Every year in Pamplona Spain thousands of people risk their lives for the thrill of being chased by a 1,000 lbs bull. This is known as the encierro, the famous tradition of the nine-day festival of San Fermín starting the 6^th of July.

In honor of the festival that kicked off this week, I thought that this was a great opportunity to show how Finder! can be a great trip planning tool. With the ability to download geographic datasets of your choice as well as create your own, you can use Finder! to make a custom and interactive map that displays everything you want to do and see on your trip. If you are interested in joining the chaos of San Fermines, or just want to experience the region’s famous tapas and beautiful landscapes, we have created some datasets in Finder! that can help.

Most importantly, you can upload KMLs of the route of the run and descriptions of the most dangerous sections along the route. You can use these datasets to figure out what part you want to run or just which is the best section to watch from if you plan on renting a balcony. You can also watch videos of the run to see what each section looks like from ground zero.

We also have a dataset for hotels and the top tapas places so you can find a hotel close to the run or at least one that is close to all of the bars (although anywhere in Pamplona is close to a bar!). This map, for example, shows hotels and famous tapas bars.

So make sure you reserve you hotel early and that you have your white shirt and pants and red bandana packed. Also if you plan on running, with as many as 300 injured a year and the occasional death, this is a feat to be taken very seriously (and sober!). For more information on how to participate, if you dare, check out the official Pamplona website.

(Photo Credits)

Popularity: 11% [?]

Web 2.0 has created a grand collection of buzzwords and two of the most prominent have been “collective intelligence” and the “wisdom of crowds“. Both terms are closely related and have been some of the driving forces behind the success of popular applications like Digg, Wikipedia, OpenStreetMap and Freebase to name a few. While there are many positives associated with these aspects’ crowd behavior, there are also negative possibilities.

This struck home for me on my 4th of July drive from Princeton, NJ back to Washington DC. I’d been on a lucky roll avoiding traffic and toll backups till I arrived at the Susquehanna Bridge and traffic ground to a halt with red brakes lights flaring. I spent the next 45 minutes creeping along in a stop and go morass. As we poked along I began to wonder what the cause of the congestion would be, ranging from 1) a big wreck 2) road construction 3) DUI check point 4) a deer strike 5) Britney Spears pulled over for speeding or 6) an alien landing…

Eventually I got to the crest of a hill and saw the culprit. On the other side of the interstate, going in the opposite direction, a car had been pulled over by two cops (and the occupant wasn’t even Britney Spears). As traffic came parallel to the pulled over car everyone slammed on their brakes to have a good long look then went back to regular speed - no congestion - flowing smoothly.

In economics terms this is called “rent seeking” behavior “Cutting yourself a bigger slice of the cake rather than making the cake bigger.” In this case, cars are getting their slice of cake looking at the silliness on the side of the road and the whole cake suffers from a 45 minute delay. There are lots of examples like this where a crowd results in a negative externality - often times not intuitive like Braess’s Paradox where adding additional lanes to a highway or building a new road or bridge actually increases traffic congestion. Here the crowd all takes the new or expanded route (collectively stupid) and traffic gets worse.

We see the negative externalities of crowds happening all the time whether it is in traffic congestion or financial markets. This made me wonder what the negative externalities of crowds were on the Web. Some have posited that the negativity comes in using crowds for prediction since it means the abandonment of the scientific method. Further, that innovation rarely comes from crowds but individuals (see Schumpeter) - crowds doom us to mediocrity. Keen and others have called this the “Cult of the Amateur” or “The People vs. the Expert“.

All good and interesting points, but the traffic bottle neck on I-95 reminded me of another pitfall with crowds and their potential for rent seeking behaviour. The crowd can often focus on what is directly in front of them and not the context of the bigger picture. That is effectively rubber necking. Compare it to animals that become fixated on a “bright shiny object” - like your cat or dog chasing a laser pointer spot into the wall.

We see this all the time on the Web and in the media where some small piece of information taken out of context, is spread by the crowd, and taken as fact. There are entire websites devoted to dispelling Obama myths, and even Obamapedia a wiki for correcting Obama falsehoods spread on Web. It is a bipartisan affair and the world has been convinced by YouTube clips that Bush can hardly spell his own name or pronounce the majority of English words. Taken in context both are equally preposterous, but the crowd spreads them as gospel and we all become collectively dumber.

This is also the fear I have of hyperlocal and the narrow focus media companies have on it. If we provide hyperlocal information without context we run the risk building the local equivalent of TMZ that just serve up vacuous information. Some traditional media folks I’ve talked to feel the only place their readers want a map is when it’s delivering hyperlocal information like movie theaters and restaurants near them.

When it comes to GIS there is still a prominent fear of the crowd (a.k.a. the public) especially when it comes to creating maps and data. This goes back to the people versus the expert debate above. The fear that letting the masses in will corrupt information and result in mediocrity that cannot be worked with. The conclusion by some of the GIS establishment is that you should keep barriers to entry high to keep the amateurs out. This is the debate that has been going on over on James Fee’s blog in regards to what we’ve been trying to do with Finder!. I won’t try to reproduce it here, but the debate between the wisdom and/or stupidity of crowds is alive and well.

I have not convinced myself that traffic congestion, rent-seeking behavior, crowdsourcing and hyperlocal actually all connect well together, but we’ll see what the crowd dictates (sarcasm here).

Popularity: 15% [?]

