With news of increasing violence and American deaths in Afghanistan lately, I thought maybe areas with more war causalities would have shown their discontent of continued wars in the voting booths. The map below shows the election results by county along with the number of causalities by city from icasualties.org/. While no strong pattern emerges, it seems that some areas with higher causalities voted less for McDonnell. The apparent connection could be due in part to higher populated areas which have more men and women fighting in the wars and who lean more towards the democrats.

Next, I thought maybe areas that have experienced a dramatic increase in unemployment in the past year were more likely to vote republican because of disappointment of the lack of improvement in the economy since Obama took office. To find out, I used the correlation tool in Maker! to see if there is any relationship at the county level between the 12 month change in unemployment from September 2008 to September 2009 and the percentage of votes for McDonnell. You can see in Makers!’s results in the image below that there is no correlation. Ok, I thought, maybe areas that had lower overall unemployment in September 2009 voted republican. Again, I did a correlation using the tool in Maker!. And again, no relationship. Lastly I decided to look at the percentage of voter turnout. If it’s true that small voter turnout can explain why the republicans won then there should be a correlation between the voter turnout and the percentage of votes for McDonnell. Third time is a charm, right? It appears not. Again, no correlation. Well, a failed attempt at explaining the election results has at least provided a good example of how there is nothing like a great mapping tool to disprove bad hypotheses. If you think you know why the republicans won feel free to find data or use some already in Finder! and let Maker! put your theory to the test. Good luck!

Also check out our dashboard on the Virginia Election 2009 to find more great datasets and maps.

In Kristof’s article he says he used the source, The Catalogue of Philanthropy to measure generosity by state. I went to this source and decided to map the inverse of what they call their “Generosity Index Ranking”. The site describes the index as rank of each state’s average adjusted gross income (AAGI) to the rank of each state’s average itemized charitable deductions (AICD). The arithmetical differences between these two rankings are then themselves ranked, resulting in the Generosity Index rank. So to basically sum it up the Generosity Index measures who spends a greater percentage of their income on charitable causes.

I mapped the Catalogue of Philanthropy’s figures for 1996, 2000, and 2004 because these years coincided with Presidential Elections were you would be able to see which states were red (conservative) or blue (liberal). The following combined datasets are mapped below. The orange states represent states that were red (conservative) and the blue states represent states that were blue (liberal). Also, the white circles represent the Inverse Generosity Index Ranking for the state. A larger white circle means a top ranking or that residents of the state contribute a larger percentage of their income to charity. Pay close attention to the average generosity scores at the bottom of each map. (Please click on images for a larger view or go to their Maker! weblink below the picture for an interactive view)

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Average Red State Generosity Index Score = 33.84, Average Blue State Generosity Index Score = 11.89

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Average Red State Generosity Index Score = 32.80, Average Blue State Generosity Index Score = 14.55

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Average Red State Generosity Index Score = 35.37, Average Blue State Generosity Index Score = 19.45

During all three years states that voted republican had higher Inverse Generosity Index Scores as a whole over states that voted democratic according to the average index scores. Just by looking at the maps you can see that the orange colored states (conservative states) have the larger white circles (high rankings) and the blue colored states (liberal states) have the smaller white circles (low rankings). The next map shows states that during all three elections in 96, 00, and 04 voted for the same party versus an average of the state’s Inverse Generosity Index score for those three years. This is to give a good overall average of how very democratic states compare to very republican states when it comes to giving to charity.

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Average Red State Generosity Index Score = 35.35, Average Blue State Generosity Index Score = 12.46

As you can see from the above map the data from past Presidential Elections the truly republican states have higher generosity rankings than the states that have solely voted for democrats. So maybe it is true that republicans are more generous.

Kristof in his article says that liberals often claim these findings are misleading because conservative states have higher religious populations. This causes their charity to go toward building big churches which is not accurate of measuring charity. I decided to take a close look at this accusation.

Below I mapped percentages of state populations that say they practice no religion (Dark Blue = high no religion population, dark orange = low no religion population) from a 2001 study by a group from Graduate Center of the City University of New York. I then compared these figures with the Inverse Generosity Index rankings from 2000. By doing this I figured it would give a look at how states with high religious participation gave to charity and how it compared to how states with low religious populations gave to charity.

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Do liberals have a valid accusation? According to this map it appears so. For the most part it seems that dark orange states, the states that have high religious populations, have large black circles which indicate that they have a higher Inverse Generosity Index. Also most of the dark blue states have small black circles. But there are still a few outliers and when comparing correlations between no religion vs. charity to politics vs. charity we see that there is a stronger correlation between politics vs. charity (see below).

Values closer to -1 or a slope that is almost a straight line, show strong correlations. Therefore we see that low activity in church vs. generosity does not show as strong of a correlation as politics vs. charity. So can liberals really say that their lack of giving is because they are not giving to churches as much as conservatives? Are the facts about religious participation strong enough to discredit the facts on political participation?

So are liberals stingier than conservatives?. The data has suggested that they are. One thing that I am unhappy with is that this data is somewhat dated with the most recent year being in 2004 for philanthropy stats from the Catalogue of Philanthropy. I am anxious to see if the trend has continued into the present and am eager to compare 2008 charity figures with red and blue states from the 2008 Presidential Election.

Overall, I like how Kristof does not see the data as a negative, but a way to encourage more of his fellow liberals to contribute more. He states in his article, “Come on liberals, redeem yourselves, and put your wallets where your hearts are.”

