Thanksgiving is a time for giving thanks and for many it is also a time for giving. I thought I would show how GeoCommons can be used to promote giving back this holiday. One way many individuals and families give during Thanksgiving is by donating to or volunteering at a local food bank’s Thanksgiving feast. This year, these feasts are particularly important with so many suffering from the economic crisis.

Feeding America is a network of individuals, local food banks, national offices, and corporate and government partners who work together to try to solve America’s hunger crisis. With 205 food banks across the country, they were a good resource to put together a quick dataset and map. Below is a map showing all of the Feeding America food banks by the number of pounds of food distributed annually. The map can be used to find a food bank near you. Click on the map to see the interactive version in Maker! By clicking on the icon, you access all of the information on that food bank including the services it offers and its website. You can find out detailes on how to help for thanksgiving by calling or on the food bank’s website. Their website also has a neat map of their own which provides hunger and poverty statistics for each state.

Popularity: 10% [?]

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% [?]

Although there are some signs that the economy is on its way to recovery, the foreclosure rate is not one of them. The most recent data from RealtyTrac show that rates are at an all time high. In the third quarter of 2009, one in every 136 homes in the U.S. were foreclosed on. This is the highest quarterly rate since the housing crisis began. The third quarter rates increased five percent from the previous quarter and almost 23 percent from Q3 2008. It has been speculated that instead of forclosures resulting from bad loans, these new foreclosures are due to increasing unemployment and are a result of a bad economy.

Because many datasets in Finder! are regularly updated, it is easy to access the most current data as well as historic datasets for analysis or to make maps using Maker!. I thought I would use some of the updated and historic datasets on foreclosures to get a better picture of the foreclosure situation.

After searching for the most recent dataset for foreclosures as well as datasets from past months, I have created some maps to demonstrate how foreclosures have shifted geographically. The following set of maps shows the foreclosure rates overtime starting in February 2008. Note that each map is drawn to a different scale so that comparisons between states for each month are emphasized. Foreclosure Rates represent the number of foreclosures filed for every X housing units.

A closer examination of the scales for each month help to illuminate how rates have increased overall. The lower the number, the more foreclosures there are relative to homes. Next I used Finder’s historic unemployment data to see if a relationship between unemployment and foreclosures can be geographically visualized. To compare unemployment rates with foreclosure rates, I have provided for a year lapse from job lost to foreclosure to allow for 6 months of unemployment benefits and 6 months of non payment before the house is foreclosed on. The first map shows the 12 month change in unemployment rates from August 2007 to August 2008 by county. This map shows where jobs had been lost in the end of 2007 to the beginning and middle of 2008. The white counties are where unemployment actually decreased. The second map shows foreclosure rates for the third quarter of 2009. The darker green states have had the most foreclosures in the past quarter. The maps show that some regions do have both high unemployment from the previous year and high foreclosure rates. Of course any conclusion of direct causation can not be drawn from these maps, however, the two factors do seem to be occuring together geographically.

Popularity: 9% [?]

October is National Breast Cancer Awareness Month. According to the American Cancer Society, nearly one in eight women (12%) in the US will develop invasive breast cancer in their lifetime. Globaly it was the second most common cancer in incidence and death for women according to the World Health Organization. To increase awareness of and about breast cancer, I have created some maps to visualize some of the breast cancer data available. The first map, based on data from the CDC looks at breast cancer rates (adjusted for age) in the US by state. For all races, Connecticut and Delaware have the highest rates and Arizona and Mississippi the lowest. Globally the USA has the highest breast cancer age-standardized rate of all countries. The next map, based on data from the World Health Organization and provided by the International Agency for Research on Cancer, shows breast cancer age-standardized rates for 171 countries. This map clearly shows that developed countries have a higher incidence of breast cancer than developing countries, even when age is taken into account. This may be in part due to differences in family planning in developing countries where women have more children at an earlier age and generally breast feed more often and for longer. In a 2002 study, it was found that “women with breast cancer had had fewer births than controls (2.2 vs. 2.6), and a larger proportion of them had never given birth (16% vs. 14%)”. The study also found that “among women who had given birth, those with cancer were less likely than controls to have breastfed (71% vs. 79%) and reported a shorter average lifetime duration of breastfeeding (9.8 vs. 15.6 months)”. To get a better idea of the range of breast cancer incidence among the developed countries, the maps below show them in a more detailed scale. Although the US has the highest rate of breast cancer, it has the best five year survival rate in the world. The next map shows the five year survival numbers normalized by breast cancer cases. This map shows a very strong association between breast cancer survival and location. A womens’s chances of survival are very much dependent on which region in the world they live. Africa has the lowest five year breast cancer survival rates. Latin America also has low rates followed by India, China, the Middle East and South East Asia. Western Europe and Australia have higher rates than Eastern Europe and then finally Japan and North America have the highest five year survival. It should be noted that in the US, there is a racial gap with white patients more likely to survive than black patients. Research, hormone treatment, and early diagnosis have played key roles in improving the likelihood of surviving breast cancer. The Susan B. Komen Race for the Cure is an event that anyone can take part in and raises substantial amount of money to support breast cancer research, education, screening and treatment. These races take place throughout the year all over the US as well as in a few international cities. This last map shows the locations of the Race for the Cure events around the world in 2009 and those that have been planned so far for 2010. You can click on the map to see it in Maker! and once in Maker! click on each location to see the details of the race. If you are interested in seeing this data yourself, please download the spreadsheets from Finder! or you can make your own maps using the datasets in Maker!.

Popularity: 16% [?]