The “Spiky” Geography of Art History

…according to the Metropolitan Museum, NYC


According to its founding mandate: “The mission of The Metropolitan Museum of Art is to collect, preserve, study, exhibit, and stimulate appreciation for and advance knowledge of works of art that collectively represent the broadest spectrum of human achievement at the highest level of quality.”
Over the past few years, the Metropolitan Museum has catalogued over 25% of its holdings online. This represents ~590,000 objects, covering over 5,000 years of human history from 17 curatorial departments. The diversity of objects in a museum’s collection (and the amount of contextual information known about these objects) may reflect the kinds of narratives a museum can curate about artistic and global history. This animation charts the provenance and year of production of every single object that is catalogued on the Metropolian Museum website, whenever this information is known.
The geography of art history is, in some ways, “spiky.” Certain regions, particularly cities, are home to diverse and famous artistic output. Thomas Friedman similarly describes globalization as being spiky and concentrated in big cities. Other regions are comparatively less productive and less often collected. Either this reflects museum curator’s historic bias against Africa, Latin America, etc. in favor of Europe. Or, this might reflect a more fundamental historical reality: If geography guides artistic production and privileges regions with good geography, like areas surrounding the Mediterranean, then landlocked and inaccessible regions with poor geography will have less “exciting” artistic output.
If you liked this, please see my analysis and animation of the Museum of Modern Art’s collection history, where I seek to answer the question Where in the world is modern art?



In this animation, each colored dot indicates one geographical location represented by art in the Met’s online collection. The dot’s location indicates where this object was created. The dot’s size corresponds to the number of objects from this location. The time each dot appears corresponds to the year this object was created. Collectively this animation reveals the potential geographical and temporal preferences of the Met’s online inventories for objects collected in the common era (the year 1 c.e. to present-day). The dots above are assumed to be a relatively accurate sample size.

However, there are many objects in the collections with known provenance but unknown production date. Figure 1 below illustrates objects with known provenance and known year. Figure 2 shows objects with known provenance, regardless of whether year is known. The data-set in figure 2 has approximately double the number of objects, but these are concentrated in the same regions as objects in figure 1. This is because objects with known year also tend to have known provenance. Hence, figures 1 and 2 exhibit similar tendencies.


Art objects from ancient cultures like China, Egypt, and Sumeria frequently have known provenance but unknown year of production. This year might be estimated to the level of century with the help of carbon dating and through comparison with similar objects whose date is known for certain. Were the dates of these ancient objects known for certain, they could have been included in the animation above, thereby increasing the size and density of dots in under-represented regions. In this case, the animation would have resembled figure 2.

There is one more interpretive problem: Does this visualization reveal more about the diversity of the collections, or the preferences for which objects are selected for inventory online? For instance, does the statistical absence of objects from East Asia, in comparison to France, mean that the Met collects objects from East Asia less actively and in fewer quantities? Or, does this absence merely mean that fewer objects from the East Asian collections are selected for display on the museum website?

Metadata for this animation was downloaded here from the Met Museum’s website, then edited as a spreadsheet in excel and visualized in Tableau Public. This data was published by the museum staff in the public domain under a Creative Commons license. I am also publishing this visualization as an interactive map; it is open source and free to download  at this link.


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