.

The answers are unclear, but we can explore the data further by comparing the relative visibility of references to "tea" and "coffee" in English and Chinese:

What we see here is that while there is unsurprisingly more Chinese content referencing both tea and coffee in most parts of the region, there are interestingly more references to tea in English in large parts of rural China. Many of these blue blotches of English-language references are actually layered over tea plantations in the south of the country. It is possible though that the English-language references to tea tells us more about Internet content in China than hot green or black beverages. In almost all of the cases in which there is more English-language content, it is because the English word ("tea") receives only one hit while the Chinese word ("茶") receives none. So, the explanation for these differences could simply be that Google has just not got around to indexing local content throughout much of rural China.

In any case, next time you hear some use the phrase, "not for all the tea in China" point them to these maps so they have a better sense of what they're giving up.


Thanks to Han-Teng Liao for the help with this post.

The Density of GeoNames

Geonames is a crowdsourced database of geographical names for places. Essentially a crowd-sourced gazetteer which is available for free. A very cool resource. But what really attracted our attention was the map of the density of the features in their database. The dark spots in the map below show places that have very few place names. These are largely uninhabited areas.

BUT geonames also notes that "India (0.0114) and China (0.0113) have surprisingly few GeoNames features compared to their high population density."

Yet another example of the varying density of geo-tagged information on the Internet.

The Geographies of Wikipedia in China

This post takes a more detailed look at the geographies of Wikipedia in China. Drawing only on the Chinese Wikipedia, this first map aggregates all geotagged articles at the provincial level.

The pattern is similar to what one might expect. A larger number of articles in Taiwan and the prosperous eastern provinces than than in poorer Western China. What is interesting though is the shift in pattern if we look at the geographies of all geotagged Wikipedia articles in all languages.

Here we see a radical shift in focus. Eastern provinces retain a relatively large number of articles, but it is Tibet and Xinjiang that really stand out. Is this because non-Chinese wikipedians are far more interested in writing about the two autonomous regions than their Chinese speaking counterparts?

It has been demonstrated that there appears to be a correlation between the locations of contributors and the topics that they write about. The fact that most Chinese Wikipedians are from eastern provinces would then seem to explain some of the differences between the geographies of Chinese Wikipedia and all other Wikipedias.

Or can we attribute these differences to either self- or government-censorship? There have certainly already been convincing claims made to that effect.

We plan to revisit this topic at regular intervals in order to map any possible spatial shifts over time. We would also appreciate any comments that help us to shed light on this issue.

Information Inequality

Following on from the last post, here are some examples of Google placemark inequality:

First of all, China offers perhaps one of the most striking examples of regional disparities. Beijing, Shanghai, and the Pearl River Delta Region all are characterized by heavy information densities. In other words, a lot of information has been created and uploaded about these places. However, much of the rest of the country has very little cyber-presence within the Google Geoweb. In the map below, the height of each bar is an indicator the number of placemarks in each location.

The U.S.-Mexico border along the Rio Grande river offers a similarly striking contrast between high and low information densities.

The border between North and South Korea offers another example of placemark density not being correlated to population density. For obvious reasons, very little information is being created and uploaded about North Korea. In the map below (top), each dot represents 100+ placemarks. Interestingly, there are strong similarities between the map of placemarks on the Korean Peninsula, and satellite maps of lights visible from the Peninsula at night (bottom).

image source: globalsecurity.org

Information inequalities are clearly a defining characteristic of the Geoweb. Some places are highly visible, while others remain a virtual terra incognita. In particular, Africa, South America, and large parts of Asia are being left out of the flurry of mapping that is happing online (e.g. the Tokyo/Yokohama metro region has almost three times as many 0/1 placemark hits (923,034) as the entire continent of Africa (311,770)). Some of the geographical implications of cyber-visibility and invisibility have been examined in part (e.g. here and here), but there is clearly a lot more to be discussed. In particular, because Google allows any keyword to be searched for (not only "0" and "1"), we are able to explore not only the raw amounts of information attached to each place, but also the contents of that information.