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.

This is one of several traits he shares with Peter Batty. Peter has an amazing ability to not only see where the industry is going, but also a deep understanding of where it has been. He has been one of the most prominent links between traditional GIS and the mainstreaming of geospatial into the broader Web. Even more impressive is that Peter not only thinks it, he has been out there executing against his vision. From IBM to SmallWorlds to Ubisense to Intergraph he has not only had big thoughts but turned them into reality. I consider it a great complement to the company and the vision that he and Jeff have agreed to join up. Combined with Wolf Ruzicka of Eastbanc and Michael Frankel of LexisNexis we have a great set of minds to help us grow the next generation of location intelligence.

One of the problems I faced while combing through the GPMP, aside from the very technical terminology (a lot of the data the Columbia students created uses complex formulas:), was that the maps themselves were only viewable in .pdf format without any real mapping interface.  I wanted to click on different areas of their maps to get a better idea of the poverty level for each nation, provincial, and municipal level areas.  This was easily made possible after a couple clicks in Finder! and Maker!.

Below are two of the maps I created in Maker!. The first being a fairly basic global-level map showing the prevalence of global child malnutrition:

Click here to view map in Maker! and here to view dataset in Finder!

Darkest green countries with a negative value didn’t have child malnutrition data; likely not created by different national government data sources. By also clicking different countries you can get an idea of the percent of the population under 5 years of age.

A second map I’ve created from the GPMP is a bit more complicated as I mentioned above because it uses a complex formula called the Poverty Gap Index. It measures the severity of poverty by taking the per capita cost of eliminating poverty (relative to the poverty line). Below I’ve showed the Poverty Gap Index of Bangladesh in each municipality.  The areas with darker color red are the poorest municipalities and the lighter shades of red are the non poor because they  represent a zero poverty gap.  In other words, the darker the shade of red the greater the poverty gap the poorer the municipality.

Click here to view the map Maker! and click here to view in Finder!

It is striking how diametrically divergent the views are in the two pieces. Granted one is title “GIS is Dead” and the other is written by the largest GIS vendor in the world. That said there are some very different thoughts about where our industry is headed. To provide a bit of context take Direction Magazine’s Adena Schutzberg’s comment to Meltz’s post “The other thing to bear in mind, I think, is that “desktop GIS” is slowly dying, so learning it is not a long term career move.” Then compare that to ArcNew’s take, “Desktop GIS will continue to grow as the solution for most spatial analysis projects and the fundamental authoring platform for creating the majority of the geographic information on the GeoWeb.”

I believe the main point of divergence centers on the role and future of the GIS professional. Meltz states “GIS is on it’s way out as a profession”", while ArcNews states “GIS professionals are more relevant than ever”. I actually think both statements have merit. Having a solid grasp of geographic concepts and theory is going to be increasingly relevant to a geo-enabled world, but access to technology tools to implement those concepts is going to increase dramatically. The current GIS practice of the map being a one directional work flow where GIS professionals create maps and the rest of the world is limited to viewing them is on the way out, if not already through the door.

The single directional flow of maps seen in the diagram in ArcNews is in the midst of being changed to a bi-directional flow. Society is creating maps and pushing data back to GIS professionals. The GIS professional is still very much part of the game, but not the gate keeper or sole purveyor of maps. The philosophy promoted in ArcNews that “the map…is a rich stand alone information product than must be designed carefully for end users (by GIS professionals)” is being bypassed by several technologies. There have been millions of Google MyMaps created. GeoCommons alone has over 14,000 maps designed by non-professionals. This includes thematic mapping and spatial/temporal analysis. SpatialKey, RhizaLabs, MapSpread and several others provide similar services for non-professionals.

I think statistics is the better analogy for where we’ve seen this shift in the past. For many years statistics was the domain of specialists who used packages like Stata, SPSS and SAS. Then spreadsheets came along allowing non-professionals to do basic statistical work. Over time spreadsheets became more complex allowing users to do more sophisticated statistical analysis, but statisticians are still alive and well. In fact many are saying statistics is one of the hot job prospects in the next ten years.

In short the destiny of GIS is not to be walled garden where the GIS professional dictates what can be a map. Instead it is a community where GIS professionals are a thriving participant contributing to a growing ecosystem of map creators and data contributors. The removal of the walls and control is critical to the science of GIS growing and its power being appreciated by the public. You can only appreciate a persons value when you’ve walked a mile in their shoes. We need to embrace both - what the map once was and what it will be in the future.

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.

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!.

The map below shows the locations of all the past and currently planned Olympics (included are those that were canceled due to WWI and WWII). The first Olympics was in 1896 in Athens. Before the Rio decision, no country in Latin America or Africa has ever hosted one.

Click on map to see it in Maker! This next map shows the number of medals per country in the 2008 Winter Olympics in China. Click on map to see it in Maker! This last map shows the expected locations of the competition venues in Rio for 2016.

Socialized medicine, which in the case of US politics is another term for universal health care, has been highly criticized by those hoping to defeat President Obama’s health care plan. Claims that it will lower the quality of services, will cost more, and will mean waiting longer for care are among the criticisms. Many are pointing to countries who have universal health care as examples of just how bad it will be. So how bad is it?

I thought I would use Maker! and some datasets from Finder! to see what these ’socialist’ health care programs look like…on a map. I took various indicators of health and health care quality from the US and compared them to other industrialized countries that do offer some form of universal health care.

