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: 14% [?]

Our own Andrew Turner and Mikel Maron presented at Web 2.0 Expo today on trends and technology in Where 2.0.

Cutting Edge Where 2.0 Trends

To kick things off Brady Forrest of O’Reilly and Andrew just published a report on the GeoWeb space, it’s available now.

For those not familiar with the Geo-geek world they started off defining the Geoweb as an interlinked set of people and places around the world that is finally web aligned. These people and places are linked together through open standards that can be searched and indexed online. This gives us a huge trove of information and data from numerous sources.

A new driver of geodata is the trend towards mass adoption of location aware mobile devices. Users are bringing mobile devices into social environments, business, and even global settings. Socialight provides users collaboration capabilities leveraging online and mobile technologies to provide reviews and information on top restaurants and other locations. Large established companies such as TeleAtlas and NAVTEQ are actively expanding into mobile as well. Users can also gather data on their own, with massive success of OpenStreetMap being one prominent example.

The majority web 2.0 services are starting to add geography. Wikipedia, Flickr, even YouTube. This information can now be mined. Check out geocodr which create geotags based on Flickr photos.

One issue that’s coming up with all this open data is metadata and providing source authentication. Can the data be trusted? The ability to crowdsource information for a specific crowd or business is exciting, but having authoritatively knowing the source of where the information is coming from is even more important. Privacy is another pressing issue with open data. Flickr is offering geo-privacy to their photo uploads - this introduces the idea of “casual privacy”. Trusted locations also have a strong impact on how data is perceived by users.

Omnifocus has an iPhone tool that adds geolocation to your “to-do” list. It automatically geo-locates you, so you can discover the closest option to complete the next task on your list. So, if you’re out at CVS and need to go pick up something at a grocery store, Omnifocus can tell you where the closest store is to check that task off your list.

Mapvertising is another interesting concept. Coupons and other location based advertising have to be able to understand what users are searching for in context. For example, searching for a “romantic restaurant” should NOT result in an advertisement for Hooters.

FAIL:

Andrew also discussed advances in traditional GIS. ESRI is one big GIS solution provider that is opening their data via KML, javascript and flash.

Many new users are looking to do more complex analysis than just mapping push pins and would like to map more sophisticated open source and private data. For example, with Maker! you can take a look at how average rent in Manhattan can be thematically mapped to discover price trends.

Burning Man 2008 GeoHacks Technology (The future)

At this point Andrew passed the presentation over the Mikel to provide more of a geo-hacking perspective perspective on the GeoWeb. He got things into gear by discussing what he learned in the Black rock Desert of Nevada. Burning Man is a blank canvas on which to dream and create anything. It’s a single week long social experiment and a petri dish of urban development. It’s almost a laboratory that we can use to begin to examine all the geodata and tools we have. We can look at urban development, how social groups form in cities, etc.

Burning Man Earth had a lot of “geogeekery”. Over 100 GB of data were collected over the week.

Why is this important for Web 2.0 Expo? This is a prime example of Web 2.0 and Where 2.0 and what these technologies can enable. Open platforms, open standards, open data, and collaboration that is easy and cheap.

Amateur Remote Sensing

• pictearth.com
• diydrones.ning.com
• openaerialmap.org

We took remote sensing data every day. You don’t need expensive gear or a satellite. Small planes are used to get imagery with under $500. A plane is used with continuous shots and a camera hanging out the window with a clamp. The pilot has to manage the camera out the window, but there were no problems finding volunteers. “There are 3 F’s that no pilot will refuse….Food, Fuel, and something else…” Not only did we get aerial pics from planes, but also from kites.

Some great shots were taken as burning man convened. You can see how people created their spaces based on where the ones before them placed their tents.

We then processed the photo using ERMapper, ESRI, Photoshop, and some blood, sweat and tears.

The GeoDjango platform was used to collect even more visual data. If you have geographic items in your models, it can map to nicer views. A camp layout was placed in CAD for even more visualization, which we received in a PDF. To georectify, we used ESRI. We also had to use WMS and tiling (TileCache). These OpenLayers provided vectors from the PDF.

We then wanted to extend this to a social networking platform and get media artifacts from the information. We used “pinax” for networking. This data can be used for future city layout and camp planning tools.

Flickr took these tiles so people could geotag their photos from Burning Man based on the location of their maps.

Some of the other technologies that were used during burning man was Garmin radio for friend location, GPS tracking of vehicles, digipeater (which rebroadcasts to the Internet down the line for free).

Parting words:

“The Internet brings us together, but what if the single link holding us in place breaks?
What if what we learn in the harsh environment of the playa could be reapplied to those in crisis, instead of artistic indulgence?”

Popularity: 18% [?]

Sean mentioned in his blog about how pooling together of efforts by Andrew, Sean, Bill et. al, the Fortifacture/MapuCommons folks were able to bring to you in record time the near-real time pollution data from Beijing. As we were working on this, we realized that there is a huge difference in the perceptions between the host nation and most of the western world/media on what constitutes severe air quality problem. For eg. see below the two pics, both dated 5th August, 2008. One shows Beijing “Clear skies” while the other has haze/smog blanketing Beijing. Wonder whether they are talking about different places and different days!
Xinhua Photo

The photo taken on Aug. 5, 2008 shows the clear sky above the National Stadium, namely the Bird’s Nest, in Beijing, capital of China. (Xinhua Photo/Li Ziheng)

BBC Photo

5 August PM10 reading: 104 micrograms per cubic metre. The World Health Organisation guideline maximum is 50 micrograms per cubic metre, averaged over 24 hours.

