Fear of financial meltdown was the reason given then for the taxpayer paid bailout of AIG, that would eventually rise to more than $160 billion. However what was not clear then, in what way AIG bailout would save the rest of the world. Even as some in the media did expose the role played by the independently operating but wholly owned subsidiary AIGFP and its unit in London headed by Mr. Cassano, under the name of innovative financial products and services was essentially gambling with AIG investors money. And yet it was not clear who were the partners in this gambling scheme who would ultimately get the counterparty claims.

Both AIG and the Feds hemmed and hawed around to release the names of these counterparties under the legalistic language of confidentiality agreements. And it would have continued that way till the AIG bonus-babies scandal broke last week, forcing AIG to issue the press release that gave partial list of trading partners/counter parties.

Below are the maps that show locations of financial institutions who were paid by AIG in the counterparty claims for the complex financial transactions such as Credit Default Swaps (CDS), repurchase of mortgage-backed securities and security lending obligations.

From these maps it would appear that the problem is limited to Western Europe and the U.S. And yet nearly $22 billion was paid to other counter parties, who probably are scattered all across the globe. The partial list of Western European institutions and those in the U.S. reads like who’s who in the financial world. A cross check with Madoff’s clients and TARP list reveals that many of these are the same players who lost billions in Madoff’s ponzi scheme and are also getting TARP money and its quite likely that these same set of players will show up for TARP-II, the TALF!

Talk about small-world! In the name of spreading risk, they have collectively managed to plunge the U.S. and rest of the world into the worst financial crisis since the Great Depression.

Your tax $ at work: AIG’s payment to financial institutions for Credit Default Swaps (CDS)

View the map in Maker! here

Your tax $ at work: AIG’s payment to financial institutions for Mortgage-backed securities

View the map in Maker! here

Your tax $ at work: AIG’s payment to financial institutions for Security lending obligations

View the map in Maker! here

Explore interactive map on the Maker!

Maker.maker_host=’http://maker.news.geocommons.com’;Maker.finder_host=’http://finder.news.geocommons.com’;Maker.core_host=’http://core.news.geocommons.com’;
Maker.load_map(“maker_map_55″, “55″);

Considering that I was a rower in college, I was pleased to find this data. I was particularly interested in seeing what it would look like if I mapped out the college universities that have rowing programs and then by using proportion symbols, I could see which colleges had the biggest revenues. The following map displays revenues of collegiate rowing teams for both male and female programs combined in 2007:

(Click Finder! to view the data set)

Now to give you an idea of what each college rowing program revenue looks like by gender, the following map is broken down by female revenue and male revenue using proportion symbols to show the amount of revenue comparatively.

(Click map or Maker! to view map)

The states that are a very light cream color (Oregon, Montana, Delaware, New Hampshire) are the states that have no sales/general tax. The darker the state the higher the sales tax rate is in that state.

Now my next question is this. If I am to go to one of these states to shop will I really end up saving more money? I may not be spending money for a sales tax but I certainly will be spending more money on gas to travel the extra distance. I will set up a hypothetical situation using Finder! and Maker! to see what my answer will be.

Let’s say I live in the lovely state of Washington in the city called Castle Rock. In Washington the sales tax is at a rate of 6.5%. Next door to me is my neighbor Oregon that has a 0% sales tax. Now on Finder! I can load major shopping centers that are around me in my area. The map below shows that I have two major shopping centers right by me that are relatively close off of Interstate 5, one in Centralia, WA (Centralia Shopping Center, 34.4 miles away) and the other in Portland, OR (Jantzen Beach SuperCenter, 50.4 miles). These will be the two places that I will compare and the map is below of the two with Castle Rock right in the middle. The map is shown below:

Now let’s do some math. My holiday shopping expenses look like this:

Wife = $70, Mom = $60, Dad = $60, Sisters = $120 = = Total of $310 on gifts

In Washington, with shopping tax this equals 310 x 6.5% tax = 20.15, 310 + 20.15 = $330.15. So the difference between the two states is $20.15.

Now let’s look at gas expenses:

Let’s say gas in Castle Rock is $2.00 a gallon and my car averages a rate of 25 mpg. If my round trip from Castle Rock to Centralia is 68.8 and my trip from Portland and back is 100.8 miles, then my gas costs will look like this.

