7 Months of Obituaries

As a jumping off point:

In journalism, we recognize a kind of hierarchy of fame among the famous. We measure it in two ways: by the length of an obituary and by how far in advance it is prepared.

-Walter Cronkite

The chart:

The grey bars represent the ten longest obituaries of the last 7 months. From left to right, they are:

“A Star Idolized and Haunted, Michael Jackson Dies at 50″ (2839 words)
“Jack Nelson, an Investigative Reporter, Dies at 80″ (1266 words)
“Dominick Dunne, Writer Who Chronicled High-Profile Crime, Is Dead at 83″ (1966 words)
“Robert Rines, Inventor and Monster Hunter, Dies at 87″ (2839 words)
“Howard Unruh, 88, Dies; Killed 13 of His Neighbors in Camden in 1949″ (1304 words)
“Roy DeCarava, Harlem Insider Who Photographed Ordinary Life, Dies at 89″ (1485 words)
“Walter Cronkite, 92, Dies; Trusted Voice of TV News” (2968 words)
“Budd Schulberg, ‘On the Waterfront’ Writer, Dies at 95″ (1855 words)
“Henrich, Yankees Clutch Hitter, Dies at 96″ (1086 words)
“Bela Kiraly Dies at 97; Led Revolt In Hungary” (1136 words)

Notable outliers are:

Michael Jackson, receiving a 2839 word obituary at the age of 50 (Billy Mays is the other 50 year old). Jack Rose, the youngest recipient of a NYTimes obituary. Along with Michael Jackson, Walter Cronkite received a very long obituary that ran 50% longer than the two next longest obituaries.

Final Project (Shift/) Proposal

1. OverviewAn exploration of the NYTimes obituary to examine what is supposed to be the most notable people to have died on a given day. This very very short daily list is then contrasted against the much larger set of likely mundane, but certainly much more varied nation-wide set of newspaper obituaries. This exercise seeks to both bring attention to the large number of deaths that occur every day and find an alternative snapshot of what the American life is through its daily deceased.
2. DataMy data sets will be the NYTimes Article Search API (searching for “obituary”) + an RSS feeds from the site obituaries101.com located at http://www.big101.com/obituary_search_find_famous_death_notices.php.
3. Design QuestionsMy initial approach is to use scale and variation in type size to underline how small of a snapshot the NYTimes obituary section is of the greater body of obituaries in the United States. I’m not exactly sure if I will be using any graphics as text is a central part of this exploration.
4. Precedents
   • Rupa’s project, Out of Sight, Out of Mind, dealing with visualizing deaths and she was previously mentioning the use of bar graphs to identify spikes in casualties across time.
   • NPR did a radio show called The Art of the Obituary which revealed the behind-the-scenes process of identifying aging members of society of note and prewriting and subsequently updating obituaries. Hearing this story when it aired a number of years ago really piqued my interest in the topic of obituary writing.
   • Almost forgot the most striking infographic I’ve seen that related to death: the visualization of suicides on the Golden Gate Bridge: http://www.sfgate.com/cgi-bin/object/article?f=/c/a/2005/10/30/MNG2NFF7KI1.DTL&m=/c/pictures/2005/10/30/mn_suicide30_loc_tt.gif.

Congrats, You Can Read the New York Times

Midterm Project by Rupa & Bobby.

This project started with a curiosity stemming from the standardized English Language Arts tests taken by all elementary school children in the NYCity public school system. When I was in 3rd grade, the highest percentile of test takers were told that they had a level of reading comprehension that enabled them to read the NYTimes. This experience led to questions:

1. how is reading level measured
2. are nytimes articles truly challenging
3. how does the NYTimes compare to other news publications with comparatively lower reputations
4. how might such a  comparison be visualized

Research indicated that many methods to calculate reading level exist and that most rely upon counting syllables (due to the complexity of polysyllabic words).  Our tests showed that the NYTimes is consistently more difficult to read than Newsday, USA Today, the Wall Street Journal, and the Washington Post.

