Data visualization is the fastest growing aspect of data blogging (based on informal and super scientific method of “Googling”). Why this is the case is up to debate. Perhaps it is due to the straightforward nature of data visualization: almost anyone can look at a graph and decipher its content. Or perhaps this is due to the technological revolution making it easier (via open source, etc.) to share and create new forms of visualization.
One way we could understand the popularity of data visualization is by tracing its history. I find the work of Michael Friendly and Daniel J. Denis to be the most comprehensive in cataloging milestones in data visualization. Their website is called Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization. Their work involves breaking down each milestone in an interactive timeline. Friendly also has a paper by the same name, which is worth the read if you are interested in this topic.
There are some interesting stuff on the site, such as the first star chart (pictured below) and the hysterical best/worst data visualizations.
“By the mid-1800s, all the conditions for the rapid growth of visualization had been established. Official state statistical offices were established [throughout] Europe, in recognition of the growing importance of numerical information for social planning, industrialization, commerce, and transportation. Statistical theory, initiated by Gauss and Laplace, and extended to the social realm by Guerry  and Quetelet , provided the means to make sense of large bodies of data.” (Friendly 18)
Conversely, the mid-1930s is described as the “modern dark ages” of data visualization:
“There were few graphical innovations, and, by the mid-1930s, the enthusiasm for visualization which characterized the late 1800s had been supplanted by the rise of quantification and formal, often statistical, models in the social sciences. Numbers, parameter estimates, and, especially, standard errors were precise. Pictures were—well, just pictures: pretty or evocative, perhaps, but incapable of stating a “fact” to three or more decimals. Or so it seemed to statisticians.” (Friendly 27)
What about today? According to Friendly, the period that we’re living in now is known as the “High-D data visualization” era. This period is characterized by accelerated and varied visualization processes mainly due to technological advancement. We should note that there have not been many innovations from the 2000s.
When we place data in context of time, we can see why data visualization remains to be the most popular type of data site out there: We are in a period of awakening from the data dark ages. This movement reflects our own mission to improve historical data access to the public and our use of tools such as GIS data mapping to push the boundaries of visualization.