Popcorn & Politics

“Popcorn & Politics” — data viz of movies, their audience and their impact on the 2008 US Presidential Race

We Read, We Tweet

“We Read, We Tweet” geographically visualizes Tweets about New York Times articles. Each line connects the location of a tweet to the contextual location of the article it references. The lines are generated based on the sequence in which the tweets occurred.

Personal Blog Writeup

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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.

Tweetcatcha

TweetCatcha seeks to uncover the organic nature of news as it travels through Twitter over time, by examining the movement of NY Times articles through Twitter.

Nick Hardeman + Bruce Drummond.

Presentation

Demo

Now&Then : Exploring the mutual influence of popularity in music industry and news media

Now&Then : Exploring the mutual influence of popularity in music industry and news media
by Yoon and Seung

Detail description, screen shots and Demo page Link

Visualizar ‘09

Some lovely visualizations coming out of Visualizar ‘09. Below is a screenshot from “New Political Interfaces”…a look into what politicians vs. news outlets are talking about:

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.

Engadget is Biting Our Style

Engadget recently unveiled their site redesign, and I missed this early on, but it seems to be a new thing they’ve added -

Clearly they must have seen the brilliant work Steve and I are doing and decided to jump on board. Check out the link to see it at work, pretty simple but effective visualization they’ve got going on.

[ Engadget ]

Final Proposal: Justin

1. Overview

My final project will explore the relationship between the geographical location of Twitter users and the New York Times articles they tweet about. I’m interested in seeing (geographically) where the interest of Twitter users lie on a daily, monthly, and (possibly) yearly basis. I also plan to implement filters, allowing users to explore where New York Times article topics are being talked about most, and the distribution of tweets about New York Times articles by section.

2. Data

• NYTimes Articles API
• BackTweets API
• Twitter API
• Google Maps API

3. Design Questions

• What does the distribution of Twitter user’s interests about various topics, locations, and sections from the New York Times look like visually?
• Do current issues in the news effect where Twitter users decide to tweet about?
• Do patterns emerge based on country/region, or are the Tweet/Articles relationships random?
• Are there unseen political/economic/social relationships between countries/regions that are hidden in the data?

4. Prior Art / Precedents

Flight Patterns by Aaron Koblin

This visualization elegantly maps air traffic patterns. Some of the images in this series show incredibly intricate networks that are formed by air traffic, as well as the locations of the largest airports.

Just Landed by Jer Thorp

Jer Thorp’s processing based visualization shows the locations of twitter users and the places that they fly to, cleverly scraped based on the two tweeted words “just landed”. One of the most compelling aspects of this piece is the 3d translation of data, allowing for an exploration into the intricacies of the paths.

Final Proposal: Laura, Andrea and Thai

Here is a link to the proposal: Powerpoint Proposal