Knowing that many countries in Asia, including India and China share the dubious distinction of having the most polluted cities in the world, the media’s obsession with hazy skies should come as no surprise and that much of the media coverage of Beijing Olympics has been about the quality of air. See for example, this split picture of Beijing skyline on a clear and a hazy day on the BBC’sBeijing Pollution: Facts and Figures.

BBC has, for last several weeks, a daily pic of Beijing skyline with a running commentary on the hazy conditions, on their Beijing Pollution Watch site. So we at FortiusOne/Mapufacture decided to generate a daily map of the official stats on PM10 published by Beijing Municipal Environmental Protection Bureau (BMEPB) and compare it with BBC’s Beijing Pollution Watch. PM10, the airborne particles consisting of dust from construction,landfill sites, vehicle exhaust, industrial sources etc. of size 10 microns or less, are the main culprit behind the hazy skies /bad air days in Beijing.

The map below is based on the air quality monitors spread across dozens of Beijing districts along with the locationsof Olympic events (red circles). The six slices of each pie-chart show share of PM10 at each location between 5th and 10th Aug, 2008.

th

The second map shows today’s readings of PM10 (purple colored proportional circles) for each of the air quality monitoring stations, along with a pie-chart that has share of the SO2, PM10 and NO2.

For comparison, see BBC’s pic of the same day below.

BBC: 10 August PM10 reading: 278 micrograms per cubic metre. We test for 10 minutes at midday from a seventh floor balcony in central Beijing..

While the official readings in nearly half dozen air quality monitoring stations nearby have readings near 90, it has apparently, not had an adverse effect on the athletes thus far in the games. As BBC offers daily pics of the smog, we will have daily updates on the air quality all through the Olympics. In the mean while you may explore on the Finder! the air quality data (SO2, NO2 and PM10) for the last six days i.e, 5th to 10th August, 2008, the road network, and the “>district polys as well as Olympic Athletic Venues,and Olympic village. Search using keyword “Olympics.” You are welcome to download, add, update and upload these data back to Finder!

Popularity: 29% [?]

After a bit of airline nightmare (do any US flights arrive on time anymore?) I made it to Limerick Ireland for OpenStreetMap’s (OSM) conference “State of the Map“. The talks have really highlighted how popular OSM has become. Roughly I break them into two buckets 1) the state of mapping a country 2) doing something cool with OSM data. The number of country presentations providing updates on OSM mapping progress is really impressive.

The usual suspects including Germany, Netherlands, France, our host Ireland and some surprising new places like Japan and Bolivia. The universal quality of each country presentation was how much had been mapped in the last year. The before and after pictures are quite dramatic. This was only reinforced by the massive posters on the wall of the conference venue showing six month incremental growth of mapped streets in the UK and Germany.

Equally impressive was the number of projects that are using OSM data for an innovative application. My personal favorite was OpenRouteService which allows users to geocode and route against OSM data. The project is being run at the University of Bonn and there are plans to open source the code, which would be great for providing open global geocoding services. Something we’ve struggled with finding. ITO also had a clever OSM implementation that allows you to query and filter through OSM data as separate map layers, both by feature type and edits (including temporal).

Wikitravel and Nestoria both had interesting examples of commercial services being built on top of OSM data. Wikitravel has a particularly cool approach where all the data they and the community has built up is free under Creative Commons, but they charge for on demand publishing of travel books you can take with you on a trip. Have to see if their geographic listings of travel amenities is something we can add to GeoCommons.

One of the most illuminating talks was “OpenStreetMap v the World” by Dair Grant, which provided a quantitative comparison of the accuracy of OSM vs. TeleAtals via Google Maps. Dair first did an analysis of Heath Scotland (roughly 10 km2) and found 89 errors in the TeleAtlas data. He then moved to Edinborough and did an analysis for a 10 km2 of OSM data and found 192 errors. Conclusion that none of the map providers are 100% accurate and OSM is not far off. A bit of detail on the OSM errors:

- 50% of the errors happened in 1km2
- 50% of the errors were missing roads (completeness is a common OSM challenge)
- 20% of the errors were missing names
- 15% of the errors were wrong names
- Only one error was from an incorrect junction

The big advantage of OSM when it comes to inaccurate data has been the ability to change the data easily. The big guys have caught on to this and just about all of them have developed technology to copy the concept:

- TomTom MapShare
- AND Map 2.0
- Google MapMaker
- TeleAtlas MapInsight

Dair pointed out that with all these services that there is no feedback loop to indicate your change was accepted and/or has been implemented. I believe this may not be true with Google MapMaker where the moderator provides feedback. Dair also provided a suggestion for OpenStreetBug to provide an easier mechanism to point out errors on the map. With all the discussion around crowdsourcing and accuracy this was a very enlightening talk. More to come from day two tomorrow.

Popularity: 18% [?]

Cuba has been in the spotlight lately as Raúl Castro officially takes over as President ending the 49 year rule of his brother Fidel Castro. What will be the legacy of Fidel Castro and the socialist revolution that he led since 1959? One of the most acclaimed successes for the Cuban government has been its progress in health and health care, particularly in the rural areas in the eastern part of the island. Whether or not health care in Cuba is what the government claims it to be is strongly debated. See for yourself the state of health and health care in Cuba using Finder!.

The Cuban government provides in depth statistics on the health of its population by province and finder has these data available for the years 1996 to 2006 with more than 80 health and health care related attributes. Whether you are interested in the change in infant mortality over the last decade, which provinces have more doctors per resident, or what is the leading cause of death in each province, this dataset will help illustrate what the situation is on the island.

Here is an example of what these data can be used for. This map shows the number of family doctors per habitant in 2006. Provinces in red have less doctors and the green ones have more.

See data for:
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996

Popularity: 18% [?]