These maps are based on an archival document I have been fascinated by for a while: the dancer William Dollar’s pseudonymised 410-page account of American Ballet Caravan’s South American tour, entitled “Old Granny Spreads Goodwill.” They continue the work I did with the dancer birthplaces and citizenship, in that it they are meant to complicate the exchange of touring, by visualizing it in terms of a more global network instead of something point-a-to-point-b or even a loop. I see these maps as the first stage of an associative map that depicts the world as it was seen and imagined from the perspectives of dancers on tour (as recorded in this case by a single dancer).
I constructed the underlying dataset for these by searching through Dollar’s manuscript for moments when allusions or comparisons to other places appear. For example, late in the tour, a location just outside Cucuta is compared to the “Badlands of North Dakota.” Early on in the tour, European cities tend to provide more regular points of reference for Dollar, while later on he increasingly references the South American cities the company previously visited. Languages were also part of how dancers built imaginative associations that remapped their world; there is a great description of “Maestro” (Balanchine) in Rio de Janeiro attempting to apply his English, French, and Russian to the Portuguese journal in front of him. While some associations seem to be based on places to which the dancers had previously traveled, other were likely imagined, such as the comparison between the Guaya and Congo rivers.
For this first map, I used color to indicate the city from which the association is being made, and composite operations appear darker (ie: if the same line would be drawn more than once). Note: As always, if you want to get more up close and personal than the basic zoom and drag functions that are embedded, the best way is to click the share button in the upper right, and then “link to this map” will take you to CartoDB’s site.
This first iteration of the dataset required many decisions. In order to be able to filter later, I categorized each GeoJSON linestring by location, nationality, or language, depending on how the association was made. However, it would be useful to introduce additional gradation in order to account for the difference between encountering people from a place — for example, a dancer taking up with a Polish refugee in Bogota — and a comparison to the distant place itself. At this point, I omitted all references to historical backgrounds about places, as well as the backgrounds of the dancers themselves, if neither were part of framing a particular experience. While I stuck primarily to locations that are referenced by name in general or specific form, I did fill in gaps in a few instances, such as locating “Nazi” in Germany, or correcting the transliterated “Rawshia” to Russia.
As one final test, I layered this dataset with another that I built previously regarding number of shows in each location, from lightest (least shows) to darkest (most shows). Here, I also flipped the coloring of the lines. Whereas on the first map, lines were colored by the city from which the association originated, on this second map, the association lines are colored by the (most common) cities to which they refer. Later on in the tour, you see how dancers returned to Rio and Buenos Aires, where they had stayed longer, as newly acquired points of reference.
This is still a first step. While I have noted some of the ways this dataset could be further refined, there other places to go, as well. It would be great for example, to begin to warp the basemap itself, whether historical or contemporary, in order to redraw a picture of the world from the dancers’ eyes.