With the rise of post-partisan Obama on the national political scene, there have been sporadic stories in the print and on-line media , in Op-Eds, on the cable-news/YouTube and in the blogs; of how some influential Republicans have turned into Obama supporters, the so called Obamacans, reverse of Reagan-Democrats. Of course, not everybody is buying into the Obamacan story, considering it as a media creation or part of chaos theory. However, the recent claims by McClatchy newspapers’s that their “…. computer analysis, incomplete due to the difficulty matching data from various campaign finance reports, found that hundreds of people who gave at least $200 to Bush’s 2004 campaign have donated to Obama”, caught our eye at FortiusOne.

So, if there indeed are Bush donors who now have become Obamacans, the data-team wanted to find out where they are spatially speaking. Below are the maps of our efforts showing locations of possible Obamacans in New York City and Washington D.C. Why use the term possible? Because what is mapped are the results based on spatial join and attribute join, the later being a variation of spatial join. And the accuracy of the results of such joins is subject to the limitations imposed by the accuracy of the original data (donor addresses) as well as limitations of the geocoding operation. More on this towards the end of this post. So what is mapped are donor address matches and not individual donors.

Attribute Join
The attribute join is based on an identifier “XY” constructed from the concatenation of X and Y location coordinates of the Bush-Cheney and Obama donors, where the X and Y location coordinates are obtained by geocoding donor addresses. The attribute join resulted in 250 records across the lower 48 states, mostly concentrated in major cities of North-East and West-Coast. The results are shown below for New York city (lower Manhattan) and Wash D.C., where blue circles represent Obama donors (1,415 in D.C. and 1,825 in New York city); red circles represent Bush-Cheney donors (294 in D.C. and 419 in New York). The purple squares colocated with Bush-Cheney red circles are the XY “attribute matches.” There were 32 such locations in D.C and New York City had 85.

New York City: “XY” attribute join of Bush-Cheney donors with Obama donors

Washington D.C.: “XY” attribute join of Bush-Cheney donors with Obama donors

Spatial Join
Yet another way was to carry out a “spatial” join between location of each Bush-Cheney donor with all of the co-located Obama donors, resulting in more than 9,200 Bush-Cheney records colocated with more than 42,000 Obama records in the lower 48 states. The results are shown below for New York City (lower Manhattan) and Wash D.C., where again blue circles represent Obama donors, red circles represent Bush-Cheney donors, and the purple circles with varying sizes represent count of Obama donors that are colocated with each of the “spatially” joined Bush-Cheney donor. There were more than 1,500 Obama donors colocating with 248 Bush-Cheney donors in D.C. while the comparable figures for NY city are more than 2,030 Obama donors colocating with 303 Bush-Cheney donors.

Bush-Cheney donor locations spatially joined with Obama donors in NY City

Bush-Cheney donor locations spatially joined with Obama donors in Wash D.C.

Donor Data
You may find/download the mapped as well as other supporting datasets from the Finder! by using the key-word “Obamacans“. The supporting datasets also include spatial join of all Bush-Cheney donors for each of the Obama donors.

Read the rest of this entry »

Popularity: 17% [?]

We are excited to announce the latest update to Finder! , the first application of the GeoCommons Suite (Maker! and cartographically powered maps is coming soon).

Finder! has officially graduated from ‘private beta’ and is now open to everyone for finding, organizing and sharing geodata. Along with the ability to upload (shared or private) and translate your data between different file formats, you can also set up your own library of data.

Now you can access data you’ve uploaded or copied from anywhere with Web browser access. Free your geodata from the hard drive and make it universally accessible for just yourself or the world. To get started, simply sign up to create a free account.

While we’ve made lots of great performance enhancements behind the scenes, some new additions to Finder! that you’ll notice include:

Ø Enhanced KML Export - When exporting a KML file into Google Earth, you’ll find cool stylized rendering of your datapoints.

Ø (Beta) KML Import Support - Along with spreadsheet (csv) and shapefile, you can now import KML (2.2 support except for KML with schema tag and KML network links - in progress…).

Ø Feedback Form - Now you can easily let us know if you love it or if we’ve missed something you would find useful.

Ø User Manual - We’ve created a great user manual that will answer many questions you might have about signing up, finding data, geographic file formats, uploading/downloading data and the best tagging and metadata practices that ensure your data is valuable to everyone.

You can also sign up to receive email updates about the GeoCommons Suite. We’ll keep you posted on all that’s happening with our development and upcoming releases.

Thanks!

Popularity: 7% [?]

The weak US Dollar might have a slight positive effect on the United States’ economy by luring international tourists to the States in record numbers. It’s like the whole country is on sale, and for our closest neighbors it’s just a quick drive over the border. The Office of Travel and Tourism Industries (OTTI), run by the U.S. Government records data on monthly tourism statistics. For 2007, the rise in inbound tourists from Canada and Mexico can be seen in the following chart, and also downloaded in .csv, .kml or as a .shp file from Finder!:

As a contrast to the rise in tourists, the decreasing value of the USD (United States Dollar) to the CAD (Canadian Dollar) can be seen over the same time period (2007) in the following chart (data from the Bank of Canada):

This data would be fun to explore in Finder! where you can see not only the increase in tourism but also the location attributes of where tourists are coming from into the USA. If you want to see if the weakening of the USD to the CAD is really correlated to the increase in Canadian tourism to the United States, go to Finder! and download both datasets and run some statistical analyses. Finder! also has data on other currencies and more travel and tourism statistics.

Popularity: 6% [?]