Over two weeks have passed since the 7.9 magnitude earthquake devastated Chengdu, China. The end of this tragedy is still hiding as aftershocks continue to ripple throughout the country. News feeds continue to stream from China as the recovery process continues. The most recent reports have the death toll climbing to 50,000.

The USGS keeps daily records of recorded earthquakes worldwide, and enables us to pinpoint earthquake locations by providing latitude and longitude coordinates. Within Finder we have made this data available for use in shape, kml, and CSV formats to the public.

The following Datasets can be found on Finder, and can be used to gain a spatial perspective on the current events in China.

USGS, M 1+ Earthquakes, World, 5.5.08 through 5.12.08
USGS, M 1+ earthquakes, World, 5.12.08 through 5.19.08
USGS, M 1+ earthquakes, World, 5.20.08 through 5.27.08
USGS, Earthquake Records, World, 1998-2007

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Are paper maps no more? GIS Lounge reports that the cartography division of the California State Automobile Association is slowly being phased out. The cause for the demise is the widespread availability of online map directions and in-car navigation units which cut demand for the paper maps by 13% in 2007.

The Geospatial Semantic Web Blog shares some good news for the semantic web community. The U.S. Security and Exchange Commission recently proposed a timetable requiring 500 of the largest public companies to begin filling their financial data using XBRL (Extensible Business Reporting Language). This will create a mass amount of free and real-world data for research.

Speaking of data, Anand at DataWocky answers the question of why the world needs a new database system. He discusses high volumes of data that are not being utilized due to scalability. He points to the newly launched Aster Data which is a database system natively designed and architected from the ground up for a new hardware platform: commodity clusters.

Google Earth has a new browser plug-in, which continues its roll out of Google Map API for Flash and Google App Engine. Released with it is the very extensive Google Earth JavaScript API for writing 3D map applications. Moxie thinks that this has opened a new page for GeoWeb visualization.

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With all the data we are pushing into Finder! sometimes it is easy to loose track of the interesting pieces unless you are searching for something specific. So, we thought it would be fun to post up a “dataset of the day” on the blog. The goal is to let folks know about new datasets or content that is relevant to a current event.

Since Memorial Day officially kicked off the summer driving season we thought it would be useful to map the current state of gas prices. We all know that gas prices are sky rocketing, but who is getting hit the hardest and suffering the largest increases. Today Bill (one of our resident data gurus) grabbed the latest data from AAA, calculated percent change from a year ago and loaded it into Finder! You can access the data here.

Next he took the data out as a shapefile and whipped up a quick thematic map in a GIS application. The first map is of prices with red being the highest and green being the lowest:

For the next map he took the percent change he calculated to illustrate the states with the largest increase in price (shaded red) and those experiencing the least price increases (green):

When it comes to the most expensive gas California, New York, Connecticut, Michigan and Illinois lead the pack and they are spread fairly evenly across the country. On the other hand if you look at percent increase in prices there is a large concentration of reds in the Midwest and South. This is especially stark in the manufacturing rust belt states of Michigan, Ohio, West Virginia and Indiana. The unfortunate things is these are the same places that have been hardest hit the recession and unemployment as you can see in the map below of percent unemployed for February 2008 (data here):

While there is no cause and effect between “percent unemployed” and “percent change in gas prices” it does illustrate the sad reality that those with the least ability to pay for an increase in their gas budget are being hit the hardest. Although if you wanted to run a correlation analysis you could pull the files out as .CSV and go to town.

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