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!.

There seem to be two fundamental problems, of many, worsening our current economic quagmire. 1) The housing bubble pushed home prices to levels most working Americans could not afford and to keep the bubble going the financial community became very creative with mortgages and how the risk associated with them was calculated. The end result was lots of people in houses they could not really afford and very little transparency in the risk this created in the financial markets. There is a lot more to the story but for the sake of brevity we’ll leave it at that. 2) Credit liquidity in the current market has almost ossified causing our collective economic gears to come to a rattling halt. Wall Street freaks…the media freaks…the consumer freaks (no spending)…sales of goods plummet…Wall Street freaks again…media fuels more freaking…rinse and repeat.

To break the cycle it would seem logical that liquidity needs to be injected into the market. A lot of pundits have looked at this being solved by the government buying up the bad assets, giving capital to the banks in return for equity stakes, and several other derivative plans. While all these ideas have their merits and risks the idea I exposed on the plane was slightly different. Back to the core issues – I saw the biggest failing being lack of market transparency and a fundamental mismatch between supply and demand in the housing market. So how could we restore transparency to the market while getting people in homes they can actually afford thus freeing capital for consumer spending and financial investment.

My answer was a foreclosure clearing house. This may be Polly Anna and not feasible, but it made for a fun intellectual exercise. There has been lots of talk around providing bail outs to people whose homes are foreclosing, but even this will be short term and will not solve the fundamental problem that they are in a home they cannot afford. The only real solution is to put these individuals and families into homes they can afford. The easy credit and risk shell game that banks ran has created a basic mismatch of people buying supply with demand they did not really have.

The clearing house is a simple idea of providing a transparent market place where people can trade down to houses they can afford and have new loans guaranteed to do so. The loans could be guaranteed by the government but competed for by the banks. Banks that already have the mortgages on existing properties could have the choice of refinancing the house so the owner could afford the payments (that would be their own risk calculation) or entering the home into the clearing house. Also the home owner could have the choice to enter their home into the clearing house if they would like to trade down voluntarily.

The clearing house itself could run like many of the existing home real estate market places matching buyers and sellers (Zillow, Trullia. RedFin etc.). In fact the government could probably contract with one of the sites to run the technology side of the clearing house at a reasonable cost. Once a person’s home was identified for purchase they would then be free to look for a new home in the clearinghouse they could afford. The government backing would allow loans to be made so the individual, now free of the foreclosed home, could buy a new home they could afford. Banks would still compete to provide the best rate and terms to new owner, but the risk would all be transparent to the government since they would be providing financial backing and to the owners so they were not mislead into buying more house than they could afford (again).

In theory this should introduce liquidity back into the market and with a little time put liquidity back into the consumer market since the majority of a person’s paycheck would no longer be going to a mortgage. The market would be transparent again but not run or partially owned by the government. I would argue that it was not capitalism or the market economy that broke during this financial crisis, but a loss of transparency and a resulting hiding of risk. In fixing the crisis the government’s role should be ensuring transparency in the market place so that it can function effectively. My idea is most likely off the deep end, but I do hope government action is centered around restoring transparency and restoring liquidity to the market. If you were Sec. Paulson for a day what would you do with $700 billion? There are no shortage of smart people around the globe. Can we crowdsource an answer?

The following maps have been created in Maker to reveal pertinent election coverage and data that my fellow data colleagues and I thought would be helpful going into the big election tonight.

This map displays early voting data for selected states from yesterday:

Pay close attention to Virginia—a highly contested swing state —because if Barack Obama can win in the former Old Confederacy capital of Richmond then the odds of him winning the election will be in his favor.

The following is a map of active registered voters in Virginia:

Here is the latest polling data that shows Obama’s lead vs. McCain’s:

Here is a link to the data set in Finder: http://finder.geocommons.com/overlays/5911

And a link to the map in Maker: http://maker.geocommons.com/maps/1311?page=

The image below shows the candidates’ lead in the polls from the end of August till now. It is interesting to watch some battleground states, such as Montana and Pennsylvania, become solidified while other contested states such as Ohio, North Carolina, and Florida change back and forth between candidates.

Check out this interactive map showing the latest changes in U.S. Election polls by state. The date on each map represents the date that the polls were updated and not necessarily the date of the polls for each state.

To see how this election map compares to 2004, here is a map of the state won by Bush and Kerry. You can see that many of the states that bush won are either contested or are supporting Obama in this election.

Also it’s not necessarily the number of states won but number of electoral votes. This map shows the states according to their electoral votes. Many of the contested states are the ones with more votes at stake.

It is important to remember, when looking at polls, that they are often inaccurate and sometimes even bias. Many times, misleading polls actually have the potential to sway the popular opinion of a candidate and can have an unfair effect on the outcome of an election.

The poll maps were made from datasets in Finder! collected from USA Election Polls, who collect the latest state by state poll results and post them on their website.

Those datasets can be found at the following links.

• October 20th Election Polls
• October 14th Election Polls
• October 2nd Election Polls
• August 28th Election Polls

The dataset for the election of 2004 can be found here and the electoral votes data can be found in any of the poll datasets. The map of the polls can be found here, the map of the 2004 results can be found here and the electoral votes map here.

States are polled by different polling companies and polls take place over different time periods but are updated when the newest ones are released. Battleground states are usually the most frequently polled.

Polling sources include Rasmussen Reports, Survey USA, NBC Mason Dixon, Insider Advantage, American Research Group and more.