The first map shows Total Health Expenditure per capita, US$ PPP. The US spends the most on healthcare at $7,290 per capita, $2,500 more than the next biggest spender.

The next two maps show population health indicators from the OECD; Infant Mortality Rates and Potential Years of Life Lost for Males. The US, despite spending the most on health care, lags behind on both of these indicators. Many opponents of universal health care in the US claim that countries that do have it have to wait longer for their care. A study in 2007 by The Commonwealth Fund shows that sentiment to be inaccurate. The following maps show wait times in five countries. The countries included are Australia, Canada, New Zealand, United Kingdom, United States, and Germany. This next map shows the difference between waits for urgent care and waits for non-emergency surgery. The red circles represent the percent of sicker adults reporting a wait of 6 days or more to see the doctor when sick or in need of medical attention. The green circles represent the percent of sicker adults who experienced wait times of 4 months or more for non-emergency or elective surgery. The larger the circle, the more people who reported waits. While the US has next to the shortest waiting times for non-emergency or elective surgery, they have next to the longest waiting times for urgent medical attention. The last map suggests that perhaps wait times are better for those who can afford better health care and therefore are able to have non-emergency or elective surgeries. This next map, however, shows that wait times are also long for those with above average incomes in the US. In this map, the orange squares represent wait times for adults with below average income and the blue circles for above average income. Although the US does have the largest disparity between higher and lower incomes for wait times (difference between size of square and circle), people with above average incomes also have longer wait times for urgent medical attention than those with above average income in two of the four other countries. This suggests that even those who can afford good health care have longer wait times. While this data falls far short of telling the whole story it does point out consistently better results in health outcomes and efficiency in countries with universal health. To get an idea of how the US matches up to all countries in health care, we can look at a map of the WHO’s World Health Report Health Systems Performance Rankings where the US ranks #37. There are many other interesting metrics that can be mapped on this topic so check out these datasets in Finder!, or upload your own and map them in Maker!

You can get your check-in feed as RSS, KML and ICS from the feeds page.

From the Finder! API you can register URLs.  Below is a sample curl command to register a KML feed of your Foursquare check-ins.

curl -i -X POST -u “yourusername:yourpassword” -d “overlay[wild_url]=http://feeds.foursquare.com/history/yourkmlfeed.kml” http://finder.geocommons.com/overlays.xml

Replace “yourusername:yourpassword” with your GeoCommons username and password and “http://feeds.foursquare.com/history/yourkmlfeed.kml” with your Foursquare KML feed.

By default your layer is not shared in Finder!, if you want to make a map though you have to share it.  In order to share it go to Finder and login.  Once logged in press “My Layers” and share your layer there.  If you ever decide you no longer want to share your check-in feed you can mark it “not shared” here as well.

Once your layer is uploaded and shared you can create a map.

I embedded the map I made in my personal blog’s about page.  To embed your map click on the “Details” button while viewing the map and then click on “Do you want to embed this map in your website?”  Copy the code that appears and paste it into the HTML of your blog or other website.

If you enjoy playing Foursquare and want to share your feed try this out.  For more information on the Finder! API checkout it out here. Also if you ever happen to visit us in the GeoCommons’ office, check-in it is an order from the Mayor.

The first map displays the correlation between regular season standings and the average cost of beer by ballpark. The correlation between the two is not that great with .32. This could be used to confirm that ballclubs with losing records and pricey beer can’t justify making fans pay that much to drink while watching the game. Let’s examine the data a little bit closer. Click each map for a larger view.

Teams such as the Pirates, Nationals, and Diamondbacks have terrible regular season standings, but also have pretty low cost of beer. So, if you are looking for a game to watch and drink cheap beer, these would be your best options. The Oakland Athletics stands out on the scatter plot because they have a great regular season record but also have some of the most expensive beer in the biz. So, perhaps the cost is justified because the team has a better record and fans might not mind sipping the premium suds.

I thought it might be interesting to also see whether the fan cost index correlated with the team’s regular season record. The Fan Cost Index comprises the prices of two (2) adult average-price tickets, two (2) child average-price tickets, two (2) small draft beers, four (4) small soft drinks, four (4) regular-size hot dogs, parking for one (1) car, two (2) game programs and two (2) least expensive, adult-size adjustable caps.

The FCI and regular season record correlation is slightly higher than the cost of beer correlation at .45, which means that about half of the time ballclubs with a high FCI also have a high win record. There are again some outliers, which can be viewed in the map below.

The NY Yanks have a regular season record of .637 and a FCI of $410.88, which means if you want to see a team with a good record, you’ve got to shell out the big bucks. If you don’t mind watching a team lose while you take your family to a game, again the Pirates or the Diamonbacks might be a good choice as the FCI for those teams is 135.06 and 114.24 respectively, and both teams have .385 and .438 records respectively.

Either way you look at it there is no team that has a perfect correlation and with so many die hard fans out there for Major League Baseball, people will pay the bucks for their teams no matter what. It might be interesting to take this data and do a correlation between FCI and fan attendance and see whether or not there is a trend in the past ten years towards less attendance for certain teams and whether or not that has to do with the teams records or the average FCI associated with that ballpark. Are people less likely to attend a game because the FCI is too high or because the team stinks? Explore the data out there and see if you can come up with an interesting correlation for this or anything else you think is provocative, feel free to post a comment with your analysis.

In the meantime, have a beer and go support your local team.