We worked with the World Bank starting about a year ago to launch Mapping for Results, a revolutionary initiative to geolocate and openly share every single location the World Bank was supporting activities. This past spring they launched with the 79 IDA countries including project activity locations and social indicators.

Today, they are releasing data for nearly their entire portfolio covering 136 countries, including the newest country of South Sudan where the World Bank has project activities but no reported financing yet. The data updates daily from the World Bank’s project API and is easily accessible through the download links on each page.

The World Bank are also leveraging the open-source Acetate maps to include terrain hillshading and placename labels to provide better context and meaning to the projects and activities in assessing their impact. See Kenya and Afghanistan.

What was particularly exciting about this release was that we were not involved at all in the development and updating of the newly released country data. Through the easy to use GeoIQ platform, the World Bank team was able to indepedently manage, visualize, and publish their own maps. Data and Mapping tools are made to be used by the domain and development experts, not by technologists and integrators just to share new data. The easier it is to share and create compelling and informative analysis the more timely and more effective that analysis will be.

There are a number of upcoming additions to Mapping for Results that we’ll be working with the World Bank team in adding some new capabilities. Now that the project has been gathering data they will be able to visualize changes over time in order to better share where development efforts are effective – and where they could be better. Enjoy the livestream and join the conversation.

Still not impressed? Check out Solar Panel Installations map with over 100,000 points all rendered on the client!

In addition to points, we were able to push the render bounds polygonal datasets. The following map features over 3,000 individually colored polygons:

Another new feature is the ability to select groups of intersecting geometries with a single click. This functionality comes in handy when you have several overlapping data layers and need to have access to the data stack.

Whenever you work with a complex geometry or a geometry group it is often difficult to see the bounds of the group. To address this issue we implemented “selected geometry outline” which highlights the bounds of the selected object or the group. Take a look:

The real power of GeoCommons 2.0 comes from the combination of data processing, analysis, styling and visualization and we can’t wait to see all the beautiful maps you are going to create!

Stay tuned for more GeoCommons 2.0 updates and announcements.

In Washington, DC there is a lot of data provided by the District government through their data catalog. You can download all the 311 requests from 2010 for example, there is even an API for 311 requests so developers can build additional 311 tools. In downloading the requests from 2010 I noticed that there is a field titled “SERVICEORDERSTATUS” and the values are Open/Closed/Overdue. I became interested in looking geographically at where the overdue tickets were versus the total number. I decided to focus on abandoned vehicles for my analysis. I created a subset spreadsheet of abandoned vehicle requests and then a subset of those requests that were specifically overdue. I then uploaded those into GeoCommons as datasets. The full abandoned vehicles 311 request dataset is available here and the specifically overdue ones are here. Once these were uploaded I aggregated the data by Ward, this way I could see where the most 311 requests were coming from as well as the most that were overdue. To further my analysis I calculated the percentage of overdue requests out of the total requests. The resulting map shows significantly more overdue requests by percentage in Ward 7 and Ward 8. This allows me to start asking further questions, if I actually was a journalist maybe I would approach the DC government with this information and see what they said. Perhaps I would interview residents in those Wards for further information as well. Finally when I’ve developed the full story I can include the map I’ve used for my analysis with my resulting story.

I include the map by clicking on the “Details” button.

Next I click the “Embed this map in your website” button, which brings up the map embed code. I can now copy and paste this code into my news site’s content management system.

Many news organizations are already using GeoCommons to help enhance their stories. Ones we’ve noticed have included:

LA Times: California’s Unemployment Rate is Second Highest in the Nation

Calgary Herald: Explore Dog Data in your Calgary Neighbourhood

Huffington Post: How The Economic Impact of the Oil Spill Is Spreading Beyond the Gulf

Atlantic Monthly: WikiLeaks Data on Afghanistan Deaths Visualized

The Roanoke Times: Roanoke’s Population Expands Over Decade

The Vancouver Sun: Worst Intersections: Vancouver

ABC World News Blog: Live Feed of Tweets Mentioning #Egypt #Jan25 #Tahrir

Richmond Times-Dispatch: Census 2010: Racial Breakdown by Geographic Area

Edmonton Journal: Edmonton Public School Attendance Maps

Ottawa Citizen: High Flying Prime Minister Harper Racks up Air Miles: Analysis

Are you a journalist already using GeoCommons and you’d like to be included on the list above? Let us know in the comments.
Did my DC analysis peak your interest and you might want to do a story? Checkout the map below:

View full map

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!

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

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.

