The big news this week was the republican victories in Tuesday’s general elections. Since I work in Arlington Virginia (although admittedly I am a Baltimore native and by no means familiar with local Virginian politics) I thought it would be appropriate to take a closer look at Virginia’s gubernatorial election in which Republican Bob McDonnell won by 17 percentage points. Many people, mainly republicans, are claiming that this race was a reflection of public opinion on the job that President Obama has done thus far. Others say that McDonnell won due to low voter turnout compared to the presidential election a year ago. Some just chalk it up to a weak democratic candidate. No one outside of Virginia seems to know for sure (that is the nature of local politics I guess) so I thought I would use Maker!’s analytical tools to try to test out my own theories.

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.

Popularity: 7% [?]

I came across an article that appeared in the New York Times called “Bleeding Heart Tightwads” by Nicholas D. Kristof. Kristof, a liberal, goes on to say that liberals tend to be stingy when it comes to giving to charity. I found it to be very interesting and also contradictory to what I originally believed. I had always in the past believed liberals to be the group that is extremely generous and more prone to give to charity than Republicans. This is because liberals tend to push for policies that focus on using government spending to increase opportunities for the needy, something that is consistent of a charitable nature. After reading this article and doing some further research I decided to use Finder! and Maker! to display some of my findings on the subject.

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.”

Popularity: 28% [?]

In January, a record 4 million people are expected to gather in Washington, DC to take part in the Inauguration celebration. Hotels near the area sold out nearly the day after Obama was elected. Luckily, where traditional lodging failed, Craigslist saved the day. People all around Washington, DC (and I mean all around, including from places like Baltimore which is nearly 40 miles away) have been so kind as to offer up their couches, rooms, whole houses, and even offices to those in need of a place to stay for the event. That is, if you are willing to sell your first born child!

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!.

Popularity: 14% [?]

(maps made by Emily Sciarillo)

The question of who is allowed to purchase and possess firearms has been debated in state legislatures all across the country. Some want more restrictions and some want fewer restrictions, and every state has its own unique set of rules. Debates rage on and it seems that any amount of restrictions, high or low, will not keep everybody happy. Here, at Fortiusone we see ourselves as an unbiased party that simply wants to present facts. So we thought we would take a look into the heated topic and see if creating more restrictions was for the best, for the worst, or if it even mattered at all.

The first thing we did was create a dataset in Finder! that scored each state’s leniency toward the amount of restrictions put in place when purchasing and/or possessing a firearm. The dataset can be found here:

http://finder.geocommons.com/overlays/7897

We compared firearm restrictions on age, criminal background, and type of weapons across all states in the USA. We gave different point values for the severity of the restriction. Higher numbers were the result of tougher enforcement and lesser values were the result of lesser enforcement. A full rundown of how this point system was systematically determined can be found in the dataset description. The one important value that we obtained was a summation of all these different values for each state that we deemed the State Firearm Restrictive Value. The higher a state’s State Firearm Restrictive Value (the bigger the orange circle on the map) the less lenient the state was in their firearm restrictions. The map is below:

Now that we have this data we decided to pair it up with crime rate data across the country by state. The two areas of crime that we focused on were the amount of firearm related murders per capita by state and burglary rates per 100,000 inhabitants within the state. First we will look at firearm related murders. We decided to use this crime category because it gives us a great sense of the how serious firearm crime is in a state. The dark areas on the map below represent the states that display high rates of firearm related murder per capita.

The link to this dataset can be found at:

http://finder.geocommons.com/overlays/7902

and the map is below paired with the State Firearm Restrictive Values.

Now we will look at burglary rates (per 100,000 inhabitants) within the state paired with the State Firearm Restriction Values the map below. We decided that this would be a good category because it is often said that increases in gun ownership might lead to less burglary. Some on the other hand find this to be false. All in all it is another debatable firearm ownership topic that we can explore.

The link to the dataset can be found at:

http://finder.geocommons.com/overlays/7896

What can we conclude from observing the two sets of crime data with the State Firearm Restrictive Values? There is no show of a strong correlation in either case. When running a correlation formula between the data of firearm murders and restriction values you get a value of .1155888. When running the same correlation between burglary rates and restriction values you get a value of -.0144564. With values so close to zero it is easy to determine that a distinct correlation between the two values does not exist in either case.

Basically low levels of restrictions are found in states where crimes rates are high and also where crime rates are low. You can also find high levels of restrictions found in states where crime rates are both high and low. The results vary greatly. To conclude, it is perhaps wise to say that crime rates are not the sole factor when putting gun restrictions into state legislation.

Popularity: 14% [?]