FortiusOne has been involved in a project to map all the World Bank’s projects. Dubbed “Mapping for Results” it is a collaboration between Development Gateway, AidData and the World Bank.  The initial step is to actually map where the projects are taking place. Geocoding is the process of placing information in its geographic place.  When you enter an address into a routing application to get directions the process of placing that address on the map is a type of geocoding.  The process being utilized in Mapping for Results is far more manual than that though.

A team from College of William and Mary and Brigham Young University have been hard doing this geocoding.  They use a variety of sources on the web in an attempt to pinpoint where projects are taking place.   I didn’t think it was possible to be more excited about geocoding than I, but after watching this video I think they might be.  Where FortiusOne comes into this project is the visualization once the projects have been mapped.

Breaking down aid funding by a variety of geographies and projects it gives a clearer picture of what is being work on where.  This can assist in evaluation of the efficacy of various projects and help determine under-served regions.  You can see video of some of the resulting visualization in the video below:

Popularity: 2% [?]

This summer, the FIFA Soccer World Cup will take place for the first time in an African country, South Africa. The Final Draw, the decider of which teams will be grouped together took place on December 4th. For many, this highly anticipated event officially kicks off the World Cup season. I am admittedly not much of a sports fan except for every four years when the geographer and traveler in me goes absolutely mad for the FIFA World Cup. Like many, I watch every game and savor the country to country battles over domination of their group and depending on where the games are taking place stay up late or wake up early to watch the elimination matches (remember 2002 in South Korea/Japan). The Would Cup statistics are perfect for making maps in Maker!. I have uploaded a bunch of different dataset in Finder! and have created some cool maps on World Cup 2010 as well as world cups of the past. This first map shows the world team rankings by points along with the groups chosen on December 4th. Each group contains one of the top rated teams. The other 3 teams in each group are randomly chosen but are kept as geographically separated as possible. Spain, also the European Cup champion, is #1 followed by Brazil and the Netherlands. Click on the map to see it in Maker! Although the next Women’s World Cup is not until 2011, I included a map of the women’s world team rankings by points. The USA is currently the #1 team followed by Germany and Brazil

Click on the map to see it in Maker!

As I mentioned, 2010 will be the first time the World Cup is hosted in an African Country. This next map shows the locations of past World Cups.

Click on the map to see it in Maker!

There have been 18 World Cup Games since 1930. From those games only six countries can call themselves the winners, with Brazil winning five times and Italy winning four including the most recent. Also, of the 193 countries that compete for FIFA only 75 countries have ever had the chance of playing in a Men’s World Cup Game. This next map shows the countries by the number of appearances made in a world cup game. The dots show the countries who have won by the number of wins.

Click on the map to see it in Maker!

Attending the games this summer in South Africa would be a once in a lifetime opportunity. If you are planing a trip for the games, knowing where the stadiums are located could be helpful in deciding on hotels and other details of your trip. This last map shows the locations of the World Cup 2010 stadiums in South Africa by their capacity.

Click on the map to see it in Maker! Enjoy the Maps…and the Games!! 176 days 16 hours 54 minutes and …..44 seconds to go! GOOOOAL!

Popularity: 11% [?]

With the recent passing of Prop 8 in California, I was inspired to do some research into gay marriage laws in the US as well as divorce statistics for straight couples. Originally I had intended to put together an opinionated piece about gay marriage laws and divorce rates, but in my search I was thwarted by a lack of solid information and references. It is exactly this “lack of” information that I find interesting. Why don’t some states report divorce numbers? Why is it so hard to find good religious statistics? Also, is there any readily available data on the rate of divorce for gay couples? Is the process of collecting the data too difficult or is there just not enough interest? Based on what I did find, here are some simple observations.

The United States has one of the highest divorce rates in the world and is also a nation polarized on the issue of gay marriage.

I thought it might be interesting to look at gay marriage laws on a state by state basis, and used the LA Times online as a great resource. Last week they published a fantastic timeline on gay marriage laws by state from January of 2000 to May of 2009. The timeline uses ten different categories of laws from least rights to most rights for gay marriage and animates how a state (and the nation) has changed over time. I took data from the first and last dates on the map, and mapped a difference for each state’s laws on gay marriage using Maker! What I found was that middle America has become less and less gay-friendly and the coastal states have tended to become more gay-friendly. States with negative numbers have become more strict on gay marriage, while those with positive have become more for gay marriage. Those states with zero values have remained the same for those two dates, but that can mean either for or against gay marriage. When you view the map in Geocommons (link published below) click on individual states for their past and current laws and to see whether or not they have changed at all.