April is Cesarean Awareness Month. According to the World Health Organization, “Cesarean section rates should range from about 5% to 15% in any facility, depending on its level.” With a 31.8 percent Cesarean rate in the US (nearly one third of women give birth surgically), many questions are arising regarding the necessity and safety of Cesarean sections. C-sections can be a life saving procedure in an emergency and because of the advances of medicine in the last century, many babies have been born alive and healthy who might not have survived otherwise. However a C-section, like any major surgery, can be dangerous for the mother and the baby and can add risk rather than reduce it when the procedure is done unnecessarily. Further, many other forms of medical interventions pushed in hospitals such as induction and use of pain medication can increase the need for a C-section..

I have put together some maps that may help shed light on the subject and even help women looking for alternatives to a hospital birth.

C-Section Rates
Not many states publish C-section rates at the county level and it is difficult to find rates for individual hospitals. New Jersey does have public rates by hospital. The map below shows those rates. What is alarming is that there is such a wide range of rates from hospital to hospital (18% to 62%), even when they are very close to each other geographically. For a procedure that is meant for emergencies, it is not logical that women in different hospitals would have such different chances in ending up in surgery. One major factor in this discrepancy is the individual hospital’s policy on Vaginal Birth after Cesarean (VBAC). Other hospital or individual OB protocols have an influence such as limiting the time of labor or use of drugs to augment labor.

Click here to see this map in Maker!

Birth Centers
This next map shows C-section rates by state and the locations of birth centers throughout the US. This map does show a spatial trend in C-section rates with the South and much of the Northeast and West Coast experiencing the highest rates and the group of states including New Mexico, Arizona, Utah, Colorado, Wyoming, and Idaho with the lowest rates. It is difficult to determine a certain relationship between a state’s C-section rate and the number of birth centers. Some states may have more supportive policies towards home birth such as New Mexico and therefore do not have the demand for birth centers. Other states, where the C-section rate is high, have many birth centers because women are looking for alternatives, such as in Florida. Also, more rural states may have more birth centers because of the long distances to hospitals. There are probably not enough birth centers anywhere for them to have a significant impact on the C-section rate.

Click here to see this map in Maker!

To show the number of birth centers normalized by the population of each state, the map below shows the total number of births divided by the number of birth centers. In this map, the blue states have no birth centers. The white states have the most for the population and the orange states have the least number of birth centers for the population.

Click here to see this map in Maker!

Homebirth
What used to be a normal custom, home births have become controversial to the point of being outlawed in many states, even though studies have shown that for low risk births, home births are as safe as hospital births and much less likely to end up with unnecessary medical intervention. While parents can not be persecuted for having their baby at home (although sometimes may be investigated by social services for neglect), the midwives who attend home births are in many states doing so against state laws and can face jail time. The next map shows the legal status of these mid-wives, know as Direst-Entry Midwives, for each state. The blue states are where it is legal and regulated by the state and then the others are various levels of legality with the dark orange states being the ones where home births are totally illegal. The dots on the map show the C-section rates for each state. Here you do see some relationship between the C-section rate and the legality of home birth.

Click here to see this map in Maker!

International Birth
While the US is know for having some of the most advanced medicine in the world, we rank below 45 other countries in our infant mortality rate which is 6.26. This is mostly due to a lack of access to health care however it does not speak well for the form of highly medicalized maternity care practiced in this country. Countries such as the Netherlands where home births and drug free births are more common have much lower rates of infant mortality (4.73). Also developed countries with lower C-section rates (12% in the Netherlands and 18% in France) tend to have lower infant mortality rates. This last map shows the world infant mortality rate rankings (with rank 1 having the highest rate) as well as some country’s C-section rates. C-section rates are not available for all countries and some of the rates shown on the map are from the 1990′s.

Click here to see this map in Maker!

What is most important is that the choice of birth place and method is left up to the women, which is less and less the case in the US. Policies against home births and VBACS and medical staff that push induction, C-sections, use of forceps and other interventions take away the ability of a mother to have a natural and normal birth. While all of these procedures are necessary in certain circumstances, they are abused and too often the mother is not informed of risks and other options. Further, alternatives are being taken away from mothers by state legislation. Check out these maps to find out the legality of home birth and the C-section rate in your state, to find the birth center nearest you, or to just learn more on the subject.

An interesting point that Madrigal makes, “when the geological conditions are right, geothermal can be downright cheap.” A good example would be the jumble of tectonic plates underneath the Philippines which has created the perfect situation to tap into considerable amounts of geothermal energy.

The map below, created in Maker, shows the above information represented in the pie chart.  The countries were geocoded and then dark orange proportion symbols are used to show the difference in percent of geothermal energy production output:

The symbols that are marked by a yellow point on the map represent one of 5 major green-tech projects which have already begun or are planned for the future in the global struggle for a cleaner environment.  Click the different symbols to obtain a description of each energy project.  Current and planned capacity of geothermal electricity(in megawatts) is also available.