We at FortiusOne have been busy monitoring the current swine flu outbreak by gathering public data to create datasets in Finder! and maps in Maker!, and we’ve even made a Swine Flu Dashboard. The outbreak is gathering a lot of attention and is now a common topic of discussion.

Below is what we have been able to compile thus far. We are working on creating more data on the topic to help educate the public on the latest happenings of the outbreak. All datasets that are associated with the content have been tagged “swineflu”. Simply search for ’swineflu’ in the search bar to view the content in Finder! and tag any newly uploaded data on Finder! with ’swineflu’ to allow others to see swine flu data that you have uploaded. If you have any data that would be relevant please post the data set to Finder, and we will update the list with the latest data. If you need help formatting or uploading your data let us know and we can help.

Below is a new mashup we have put together that mashes confirmed swine flu cases in the USA by State as of today, 4.30.09 and school closings across the country due to swine flu.

Swine Flu Data Sets:

US Human Cases of Swine Flu Infection, USA by State, as of 4.29.2009 at 11:00 am et
US Human Cases of Swine Flu Infection, USA by State, as of 4.30.2009 at 10:30 am et
Swine Flu Measures and Cases by Country, World, 4.27.2009
H1N1 Swine Flu, Global, 2009
air-travel by airport of origin between US and Mexico, April 2008
Confirmed Swine Flu Cases Worldwide by Country
California School Closings due to Swine Flu, April 2009
Texas School Closings due to Swine Flu, April 2009
School Closings in NY, MI, OH, AZ due to Swine Flu, April 2009

Popularity: 13% [?]

There has been non-stop talk about Obamas first 100 days in office, and its impact on his presidency. As the election was coming to an end economic policy was at the forefront of discussion. So, when Barack Obama took office we launched ‘The Obamameter’, which was our economic dashboard to follow the impact that the Obama administration was having on the US economy. Other websites have followed his progress on related topics, such as this one on campaign promises.

Now that his first 100 days are up, we have updated the dashboard with the latest data on unemployment, home foreclosures, and other indicators of economic health. For foreclosures and unemployment we’ve created maps showing what locations have improved and which have gotten worse during the last 100 days (roughly). There has been improvement in foreclosures in some regions, but unemployment has gotten worse across the board. We’ve also included a new selection called “100 days” that tracks jobs that have been created by Obama policies to date. We’ll continue to track all the indicators and make the data publicly available.

Click Here to Visit our Obamameter!

Popularity: 7% [?]

Health officials from across the world are currently monitoring swine flu outbreaks that are causing quite the international health scare. Mexico, which appears to be the source of the outbreak, has been hit hard with over 80 deaths and several hundreds hospitalized. Now countries around the world are preparing to fight off any possible swine flu epidemic.

We decided to look at the data ourselves and see if we could use Finder! and Maker! to show areas around the world that have been affected and areas that might be prone to the outbreak.

The map below shows areas that have been affected along with an interactive timeline. (click on the map and hit the timeline icon, little clock in the top right corner, to view the interactive timeline). Map updated with layer of ‘Possible Cases of Swine Influenza’ Tuesday, April 28th, 2009.

This map shows what certain countries around the world are doing to avoid the outbreak to spread throughout their country and the number of swine flu cases they have encountered. (link to the map in maker! is below the image)

http://maker.geocommons.com/maps/4880

This map shows the number of air traffic passengers between the US and Mexico during April of 2008. This map is important because several countries are finding that travelers to Mexico are returning back to their home country with possible swine flu symptoms.

http://maker.geocommons.com/maps/4884

How should we make sure that we do not catch the swine flu? It is easy to avoid if you take the right precautions. First, you will not get swine flu from consuming pork that is properly prepared. Swine Flu is a more prone to spread just like the spread of influenza itself. So to avoid this current outbreak be sure to wash your hands, avoid airports, and don’t plan on traveling to Mexico anytime soon.

