IKEA furniture stores seem to slowly but surely be making more of a global appearance as the demand for affordable furniture becomes greater. The success of IKEA is largely in part due to their business concept by selling well designed home furnishings at prices so low that as many people a possible can buy them rather than selling overly expensive furniture that only few can buy.

Ingvar Kamprad opened the first IKEA in 1954 in Småland, Sweden. Now with almost 300 stores worldwide, many people have had a chance to shop in IKEAs and thus aided in spreading the success of the IKEA business concept and making it possible for the opening of new stores.

Considering myself a GIS-buff, I was interested in mapping the global locations of IKEA stores to obtain a visual perspective on how far the IKEA phenomenon has spread. By using Finder and Maker, I found that after making a simple .csv file in Microsoft Excel with latitudes and longitudes of each store, I was able to see where current IKEAs are located:

To see the dataset click here: http://finder.geocommons.com/overlays/5036

To see map click here: http://maker.geocommons.com/maps/624?page=1

The map illustrates that there is a large concentration of IKEAs located in the Euro area. No surprise there because from 1958-1980 most of the IKEA locations were started in neighboring countries to Sweden where the furniture powerhouse got its roots. Several other locations opened in countries such as Australia, Hong Cong, and Singapore which became IKEA franchises in the late 1980s. Presently, there are IKEAs located in 37 different countries around the world.

The following map shows 10 new IKEA locations that will open before 2009 labeled by the blue tabs:

To see the dataset click here: http://finder.geocommons.com/overlays/5354

To see the map click here: http://maker.geocommons.com/maps/953?page=1

Keep your eyes peeled…an IKEA store may be coming to a town or city near you!

Popularity: 7% [?]

My mom called me the other day and asked me if I knew how much the U.S. spends on the military. I said that I didn’t but I that could find out. “In fact”, I told her, “I can make a map of global spending on the military by country” (because Finder! and Maker! make it so easy!). So I found global data from the World Development Indicators database that gives Military Spending for each country as the percent of central government spending. I uploaded the data to Finder! and then made my map (and sent it to my mom of course).

Then I remembered that I had recently uploaded a dataset from The Fund for Peace. This dataset rates countries around the world on a total of 12 indicators. Taking a closer look at this dataset, I realized that the indicator Demographic Pressures, when mapped, seemed to have some correlation to the Military Spending map. The Demographic Pressures indicator takes into consideration the pressure on food supply and other resources, restrictive group settlement patterns, disputes relating to group settlement patterns, and pressures from a skewed population distribution. Countries are scored an a scale from 1 to 10 where 10 equals the most pressures.

So I took a closer look at both maps.

To view the datasets click here for Military Spending and here for the Failed States Index of 2008. To view the maps click here.

I found that of the top 20 military spending countries, 40% of those scored 8 or higher on Democratic Pressures and only 20% scored less than 5 (including the USA). Of the 20 countries that spend the least on the military, 50% scored less than 5 and only 5% scored 8 or higher.

 
It is evident in the map that the pattern of high military spending and high democratic pressures seems to be most prominent in the Middle East, Africa and in some areas of Asia. The areas of low military spending and low democratic pressures fall mostly in Europe.

While the pattern is evident, the reason for the pattern may not be so easy to determine. It may be related to government attention to military building instead of key internal problems. It may be that countries that have high population density also have large military infrastructure. It could also just be a regional phenomenon. Maybe readers have an even better theory (leave a comment!). Either way, it’s an interesting pattern and one that wouldn’t have necessarily been predicted.

Popularity: 7% [?]

In the past we blogged about Sarah Palin’s reach with women voters in key swing states. Tonight’s debate will be a turning point for undecided voters who are basing their election decision on what is said and presented tonight. A Washington Post-ABC News poll shows 6 in 10 independent voters think Palin is unqualified for the job. The McCain-Palin ticket is under additional pressure with Barack Obama solidifying leads in battleground blue states such as Michigan and Pennsylvania. In recent days, Obama had already taken leads in several states which voted for President Bush in either 2000 or 2004: New Mexico, Iowa, New Hampshire, and Colorado.

Using Finder! and Maker!, we developed maps that showed the potential Sarah Palin has in securing the important female vote, particularly in her target demographic (women under 50). We first looked at which states had the highest population of women between the ages of 18 and 50, and then examined the correlation of those states which were electing female officials.

States such as California, Texas, and Virginia have a high female voting population, and are electing female Mayors. There is a high population of female voters in battleground states like Utah and Colorado, but they do not generally elect female officials. Based on these demographics, Sarah Palin may need to do some strong campaigning in those battleground states in order to ensure that women vote for the McCain-Palin ticket.

Take a look at how we used Finder! and Maker! on our YouTube channel.

Popularity: 13% [?]

After too many late nights wrapping up bug testing and deploying production servers the folks here have Maker! and an upgraded Finder! ready to go. It is a great feeling to get a first version of the full intent of GeoCommons out to the public. There are still lots of enhancements and polishing we’d like to do, but everyone is excited with what we’ve achieved with our initial release.

