Did you know that noise complaints are the no. 1 quality-of-life issue for city residents? The folks over at Movity.com, before being acquired by Trulia, decided to tackle this issue as a first step in providing detailed information about life quality of different city districts with the TenderNoise project.

In a partnership with Arup and Stamen Design, Movity contributed and worked on the application for the CityCentered Festival 2010. Using decibel readers at major intersections in the Tenderloin district of San Francisco, they collected the data in a rather rough resolution, but were able to track over 1.6 million data points for a weekend and weekday. The visualization shows the measured noise levels as bubbles on a map in conjunction with an animated time series that shows the absolute values over time.

What I like about the visualization is, intended or not, the exploration of flicker as a attribute to represent information. Areas with extreme variations in noise level seem more “nervous”. While flicker isn’t a precisely readable attribute, it is definitely capable to convey information to the user. Leveraging the digital medium we have more visual features, like direction and velocity of motion, at our disposal (see Perception in Visualization by Christopher G. Healey).

Researchers from the Northeastern University and Harvard University have released the findings of a study about the mood on Twitter over time. So, how does the mood of the United States change during one day?

The emotional measurements were plotted using a combination of cartograms and heat maps that quickly give access to the most important measurements: The amount of Tweets is displayed as the geographical scale and the mood is represented by the color of the county.

A number of interesting trends can be observed in the data. First, overall daily variations can be seen, with the early morning and late evening having the highest level of happy tweets. Second, geographic variations can be observed (second graph), with the west coast showing happier tweets in a pattern that is consistently three hours behind the east coast.

Weekly trends can be observed as well, with weekends happier than weekdays. The peak in the overall tweet mood score is observed on Sunday mornings, and the trough occurs on Thursday evenings.

The dataset consists of over 300 million tweets collected over the time period from Sep 2006 – Aug 2009. The mood of each tweet was inferred using the ANEW wordlist by NIMH Center for Emotion and Attention (CSEA) at the University of Florida.

Twitter continues to be an interesting source of data and the plots at hand achieve to reveal underlying patterns. We need to keep in mind that Twitter’s user base is not representative for all U.S. citizens but with over 190 million users worldwide it seems to give a good indication about what’s going on.

The hard facts are given: A team has 11 players, the ball is round and a match takes 90 minutes. But what happens in between? We’ve compiled a list of applications and visualization that strive to provide insights into the FIFA World Cup 2010.

World Cup 2010 Calendar

The World Cup 2010 Calendar by MARCA.com is an interactive schedule for the tournament. You can access information by national team, group, stage, stadium, city or date. Once you hover over a wedge, June 14th for example, you see all the matches that take place on that day (Go Cameroon!). The visualization does not provide any deeper insight that the end result but the matches are linked to the coverage on MARCA.com.

2010 FIFA World Cup South Africa Game Tracker

Similar to the above mentioned schedule is the Game Tracker by Bristol based digital design studio Positive. It shows the upcoming game dates and results for past matches. The visualization is built in Flash and you rotate the circular schedule with simple drag & drop gestures. Again, not much insights about the matches, players or teams.

FIFA World Cup 2010

VisualSports has created a dedicated page for the World Cup matches. Every match for the group stage can be found in a table sorted by grouping. You can analyze the past matches with a visualization showing all cards, shots and substitutions that happened.

Twitter.com/worldcup

Another dedicated page comes from Twitter: You can “view” past and upcoming matches, which means that you get two parallel Twitter timelines with mentions of the playing teams. Honestly I don’t get much information out of this one.

Total Football 2010

With Total Footbal 2010 by Colm McMullan match analysis comes to the iPhone. For current and past matches the application offers different visualizations to dive into the data. The visualizations show information like the influence of each player, passes, placements of shots, tackles, clearances, fouls and so on. All match data updates in real-time. Of course the usual match stats like goalscorers, substitutions or cards are included as well.

World Cup Data Visualiser

The London based design & development studio Mint Digital comes this concept for an iPad application. It combines the football data from Picklivewith fun and intuitive analysis tools. For example, it allows you to compare cumulatively, or per match, all footballers in the World Cup across a variety of stats like goals, shoots on target, tackles and more. I haven’t found anything about the progress of this but they seem to have a working version of their Hamlock framework without the need of Flash which is apparently a necessity to run in on the iPad. See more images onFlickr.com.

World Cup 2010 Twitter Replay

The Guardian also leverages Twitter data to create the World Cup 2010 Twitter Replay application. It’s basically a timeline based visualization that represents Twitter hashtags as a bubble chart. On the timeline you can see important actions during the game like cards and substitutions.

Live World Cup Match Tracker

The New York Times uses a custom Flash application to support coverage of past matches. It’s a small little widget but filled with important information. A match can be analyzed in different ways to focus on ball posession (bubble chart), passes (lines) and area of play (heatmap). Shots are shown as cicles below the timeline. You can skip through the whole game by switching between notable actions. I think the NYT wins this round in regard of data density (and in my opinion also in regard of visual aesthetics).

I believe there are tons of other visualizations out there — if you come across something interesting, please let me know.

Via Infosthetics & FlowingData

The Google Earth blog recently posted some examples of how well Google Earth can be used as a scientific visualization platform. The examples are posted by Thijs Damsma from the OpenEarth Initiative.

OpenEarth is the open source initiative to archive, host and disseminate Data, Models and Tools for marine & coastal scientist and engineers. It aims to remedy the above-described inefficiencies by providing a project-superseding approach.

It seems like the usage of Google Earth goes way beyond checking for your neighbours garden and with the possibilities fo KML complex data visualizations in a 3D environment are at your fingertips. OpenEarth even provides some tutorials on how to get startet with these types of visualizations.

Do you have any other examples of data visualization in Google Earth? Or are most of the data mash-ups done with Google Maps?

Triptrop NYC is a small mapping application by Jonathan Soma that maps the expected travel time from one point in New York to another using the NY Subway system. Just type in an address and it returns a map with an heatmap-like overlay.

The application uses prerendered images for 128’000 points in NYC plotted on Google Maps. In conjunction with the GoogleBar the user can quickly check for restaurants in 10 minute distance for example. Unfortunately the images don’t rescale thus giving a rather jagged look on higher scale rates.

The data is organized in a table with the years from 1942–2009 as column headers and the months of the year are shown as the rows. The cells are filled with a color scheme ranging from red (high jobless rate) to green (low jobless rate). This gives an easy to understand heatmap-like overview. The interaction model is very basic and only provides a tooltip functionality and some additional filter for recessions. It would have been nice to implement some information with higher granularity. But the overall visual design is clean and easy to read.

View the interactive table on The Wall Street Journal

While I think the heatmap does a fairly good job at giving an overview at a given timestamp it is hard to really grasp varieties on different dates. The playback feature helps in this regard but for deeper analysis some sort of direct comparison would be crucial.

Gapminder.com with it’s “traces” mode comes to mind while thinking of possible ways on how to combine timeline based animation and direct comparison over multiple timestamps. Another way would be to introduce a split-screen solution, which I feel would be a inferior solution in terms of usability. Are there any concepts out there that let’s a user compare different states of a heatmap? I would love to hear your thoughts on this.

The application is a great complement to the actual article and provides an intuitive way to earn a technological overview over the discussed topic using heatmaps, map plotts and 3d animations.