***UPDATE***

We have updated and organized our Swine Flu Maps to a Dashboard which we hope will help keep the public more informed and up-to-date with the spread of the Swine Flu. Click Here to Visit the Swine Flu Dashboard

we’ve also added a map showing the diffusion of the disease via air traffic throughout the USA and Mexico, shown here. more updates to come.

Popularity: 44% [?]

The 2008-2009 NHL Season has been a thrilling one and it continues to be with the start of the playoffs. The game’s popularity has been growing and a rise in attendance figures has been a direct result. The Total NHL Attendance figure was broken this year for the fourth consecutive year. This news made me want to take a closer look at the data.

I first went to espn.com and looked at attendance figures from the 2008-2009 season. After looking over the stats I saw that some teams had regular sellouts and other teams struggled to fill the seats. The map below shows the percentage of seats that were filled throughout the season for each team. (click on the map for a larger view)

Why did some teams sell out every game while others showed poor attendance? I decided to investigate by using Finder! and Maker! to run correlations to determine why a team could or could not get fans in the arena.

The first thing I wanted to correlate was a team’s finishing place in the league and their attendance capacity percentage for the season. This is because a common theme in sports is that fans only go to watch a team if that team is winning. I mean who wants to go see the last place team in the league play.

The correlation shows some interesting results. It appears that the place of your team does not always affect the amount of fans you put in the seats. The correlation between the two factors was only .48 (high correlations are values close to 1 or -1). For example, the Ottawa Senators were able to fill 105% of their seats during the year yet they finished 22nd out of thirty teams in the league. Also, the Carolina Hurricanes who finished 11th in the league out of thirty teams only filled 88.5% of their seats (rated 10th worst in the league).

Now I looked at running some other correlations to see if any other factors resulted in getting people into the seats. Below is what I tried.

- Number of Consecutive Playoff or Non-Playoff Seasons (shows if a team has been continuously successful or unsuccessful)

- Unemployment % for February 2009 (If you’re broke and without a job, you probably won’t be spending your money to go to a hockey game)

- Average Temperature During Hockey Season (Hockey is a sport that is heavily followed in colder climates)

None of the correlations faired much better. Surprisingly Average Temperature During Hockey Season was the closest (-.59) This led me to the conclusion that it is a combination of different factors that determine if a team is able to get people in the seats for their games. Now I took several factors and gave them specific values and combined these to come up with the “The Kev Score”. I am hoping that “The Kev Score” will show how certain factors combined will determine if an NHL team will achieve their maximum attendance capacity.

Here is how I computed “The Kev Score”

Factors:
- Finishing Place (if in 1st place = 30 points, 2nd = 29 points, and so on)
- Temperature (Coldest City = 30 points, 2nd Coldest City = 29 points, and so on)
- Canada Factor (if a Canadian team you get 15 points added to your score)
- USA Hockey IQ Factor - if a USA city is known as a town known for hockey
o Good IQ (10 points added)
o Poor IQ (No points)
- City Population (Highest City Population = 30 points, 2nd Highest City Population - 29 points)

The Formula:
Finishing Place Points + Temperature Points + Canada Factor + Good USA Hockey IQ Factor + City Population Points = “The Kev Score”

The correlation between the Arena Full Capacity Percentage and the “Kev Score” is reasonably high at a score of .81. So is the “Kev Score” a reliable way to predict how to get fans in the seats. I decided to use the formula again but to test it with statistics from the 2007-2008 season. Here is what happened.

At a much lower correlation of .60 it seems that the “Kev Score” does not prove itself to be a strong indicator of fan attendance for the 07-08 season.

Was “The Kev Score” a reliable way to judge if a team would or would not have a strong attendance? Well not really but it worked better than all the other things I tried. See if you are able to discover your own “Kev Score” and help Hockey Team owners around the NHL discover how to bring more fans to their games.

Popularity: 10% [?]