Tag Archives: data visualization

Network Praxis: Shock Incarceration in New York State 2008-18

I had a sneaky feeling that my dataset wasn’t going to work for network analysis, but I had found such a good dataset that I decided to try. This is an Excel spreadsheet compiled by the New York State Department of Corrections listing 602,665 people incarcerated in New York State over the last ten years, with information about admission type, county, gender, age, race/ethnicity, crime and facility. I knew six hundred thousand records were too many, but I figured I’d select just a few, and analyze the networks I would find in these.

The “few” records I selected were those of 771 men and women sentenced in 2018 to shock incarceration, a military-style boot camp initiative that was supposed to reform incarcerated people by subjecting them to strenuous physical and mental trials. According to the U.S. Department of Justice, shock incarceration involves “strict, military-style discipline, unquestioning obedience to orders, and highly structured days filled with drill and hard work.” The data I looked at shows that most people in these facilities were incarcerated for drug-related offenses such as criminal sale of a controlled substance (CSCS) or criminal possession of a controlled substance (CPCS). When marihuana is legalized the population in these facilities – and others – should, I hope, drastically decrease.

I fed the 771 records into Cytoscape and it was a total mess. I tried analyzing only the 106 women sentenced to shock incarceration in 2018 and that was still a mess. The main problem, I realized, was that I could see no clear relationships between the men and women listed in my data other than the relationship they have with the facility in which they are confined. I don’t know who hangs out with whom. I don’t know if people sentenced for different crimes are placed on different floors. It would be too much work to find out who transports the food to the facility and how many guards there are and so on. Frustrated with my project, I saw that trying to get data to bend to software is a lousy way to go about things. I started to think instead about what software would help me explore the data in a meaningful way and decided to see what I could do with Tableau. This was such a good choice that I’m having a hard time stopping myself from building more and more visualizations with what became a wealth of information when I stopped looking for networks that weren’t there.

I couldn’t embed Tableau Public in WordPress so I paste pictures here, but you can’t click and scroll and interact with my visualizations here, and some of the pictures are cut off so please visit my visualization on Tableau. By the way, I was happy to remember that students can get Tableau Desktop for free for a year. Here’s the link: https://www.tableau.com/academic/students

First, here is the mess I made with Cytoscape (I didn’t even try to figure out how to embed):

Isn’t that horrible?! Here’s a close-up:

And here are pictures of what I did with Tableau:

Phew, that’s all for now. See it on Tableau, there’s no comparison.

Make Space for Ghosts: Lauren Klein’s Graphic Visualizations of James Hemings in Thomas Jefferson’s Archive

In “The Image of Absence: Archival Silence, Data Visualization, and James Hemings,” Lauren Klein discusses a letter by Thomas Jefferson to a friend in Baltimore which she accessed through Papers of Thomas Jefferson Digital Edition , a digital archive which makes about 12,000 and “a significant portion” of 25,000 letters from and to Jefferson available to subscribers of the archive. In this letter, Jefferson asks his friend in Baltimore to give a message to his “former servant James” to illustrate how a simple word search would fail to identify that “James” as his former slave James Hemings, the brother of Sally Hemings, Jefferson’s slave and probably mother of five of his children.[1] Drawing our attention to how the “issue of archival silence – or gaps in the archival record – [which remain] difficult to address” in graphic visualization, Klein notes that the historians who built the Jefferson Papers archive added metadata to indicate that the James referred to in the above-mentioned letter was James Hemings [664]. I wonder what the metadata looks like; I wonder whether it provides sources or reflection, and what the extratextual conversation going on at the back end of the archive, if conversation it is, reveals.

