UUorld

Two bugs were fixed today:

1) An issue that intermittently caused stat imports to fail.

2) A crash that occurs when toggling stats on older or less capable graphics cards.

You can download the new version here.

The two issues preventing people from logging into the portal have been resolved. If you have any trouble with the portal or any other feature please contact us.

There are two bugs afoot right now:

1) When people create accounts to download data from the portal, our confirmation emails were getting caught in our mail provider’s spam filter. So for now, you can download stats without confirming your email. In the meantime we are working with our mail provider to resolve the issue.

2) Some Windows XP users who have NOT updated to SP3 may be getting either a crash or no response when they push the “Browse 10,000+ Stats” button on the application.

We are fixing these problems as we speak and a solution will be available shortly. Please check back here for updates. If you find other problems that we haven’t logged in our known issues section please contact us and tell us about it.

Thanks for your patience and we apologize for the inconvenience.

We couldn’t have done it without you…

Testers:

• Eva Gutierrez
• Jesse Linn
• Ryan Temme
• Brian Griglak
• Haven Pell
• Neville Wilson
• Nick Marcou
• Parker Goodfellow
• Matt Polak
• Eliza Barclay
• Alex Clifford
• Adam Heenan
• Nathan Mueller
• Ben Scheele
• Stefanie Turner
• Oliver Karelis
• Kenneth Ryerson

3rd Party:

• Qt
• FTGL
• Shapelib
• ffmpeg
• Bitrock
• freetype
• boost
• Postgres
• Color Brewer
• Amazon Web Services

Thanks everyone!

We’ve been hard at work for the past few months to deliver a fresh version of UUorld. 

Check it out at uuorld.com/download.

Some of the new features include:

• Custom border and stat import.
• Access to over ten thousand high-quality statistics.
• Better color and display options.
• Export to KML, SVG, and CSV in addition to image and video formats.

Enjoy!

As a kayaker, I’m always interested in how much snow or water is in a particular drainage.  So when we finished our general purpose border importer, This USGS page was one of my first stops.  I downloaded the shapefiles for both California and Colorado river basins and dug in.  I could not immediately find stats that matched up with the borders in the shapefiles so I ripped the “Discharge” and “Name” fields out of the .dbf and made a spreadsheet with those two columns.  After one failed import attempt I looked at the actual .csv in a bare bones text editor and saw that there were a bunch of junk characters appended to each field.  I copied the junk characters onto the clipboard and did a find/replace in the spreadsheet to remove them.  I then re-saved as a .csv and re-imported.  The images looked great, CO and CA respectively:



I’m not sure what “Discharge” means. They claim it’s units are CFS, but that wouldn’t make sense. In the California picture you can see that the Sierra Nevada drainages are much higher than those in the Sacramento Valley. The high alpine rivers can’t hold more water than those in the valley into which they flow. It actually looks like it’s mapping altitude in both cases.



What I need to do is match these values up with the shapefiles.  Given that they both come from the USGS this should be feasible.  But after a lot of hunting I couldn’t find the correspondence I needed. Save it for another day I guess.  But it will be cool when I get realtime updates on drainages for snow, temperature, precipitation and river flows.

While browsing through our data collection, I found myself more and more intrigued by the world airports statistic. From the sparklines on this summary page, it’s clear that some country is an outlier. By a full order of magnitude, the USA has more airports than every other major country, aside from Brazil. Let’s take a closer look.

My first encounter with the airports data was in Africa, where we see that South Africa has far more airports than any other African nation:

Then I turned to Asia, where I was surprised to see just how few airports were in China and India, in spite of their massive populations. In China, my suspicion is that the population is concentrated in the eastern cities, and, in general, the population is less mobile than that in other countries. (A bit of searching on Google suggests that China is investing heavily in their air infrastructure, and one source predicts 100 new Chinese airports in the next 10 years.)

When we zoom out to the global view (see below), it is instantly clear how the United States dwarfs other countries on this statistic. It is easy to speculate on the reason why the US has such a high number of airports: a high GDP, plenty of money for a luxury like air travel, the legacy of the Wright brothers, etc. Brazil is in second place with about a third of the number of airports of the US; Russia has about a tenth of the number of airports, and every other country has far fewer:

Curious to learn more about the distribution of airports in the US, I stopped at to pull their statistics on the number of  airports by state (below). The data I loaded shows both private and public airports. In this image, I zoomed in on the continental US, but I should note that Alaska has 732 airports and Hawaii has 54 airports.

Initially our software was designed for international comparisons and we only supported country-level data. It was a major aim of our current release to support more-fine-grained data, yet when we built the new capabilities we anchored exclusively on US data, easily available through the Census. Before release, I wanted to see if we could handle other sub-national boundaries, which led me to Afghanistan. Finding population data by province was not too hard; parsing it into an appropriate format took a bit more time, which explains the word “clean” in the image titles below — you are seeing my file name notation.

When I was finally able to load the data I was hungry to see the fruit of my labor.

And when this first image loaded up, I confess I was disappointed.

Honestly, I had been hoping for more activity; I had not expected the flatness of all provinces outside of major cities. Then I realized, the very thing I was unhappy about was new information about Afghanistan, new for me at least: Kabul is a large city; so is Ghazi; Kandahar, in orange, is substantially smaller but still a major population center.

In addition, from exploring other data I knew that if the highest value(s) are clipped, one can see a layer below the initial image. In some cases clipping values aids in removing outliers deemed inaccurate. While that step should always be taken cautiously, remembered, and ideally noted, in this case I just wanted to see Afghan population aside from the major cities, the image of which I had already absorbed.

After removing Kabul, new texture showed up in the image:

Next I clipped Ghazni, a city I had never heard of, although maybe I ought not admit such things. And, after removing Kandahar also, I could see even more demographic detail:

Looking at the value labels for each province, I was surprised to see many with a population between 50,000 and 250,000 persons.

From the first image I thought that maybe Afghanistan was mainly barren, with all of the population clustered in the cities, but after drilling in farther I saw many population centers of middling size, spread almost evenly across the country, with some concentration in the north, and with only a small fraction of provinces having extremely sparse population.

On reflection, an image that seemed initially unappealing has taught me a few essentials about the demography of a place otherwise distant and foreign. Still I will wrestle with questions about which image is more accurate: the one that shows how far the the largest cities stand out, or the one which portrays the breadth of general settlement. As with many things, I suspect the answer is that both are valid perspectives; and that maybe even better is having both images at once, a task which maybe we can entrust to our memories.

Last, a word of encouragement to anyone experimenting with the software…

Please, Dive In!

We built the software to make it easy to explore and experiment with different ways of viewing whatever data interests you. And I would be remiss if I didn’t invite you to share what you find. Almost as new parents, we are eager for screen shots that show how our software handles any data that you decide to explore.