The next thing I thought might be interesting was a state by state straight divorce rate map. Unfortunately, this kind of data is very difficult to find in one place. And many states such as California, Minnesota, Indiana, Louisiana, Georgia and Hawaii have stopped reporting their straight divorce statistics. I’m not sure why this is, but it made mapping divorce rates very difficult. I did find some information at the United Nations Statistics Division page and I’ve simply presented each state’s raw number of straight divorces for the year 2007 excluding the states mentioned above. Again, follow the link to the map below to see exact numbers for the states included.

All of these points have inspired me to dig deeper into these issues. If you have good data about any of these topics and would like to create your own maps, please post a comment with your thoughts and use Finder! and Maker! to see if you can find any correlations between gay marriage, divorce, religion, or whatever you think might be interesting. Leaving aside my thoughts on gay marriage and divorce, I think it is safe to say that divorce is a problem in our country and as a nation we are very polarized on the issue of gay marriage. What are your thoughts?

Here are the links to view these data layers:

http://finder.geocommons.com/overlays/13181

http://finder.geocommons.com/overlays/13182

http://maker.geocommons.com/maps/5750

http://maker.geocommons.com/maps/5751

Popularity: 16% [?]

The 2008-2009 NHL Season has been a thrilling one and it continues to be with the start of the playoffs. The game’s popularity has been growing and a rise in attendance figures has been a direct result. The Total NHL Attendance figure was broken this year for the fourth consecutive year. This news made me want to take a closer look at the data.

I first went to espn.com and looked at attendance figures from the 2008-2009 season. After looking over the stats I saw that some teams had regular sellouts and other teams struggled to fill the seats. The map below shows the percentage of seats that were filled throughout the season for each team. (click on the map for a larger view)

Why did some teams sell out every game while others showed poor attendance? I decided to investigate by using Finder! and Maker! to run correlations to determine why a team could or could not get fans in the arena.

The first thing I wanted to correlate was a team’s finishing place in the league and their attendance capacity percentage for the season. This is because a common theme in sports is that fans only go to watch a team if that team is winning. I mean who wants to go see the last place team in the league play.

The correlation shows some interesting results. It appears that the place of your team does not always affect the amount of fans you put in the seats. The correlation between the two factors was only .48 (high correlations are values close to 1 or -1). For example, the Ottawa Senators were able to fill 105% of their seats during the year yet they finished 22nd out of thirty teams in the league. Also, the Carolina Hurricanes who finished 11th in the league out of thirty teams only filled 88.5% of their seats (rated 10th worst in the league).

Now I looked at running some other correlations to see if any other factors resulted in getting people into the seats. Below is what I tried.

- Number of Consecutive Playoff or Non-Playoff Seasons (shows if a team has been continuously successful or unsuccessful)

- Unemployment % for February 2009 (If you’re broke and without a job, you probably won’t be spending your money to go to a hockey game)

- Average Temperature During Hockey Season (Hockey is a sport that is heavily followed in colder climates)

None of the correlations faired much better. Surprisingly Average Temperature During Hockey Season was the closest (-.59) This led me to the conclusion that it is a combination of different factors that determine if a team is able to get people in the seats for their games. Now I took several factors and gave them specific values and combined these to come up with the “The Kev Score”. I am hoping that “The Kev Score” will show how certain factors combined will determine if an NHL team will achieve their maximum attendance capacity.

Here is how I computed “The Kev Score”

Factors:
- Finishing Place (if in 1st place = 30 points, 2nd = 29 points, and so on)
- Temperature (Coldest City = 30 points, 2nd Coldest City = 29 points, and so on)
- Canada Factor (if a Canadian team you get 15 points added to your score)
- USA Hockey IQ Factor – if a USA city is known as a town known for hockey
o Good IQ (10 points added)
o Poor IQ (No points)
- City Population (Highest City Population = 30 points, 2nd Highest City Population – 29 points)

The Formula:
Finishing Place Points + Temperature Points + Canada Factor + Good USA Hockey IQ Factor + City Population Points = “The Kev Score”

The correlation between the Arena Full Capacity Percentage and the “Kev Score” is reasonably high at a score of .81. So is the “Kev Score” a reliable way to predict how to get fans in the seats. I decided to use the formula again but to test it with statistics from the 2007-2008 season. Here is what happened.

At a much lower correlation of .60 it seems that the “Kev Score” does not prove itself to be a strong indicator of fan attendance for the 07-08 season.

Was “The Kev Score” a reliable way to judge if a team would or would not have a strong attendance? Well not really but it worked better than all the other things I tried. See if you are able to discover your own “Kev Score” and help Hockey Team owners around the NHL discover how to bring more fans to their games.

Popularity: 10% [?]