Midterm Proposal: NYTimes’ Reading Level

1. Overview

Told as a child that the NYTimes was more difficult to read and comprehend than other papers such as the Daily News and Newsday, I now seek to look for complexity within the NYTimes headlnes and then compare those findings against an analogous data set of headlines from more accessible newspapers to see if there is a divide between the NYTimes and it’s less respected brethren.

2. Data

My data sets will be the NYTimes Newswire API + RSS feeds from papers both Local and National, including the USAToday Headline RSS.

3. Design Questions

A major question to tackle is hoe ease of read can be effectively mined from the data. I need to pay attention to studies on reading comprehension and search for indicators of reading sample difficulty. For now, I propose to assign longer headline length and higher range of word lengths within one headline to indicate higher difficulty.

I also need to consider mapping other data, perhaps newspaper section, time of day, or authorship in order to see if there are any other underlying relationships that can be connected to the varying reading levels across newspapers.

Previously, I explored the effectiveness of pie charts and am still interested in their use in effective data visualization, but am also interested in taking the feedback I received and implementing some sort of stacked bar chart visualization.

4. Precedents

Dave Bowker’s project, Designing the News is a major work that deals with visualizing aspects of the Guardian newspaper in unconventional ways. In the project, Bowker produced a series of visualizations of the newspaper over the course of one week. Two of the resulting six pieces dealt directly with visualizing headlines:

http://www.designingthenews.com/wp-content/uploads/2008/04/06_saturday_a1_72.jpg
http://www.designingthenews.com/wp-content/uploads/2008/04/03_wednesday_a1_72.jpg

5. Collaboration

I am planning to partner with Rupa as she has similar interests in comparing the NYTimes headlines to headlines sourced from other places. So far we haven’t yet worked out how our collaboration will come together, but we’ll figure it out soon.

Timeswire Headline Length Visualization

My idea was to pull in the headlines from the NYTimes Newswire API in order to count and visualize the total headline length and relative word lengths of each worth in the individual headlines. I ran into the technical limitation of needing more hours than were available to wrap my head around and make sense of the code that would enable me to visualize my idea. I have experience in using Processing, but it has become apparent that I need to devote more of my free time to becoming more familiar with the language and/or look into developing with an alternate language.

The applet & code are linked below. A look at the commented code will reveal a number of setbacks that I encountered on this project and I hope to address those technical back-end issues prior to launching into the upcoming midterm assignment. Applet at http://a.parsons.edu/~linc184/workaround/hw_applet/

Reading Responses [bobby]

Sorry this is late…

The following is my response to “The Database as System and Cultural Form: Anatomies of Cultural Narratives” by Christiane Paul.

The main focus of Paul’s paper is the idea of Database Aesthetics and how there are a multitude of ways for artists, designers, and programmers to interpret data (in databases) and reorganize them in creative and interesting ways. In designing the front-end visualization, new forms can emerge out of the data and connections and trends may be much more readily be seen. What’s interesting is that while many of the students, including myself, are interested in using data visualization as a means towards the goal of social commentary/illumination of social issues, nearly all of Paul’s examples fall into the realm of data-as-art: searching for interesting patterns and visually stunning structures.

The following is my response to “Data-Ink and Graphic Redesign”, a chapter from Visual Display of Quantitative Information, by Edward Tufte.

I really enjoyed this straightforward look at the topic of how much information is necessary to include in a graphic in relation to the amount of non-information. The balance between the two, he dubs the data-ink ratio. Through the use of several graphs (and various edits of each) he comes very clearly illustrates a number of his suggestions to would be data visualizers. His points are: reduce non-essential and redundant data in order to highlight the data and maximize the data-ink ratio. To this end, he also points out that we all must remember to create revisions and make edits.

How Different Groups Spend Their Day (Inspiration)

How Different Groups Spend Their Day (interactive graph from NYTimes, data from ATUS)

“The American Time Use Survey asks thousands of American residents to recall every minute of a day. Here is how people over age 15 spent their time in 2008.”

Credit goes to a coworker who passed this link on to me a few weeks ago. It displays data compiled by The American Time Use Survey that details the activities of different segments of the US population at any given hour in the average day. By clicking around the graph, one can see via animation the relative differences in time spent between activities amongst different segments of the population.

Alt Visualization: METAL!

(link)