PRAXIS 3: UNICEF Dataset

At the eleventh hour, Palladio finally worked for me — hooray!

I’m trying to contain my excitement and relief as I share my Process Notes.

My project began with a plan to “visualize absence” through “The Effect of the AIDS crisis epidemic in NYC on the Performing Arts” because as you can imagine, the performing arts tragically lost hundreds of artists to AIDS, as well as the legacy of training, peaking in the early-90’s. By peaking, I mean  “peaking” through decimation, so I thought it would be powerful to show the individuals disappearing from their individual disciplines (dance, acting, voice, writing, choreography, directing, set design, wardrobe, makeup, lighting, publicity, stage management, etc.) through their deaths. I was particularly moved by a photo gallery I’d seen entitled “Faces of Aids”

It’s an amazing annual tribute EW magazine published, in this case, for the year 1992. It’s profound to see the faces and read the names along with their professions (mostly arts-related). My plan was to do a reverse treatment of the photo gallery in datavis.

I started working with Palladio by experimenting with my own spreadsheet for PREFERENCE, my ITP & DH humanities references digital pedagogical tool project, but could not get Palladio to do anything — except frustrate me. Then it dawned on me that my spreadsheet is not a “dataset” but a “set of data” and I hope I’m accurate in that simple observation. Moral of the story: as I effort to datavis PREFERENCE, the spreadsheet has to be spot on, much like Micki Kaufman described to us in class and also during a datavis workshop I took with her in Spring.

So I had to abandon my “AIDS Visualizing Absence” concept in search of a reliable dataset.

I found many through UNICEF and I was especially intrigued because they have alot of AIDS-related datasets. I considered these but settled on:

UNICEF’s PRIMARY EDUCATION DATA and the specific dataset for Education: Primary net attendance rate – Percentage (by country)

Education: Primary net attendance rate – Percentage
Prepared by the Data and Analytics Section; Division of Data, Research and Policy, UNICEF
Last update: December 2017

I was so psyched when I achieved this map with Palladio which I display here as a screen shot (due to the limitations in Palladio Miriam Posner describes.)

UNICEF First Map

Although quite tiny and diffuse, and literally wandering off the screen, all the countries are in tact.

I was even more thrilled when I got this map to appear:

UNICEF Second Map

Because it appeared globe-like, I was hoping the coutries were in their global positions. But they’re not because what I discovered is this is just the 2D map starting to shape itself in to other views, which I find fascinating.

I had planned not to do PRAXIS 3 but as I saw everyone’s work unfolding, I really got inspired, particularly by Patrick’s work with Shakespeare and his candor in articulating that he didn’t know what the results of his project might lead to.

I feel similarly because I’m still wrapping my head around the potentialities with Voyant in the fields of study I frequent, and have many “wish list” projects for both text analysis and datavis.

More specific to DH, PRAXIS 3 is showing me how damned hard it is going to be to datavis PREFERENCE, which I knew, but now I’m becoming more psychologically prepared to tackle.