While meta-annotation may appear to be a good way to fill the gaps of archival silence, Klein argues that adding scholarship as metadata creates too great a dependence on the choices the author of the archive made. The addition of metadata to the letter to the friend in Baltimore makes me wonder where in the archive metadata was added, where not, and why. Are all the gaps filled? Had metadata not been added to the letter Klein discusses, an analysis of the archive could conclude that Jefferson never makes any mention of James Hemings in the letter he wrote to his friend in Baltimore in 1801 to try to find Hemings, or in the ensuing correspondence between Hemings and Jefferson through Jefferson’s friend, in which Jefferson tries to hire Hemings and Hemings sets terms that were probably not met [667]. A word search in the archive, however, pulls up only inventories of property, documents of manumission, notes about procuring centers of pork and cooking oysters (Hemings was Jefferson’s chef) and finally a letter in which Jefferson asks whether it’s true that Hemings committed suicide [671]. How, asks Klein, do we fill in the gaps between the pieces of information we have? She concludes that we can’t. How do we show the silences then, she asks; how do we extract more meaning from the documents that exist – letters, inventories, ledgers and sales receipts – “without reinforcing the damaging notion that African American voices from before emancipation […] are silent, and irretrievably lost?” [665].

Klein calls for a shift from “identifying and recovering silences” to “animating the mysteries of the past” [665] but not by traditional methods. Instead, Klein says that the fields of computational linguistics and data visualization help make archival silences visible and by doing so “reinscribe cultural criticism at the center of digital humanities work” [665]. Through visualization Klein fills the historical record with “ghosts” and silences, rather than trying to explain away the gaps. The visualizations she creates are both mysterious and compelling, and bear evidence in a way that adding more words does not.

[1]Sarah “Sally“ Hemings (c. 1773 – 1835) was an enslaved woman of mixed race owned by PresidentThomas Jefferson of the United States. There is a “growing historical consensus” among scholars that Jefferson had a long-term relationship with Hemings, and that he was the father of Hemings’ five children,[1] born after the death of his wife Martha Jefferson. Four of Hemings’ children survived to adulthood.[2] Hemings died in Charlottesville, Virginia, in 1835. [Wikipedia contributors, “Sarah ‘Sally’ Hemings”]

What is Visualization? – a deeper look into what data visualization can tell us

Following up on one of my concerns last week and “All Models Are Wrong“ from two weeks ago, I’m going to write more today on what information visualization does and does not tell us, inspired by Lev Manovich’s “What is Visualization”.

In the beginning of the reading, Manovich seems to support the argument from All Models are Wrong, in that models only tell a portion of the story.

“By employing graphical primitives (or, to use the language of contemporary digital media, vector graphics), infovis is able to reveal patterns and structures in the data objects that these primitives represent. However, the price being paid for this power is extreme schematization We throw away %99 of what is specific about each object to represent only %1- in the hope of revealing patterns across this %1 of objects’ characteristics.” Lev Manovich, What is Visualization?

In this excerpt, Manovich makes clear the advantage of traditional means of information visualization: revealing easily recognizable patterns from data that would otherwise take hours, days, or weeks to analyze. On the contrary, he admits that the downfall of simplifying the data is in the very act of simplifying it. This was troubling to me. I so desperately wanted there to be a way to visualize the data without loosing data, then along came “direct visualization”.

“Direct visualization” is a term coined my Manovich to explain a technique that employs visualization without reduction. He gave several examples that are no longer searchable, but two that had a strong impact on my understanding of “direct visualization”. These are Timeline (Jeremy Douglass and Lev Manovich, 2009) and Valence (Ben Fry, 2001). Both have a very “next generation” feel to them which is another aspect to “direct visualization”; technology giving us the ability to decipher massive amounts of data in a short time, and present it with the use of color, animation, and interactive elements.

This was a fascinating read and “direct visualization” is something I’m looking forward to applying to my own work where possible.

TEXT MINING — OBITS + SONGS + ODES

My process with the Praxis 1 Text Mining Assignment began with a seed that was planted during the self-Googling audits we did in the first weeks of class, because I found an obituary for a woman of my same name (sans middle name of initial).

