Dataset of the Day: Tracking the Barefoot Bandit
July 13th, 2010by Kevin Burke
I have found the recent story about the “Barefoot Bandit” to be quite fascinating. It is incredible how someone could elude authorities for so long in this day and age.
I came across an interesting graphic about the story created by Matthew Bambach of the Seattle Times. It is a map of criminal incidents that have been tied together to Colton Harris-Moore aka The Barefoot Bandit. I decided to make a similar map of the incidents, but decided to incorporate the temporal animation feature that GeoCommons provides. Below is the map I created in Maker!:
By clicking the green clock in the layers box in the upper right hand side of the page you can open up a temporal timeline. This timeline allows you to see a temporal animation of the crimes as they took place across the country. You are also able to see the periods when criminal activity was slow or very active. Overall, the temporal animation is able to give you a better understanding of the Barefoot Bandit’s activity compared to a map with static points.
Popularity: 5% [?]
Dataset of the Day: Bad Drivers, From State to State
June 18th, 2010by Kevin Burke
Have you ever traveled out of the state you live in and found yourself saying, “Wow, people in this state are terrible at driving.” Now you can see if your claim was appropriate after looking at the GMAC Insurance National Drivers Test.
GMAC Insurance has been conducting an annual survey where respondents take a driving test that contains questions from DMV tests across the country. Below is a map of the average scores from 2010 along with their inverse ranking among the 50 states and the District of Columbia.
From the map you can see that states in the darker orange color range had the highest scores and states with the lighter orange colors scored lower on the test. On the 100 point scoring scale the highest state score was Kansas with 82.3 as their average. The lowest scoring state was New York with a score of 70.0.
There was also a second part to the survey. This part surveyed drivers on the types of distracting behavior that they took part in while driving. These distracting actions include applying makeup, changing clothes, eating, talking on a cell phone, and texting on a cell phone while driving. Below is a map of the percentage of respondents per state that responded to participating in these distracting behaviors.
The above data is all very interesting and I wondered to myself what might cause the bad driving statistics? I decided to then correlate the average scores from the GMAC Test with three types of data: 1. Max State Speed Limits by State (to see if fast driving correlated to bad driving) 2. % of Deficient Bridges by State (see if poor road conditions correlate to bad driving) 3. Population Density by State (to see if congestion correlates to bad driving). These are not perfect indicators, but I thought it might be fun to see of any of these numbers might correlate strongy. Below are the maps:
The correlations are interesting:

We see that the max speed limit vs. the average scores had a low correlation of .39. So it is probably safe to say that slow max speed limits or high max speed limits do not deter people from being bad or good drivers. Bridge conditions had a slightly stronger correlation at -.49. This is a bit stronger and may hold some weight for arguments sake. Then the last correlation of population density we see as the strongest at -.56. Also not extremely strong but may be something to consider when deciding why people are bad drivers in certain states.
I found the data from GMAC Insurance to be rather interesting and had fun looking at my state and other states that I have traveled through. See what you think of the results and see if you can see why drivers from Kansas score better than drivers from New Jersey.
Popularity: 10% [?]
Dataset of the Day: Maker! Plays Cupid
February 12th, 2010by Emily Sciarillo
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Valentine’s day is this Saturday so I thought I’d made a few maps in honor of this lovers holiday. Since one thing lovers do is travel, I made a map of the 50 most romantic places in the world…at least according to Travel + Leisure in 2005. Each icon on the map contains the name of a hotel recommended by the magazine for that place.
Many holidays are exclusive… christmas is only for christians, mother’s day is only for mothers, and Valentine’s day is only for happily paired couples, sometimes the secretly admired and elementary school children who exchange little cut-out cards and candy to all their classmates. So I thought I would help those who won’t be celebrating this year and let Maker! have a chance at playing Cupid. Italy has the reputation as the as being the most romantic country in the world, so I made some maps showing where in Italy one would have the best chances of meeting single men or women.
According to these maps, if you are planing a trip to il bel paese in the hopes of being moonstruck, head to the island of Sardinia or the very top of the boot to a region called Trentino-Alto Adige and avoid much of the middle of the peninsula where all those pesky couples live. |
Popularity: 11% [?]
Dataset of the Day: Mapping the State of the Union
February 4th, 2010by Emily Sciarillo
Maps by Emily Sciarillo and William Benjamin
Last week President Obama presented the Congress and the American people with his examination of the state of the union after his first year in office. He outlined his achievements as well as some of his failures in the past year and presented his plans for the future. We thought we would make maps highlighting some of his major points in the speech. Some maps we made with datasets that are regularly updated in Finder! such as unemployment. For others we created new datasets and uploaded them into the database.
Recognizing that the economy is the one issue foremost in American’s minds, he focused much of his speech on the subject. Job creation and small business growth were major factors in his plan for improving the economy, citing the Stimulus Package as the engine for both.
The first map shows how the unemployment has changed in the past year. The green counties show areas where unemployment rates decreased since last November and the purple counties show areas with increasing unemployment.
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It is no surprise that most areas of the US have seen their unemployment rates continue to increase but to put that into perspective, it is helpful look at the same scale for the 12 months change during the last year of the Bush administration (the next map). During Bush’s last year the unemployment also rose in much of the country however it appears that during Obama’s first year, more counties increased dramatically (by 4% or more) and less counties improved their unemployment rates at all.
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