Castle Rock to Centralia: 68.8/25 = 2.75 g x $2 = $5.50

Castle Rock to Portland: 100.8/25 = 4.03 g x $2 = $8.06

By going to Centralia I will end up saving 8.06 – 5.50 = $2.56

Now as we put these two savings figures together we see that overall our trip to Portland would be a wiser choice. You will spend more money on fuel ($2.56), but you will save much more on your shopping expenses ($20.15). Together it will provide us with a savings of $17.59.

I would like to mention that this is very hypothetical. Often, other circumstances (county taxes, municipal taxes, toll roads encountered, different mpg rates on the trips, and many others) may enter into the equation and change figures. All in all this might be a solution to save money, so create your own hypotheticals using Finder! and Maker! and see if it will help. Below are links to Finder! datasets that show major shopping centers (malls, outlet malls) in a few 0% sales tax states. Happy Holidays and good luck shopping!

• Oregon
• Delaware
• New Hampshire

Click Finder! to view the dataset.

After viewing this above map, I was interested in finding out what the BCS top 25 college football team rankings and their head coaches were getting paid – comparatively to the other head coaches throughout the US. The map below shows the top 25 college football teams with orange proportion symbols and reveal, for the most part, that most of the colleges with highly paid coaches are universities that are succeeding at the highest level. The University of Texas, University of Florida, and University of Alabama show particularly high salaries.

Click Finder! to view the dataset

Prices for a room are ranging from $50 per night to above $4,000. It is quite common to see rooms rented for an average of $2,000 per night. In fact hundreds of DC entrepreneurs a day are jumping for the opportunity to make their month’s rent in one night of sleeping on a friend’s couch so they can rent their apartment. While the prices are often unbelievable, many offers include breakfast, a ride to the metro, and even babysitting.

We thought it would be interesting to see where these people’s room/homes were, if they were actually located anywhere near to the inauguration site (or DC for that matter) and how much they were charging based on their location.

We took a 3 day sample of ads from Craigslist, geo-coded them and then added some attributes based on prices and amenities. You can find this dataset in Finder!.

This first map shows the entire DC metro area and beyond to demonstrate the distribution of rooms for rent.

The next two maps show the DC area with rooms based on price per room, per night, as well as metro stops. It seems like there is very little relationship between the location of the room and the price. Go figure!

So if you are still looking for a place to stay, or if you want to check out how much you can get away with charging for your floor and a sleeping bag, check out this dataset in Maker!.

If you are wondering where most of our corn is going, then here are a couple of the largest products of corn:

Feed= 333.2 billion pounds

Ethanol= 179.2 billion pounds

High fructose corn syrup= 28 billion pounds

Sweet corn (ears, canned, and frozen)= 5.8 billion pounds

According to the CDC, one in five U.S. adults are current smokers and every year 443,000 people in the country die prematurely due to smoking and exposure to tobacco smoke. Also, between 2000-2004, 269,655 males and 173,940 females died every year as a result of smoking. The leading causes of smoking-attributable deaths were lung cancer, ischemic heart disease, and chronic obstructive pulmonary disease. Also, an average of 776 infants died every year as a result of smoking during pregnancy. (MMWR, 11/14/08, Vol.57, No.45)

In 2005, chronic obstructive pulmonary disease (COPD) was the underlining cause of one in every 20 deaths in the U.S. and smoking can be attributed to 75% of all COPD deaths. Also, additional coasts for health-care for each COPD patient are $6,000 per year. CDC data by state for deaths from COPD, for adults aged 25 and older are available in Finder!. The following map shows the death rates of males and females at the same scale. While the female death rate is much lower than the male death rate, the total number of deaths was higher for women than for men. (MMWR, 11/14/08, Vol.57, No.45)

In the next map, the death rate of Males is shown along with Finder’s! dataset on smoking bans by state in the U.S. in 2008 (the smaller the circle, the more extensive the ban).

The six states with the highest deaths rates in 2005 have no statewide smoking bans as of September 2008 with the exception of Vermont. Further, the majority of states with the most extensive smoking bans are states with the lowest deaths rates. This reflects a disconnect between a public health need for smoking ban, and actual legislation.

To see the map of death rates by gender, click here and for the map on smoking bans click here.

More datasets related to smoking:
Lung cancer incidence and death rates by gender, USA by state, 2001-2005
NYC Community Health Survey 2006 – NYC Dept. Health and Mental Hygiene
CDC, Behavioral Risk Factor Surveillance System (MMSAs), USA, 2007