From this, my thoughts went to the exquisite obituaries that were written by The New York Times after 9-11 which were published as a beautiful book titled Portraits. One of my dearest friends has a wonderful father who was engaged to a woman who perished that most fateful of New York Tuesdays. My first Voyant text mining text, therefore, was of his fiancee’s NYT obituary. And the last text I mined for this project was the obituary for the great soprano Monserrat Caballe, when I heard the news of her passing as I was drafting this post.

The word REVEAL that appears above the Voyant text box is an understatement. When the words appeared as visuals, I felt like I was learning something about her and them as a couple that I would never have been able to grasp by just reading her obituary. Indeed, I had read it many times prior. Was it the revelation of some extraordinary kind of subtext? Is this what “close reading” is or should be? The experience hit me in an unexpected way between the eyes as I looked at the screen and in the gut.

My process then shifted immediately to song lyrics because, as a singer myself who moonlights as a voice teacher and vocal coach, I’m always reviewing, teaching and learning lyrics. I saw the potential value of using Voyant in this way in high relief. I got really juiced by the prospect of all the subtexts and feeling tones that would be revealed to actors/singers via Voyant. When I started entering lyrics, this was confirmed a thousand fold on the screen. So, completely unexpectedly, I now have an awesome new tool in my music skill set. The most amazing thing about this is that I will be participating in “Performing Knowledge” an all-day theatrical offering at The Segal Center on Dec. 10 for which I submitted the following proposal that was accepted by the Theater Dept.:

“Muscle Memory: How the Body +  Voice Em”body” Songs, Poems, Arias, Odes, Monologues & Chants — Learning vocal/spoken word content, performing it, and recording it with audio technology is an intensely physical/psychological/organic process that taps into and connects with a performer’s individually unique “muscle memory”, leading to the creation of vocal/sound art with the body + voice as the vehicle of such audio content. This proposed idea seeks to analyze “songs” as “maps” in the Digital Humanities context. Participants are highly encouraged to bring a song, poem, monologue, etc. with lyric/text sheet to “map out”. The take-away will be a “working map” that employs muscle memory toward learning, memorizing, auditioning, recording and performing any  vocal/spoken word content. –Conceived, written and submitted by Carolyn A. McDonough, Monday, Sept. 17, 2018.” [I’m excited to add that during the first creative meeting toward this all-day production, I connected my proposed idea to readings of Donna Haraway and Kathering Hayles from ITP Core 1]

What better way to celebrate this, than to “voyant” song/lyric content and today’s “sad news day” obituary of a great operatic soprano. Rather than describe these Voyant Reveals through writing further, I was SO struck by the visuals generated on my screen that I wanted to show and share these as the findings of my research.

My first choice was “What I Did For Love” from A Chorus Line (on a sidenote, I’ve seen the actual legal pad that lyricist Edward Kleban wrote the score on at the NYPL Lincoln Center performing arts branch, and I thought I had a photo, but alas I do not as I really wanted to include it to show the evolution from handwritten word/text to Voyant text analysis.)

I was screaming as the results JUMPED out of the screen at me of the keyword “GONE” that is indeed the KEY to the emotional subtext an actor/singer needs to convey within this song in an audition or performance which I KNOW from having heard, studied, taught, and seen this song performed MANY times. And it’s only sung ONCE! How does Voyant achieve this super-wordle superpower?

I then chose “Nothing” also from A Chorus Line as both of these songs are sung by my favorite character, Diana Morales, aka Morales.

Can you hear the screams of discovery?!

Next was today’s obit for a great soprano which made me sad to hear on WQXR this morning because I once attended one of her rehearsals at Lincoln Center:

A complex REVEAL of a complex human being and vocal artist by profession.

AMAZING. Such visuals of texts, especially texts I know “by heart” are extremely powerful.

Lastly, over the long weekend, I’m going to “Voyant” this blog post itself, so that its layers of meaning can be revealed to me even further. –CAM