One aspect we are particularly proud of is the access non-technical users will now have to data, allowing meaningful mapping of a whole new world of information. Data that was once the sole providence of GIS professionals can now be mapped by anyone. Not only can they access the data but be guided through a process of creating a cartographically and statistically accurate map. We are indebted to Dr. Mark Harrower and his team at Axis Maps for baking their years of GIS and cartographic expertise into Maker. Specifically the “Map Brewer,” an homage to Mark’s adviser at Penn State Dr. Cindy Brewer and his first foray into cartography for the masses “Color Brewer“.

We’d also like to thank several folks in the community for their feedback on the application as it went through development. Whether it was Wherecamp, FOSS4G, GeoWanking or State of the Map, we benefited from many great conversations and feedback from friends passionate about both the GeoWeb and GIS. We hope that GeoCommons can play a small part in bringing the two communities closer together. I believe there is a huge benefit to the public of convergence between GIS and the GeoWeb - with the potential of making Geography relevant and important to everyone. If we can play even a small part in this I see the project and work as a great success.

Borat Wishes GeoCommons Great Success and Asks Users for Patience When Loading Big Data Sets in Maker! Like Location of All 25,000 Borat Haters in Great Nation of Kazakhstan

Popularity: 14% [?]

As the continuing turmoil in the financial sector and a mind boggling trillion $ bailout are together contributing to the general feeling of economic gloom and doom across the nation, the distressing news of a big bank failure last Thursday made it worse. The Office of Thrift Supervision (OTS), announced that it had closed Washington Mutual (WaMu), a big bank with over 45,000 employees, more than $300 billion in assets, 2,200 branches in 15 states with over $100 billion in deposits. Billed as one of the largest bank failures in the U.S. history, WaMu was taken into receivership by FDIC (Federal Deposit Insurance Corp), the federal regulatory agency and promptly sold to the highest bidder, JP Morgan & Chase Co for as little as $2 billion.

According to OTS, WaMu, one of the largest savings banks in the country, became unsafe after deposits of more than $16 billions left the bank in just last two weeks. WaMu, which specialized as a mortgage bank was the latest victim after IndyMac, of the combined crises of downturn in housing sector, subprime and credit crunch. FDIC maintains an ongoing list of 100 risky banks. However, this is a secret list that will never become public and neither is the process by which such a list is created.

So we at FortiusOne decided to create our own list the 100 banks based on the level of their exposure to risky/bad loans on their books. For this analysis, risky loans are computed as the sum of the total value of loans on foreclosed residential properties and mortgage defaults (30 plus and 90 plus days late mortgage payments as well as the residential properties that have stopped paying mortgages but have not been foreclosed yet.) The data for the 2nd quarters of 2008, 2007, 2006, 2005 and 2004 was downloaded from the FDIC website and then geocoded. Only those commercial banks who specialize in real-estate mortgage business were selected and then ranked according to the level of risky loans. The top 100 banks for each of these quarters were combined into one single file, then sorted by the value of the risky loans for the 2nd quarter of 2008. The map below shows only those banks that have risky loans for each of the 2nd quarters, the pie-charts represent risky loans by the 2nd quarters between 2004 to 2008.

Pie-Chart Legend: Red = 2nd qtr 2008, Orange = 2nd qtr 2007, Yellow = 2nd qtr 2006, Green = 2nd qtr 2005 and Purple = 2nd qtr 2004

So how exposed was WaMu to risky lending practices? A lot, according to our analysis of FDIC’s banking statistics for last several quarters. See the cartogram version of the data below where the size of the pie represents the total value of the risky loans. Clearly, WaMu tops in the risky loan business for each of these quarters spanning the pre- and post-housing bubble. WaMu’s bad loans for the 2nd quarters for each of the years between 2004 and 2008 range in value from just a $2.32 billion (2004), $2.63 billion (2005), $4.76 billion (2006), $6.7 billion (2007) to $15.8 billion (2008).

Pie-Chart Legend: Red = 2nd qtr 2008, Orange = 2nd qtr 2007, Yellow = 2nd qtr 2006, Green = 2nd qtr 2005 and Purple = 2nd qtr 2004

However, what is scary is that Wachovia, Countrywide, E-trade and few others are not that far behind. Does that mean, they are the next in line to fail? Not necessary, because exposure to risky loans may just be one of the factors involved in a bank’s failure. Search for a more comprehensive bank data for all quarters between 2004 and 2008 on the Finder! with key word “FDIC” and its analysis in the near future. In the meanwhile browse for the data discussed in this blog here:

Risky bank loans 2nd quarter 2008
Risky bank loans 2nd quarter 2007
Risky bank loans 2nd quarter 2006
Risky bank loans 2nd quarter 2005
Risky bank loans 2nd quarter 2004

Popularity: 22% [?]