Recently the United Nations Statistics Division released UNdata a catalog of a variety of UN data. In the about page it is described as “This database service is part of a project launched by UNSD in 2005, called “Statistics as a Public Good”, whose objectives are to provide free access to global statistics, to educate users about the importance of statistics for evidence-based policy and decision-making and to assist National Statistical Offices of Member Countries to strengthen their data dissemination capabilities.” Clearly meeting “An Open Data Litmus Test” there is a download button. As soon as I discovered UNdata I began searching for data and using it within GeoCommons.

First I selected a dataset I was interested in. In this example I picked “Years of Life Lost to Communicable Diseases (%).” I downloaded the data as a CSV and then uploaded it using Finder. Since the data did not have geographic boundaries I utilized GeoJoin.  First I selected World Boundaries and used the name column to join the UNdata to the boundary dataset.

Next I selected the columns I wanted to join by selecting “country or area” in my dataset and “Name” in the country boundaries.

Only 163 of 191 features matched, but I decided it was okay to continue on and make a map.  I first added metadata and then clicked the “Make a Map” link. Below is my final result. If you decide to upload data yourself or make a map please tag is “UNdata” so we can aggregate them together.

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Popularity: 5% [?]

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!:

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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% [?]

Today I’m off to State of the Map in Girona, Spain. State of the Map or SotM is the annual OpenStreetMap conference. SotM is a mix of the businesses surrounding OpenStreetMap as well as the community. The three day conference encompasses three days, one business day and two community days.

I’ll be there speaking about some of the work we’ve been doing related to open data as well as specifically OpenStreetMap. Friday is the business day at SotM this year, I’ll be speaking in the “OSM: We’re in Business” track about our work in on open data with different levels of government. The title of the talk is “From Arkansas to Afghanistan – Open data sharing and collaboration for better engagement.” Sunday there is a humanitarian track, which includes a talk from Nicolas Chavent of the Humanitarian OpenStreetMap Team on our work in Haiti. Afterwards I’m speaking about running OpenStreetMap with GeoCommons offline for humanitarian response. Even now six months after the earthquake Internet access varies by location in Haiti. There is a need for low and no bandwidth solutions for effective data creation and sharing.

In addition to talks I’m excited to meet people from the OSM community I’ve only previous known online. A diverse group spread out all over the world, State of the Map is the main opportunity to get many people together in person. There are a variety of “Birds of a Feather” sessions including one for OpenAerialMap and the first in person meeting of the OpenStreetMap Strategy Working Group. I’ll be participating in these somewhat “serious” events, but I’m also looking forward to more silly ones. On Saturday night there is a Concert Contest. Since I haven’t packed my bassoon I’m going to bow out of playing “Ode to a Duplicate Node” and cheer on the contestants. I’m sure it will be as good as “Map of the World as We Know It. (And I See Signs.)”

Popularity: 4% [?]

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.

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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.

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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:

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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% [?]