It is only fitting that the day after I posted “Moving Push Pins Off the Map” I saw the post on Ogle Earth about a new geotagging icon….which is?

A GIANT PUSH PIN!

With my interest peaked we did a little digging and found another geotagging icon:

ANOTHER GIANT PUSH PIN (actually when I dug into it this icon was a first version that evolved into the red one.)

I of course blame this all on the Google monolith for perpetuating push pin mania. Last time I saw Mike Jones he even had a push pin tie tack. Joking aside the reason for creating a geotagging icon itself is worth discussing.

The stated purpose on the GeoTagIcons.com website is “The Geotag Icon is intended as a web “standard” icon for identifying geotagged content to humans.” So, if a photo or blog post has been geotagged then there is an icon on it to let you know. The thought being many times geotags are hidden in microformats or the URL, thus not visible to the user.

This seems like a straight forward approach to the problem, but also seems to have overlap with existing icons such as KML and GeoRSS. The tutorial on GeoTagIcons has examples of using it for links to both KML and GeoRSS content. This could lead to some ambiguity and confusion for users.

One of the most interesting parts of the pitch for using the GeoTagIcon is, “Reason 4: It encourages development of the semantic web”. On first blush this got me excited, but reading a bit deeper realized they meant it acts as an advertisement for linked content that could help support an evolving semantic web. This is in and of itself is a worthy cause and advertising has been directed at far less useful goals.

The link between geotagging and the semantic web does bring up a good topic for debate. How will all these geotagged objects (KML, GeoRSS, geo-microformats, GPX, etc.) be tied together in a method that creates semantic meaning? What questions will the semantic technologies answer? The GeoTagIcon site provides an example of , “Show me a plot of other bloggers in my vicinity”, or “I’d like to see a map showing which of my friends have also visited Australia”, “Who else has photographed this location?”, etc.

While these are interesting I think the examples and the direction many folks are taking geotagging misses the real potential of the semantic web. The geotagging premise is based on doing increasingly sophisticated things with geo-coded annotations – 99% of the time taking the form of a pushpin. In each of the examples above users or a screen scraper and geo-coder (most likely) have added a latitude and longitude to a piece of unstructured data (bloggers, my friends, photos). While this all useful information it is often relegated to only answering trivial questions.

There is only so much you can do with a bit of unstructured text or html that has geographic coordinates. You can measure vicinity (bloggers nearby), intersection (friends that have visited Australia) and union (show me all photos from a location). There might be a few that I am missing but it is fairly small universe of questions that can be answered, and the semantic web is all about answering questions. Hopefully a very large universe of questions.

From my limited perspective the semantic web is all about bringing vast data resources to the web in an easy and intuitive way. While turning unstructured text into geocoded annotations already on the web is important I think the bigger challenge is blending existing structured data (largely in databases and not on directly on the page web) with organized unstructured data through the web in a seamless way like we have for text, pictures and video.

Metaweb has done some compelling work with Freebase. They have even been doing some interesting geo work with their database. To date Freebase has largely been working with conceptual data, but from the look of their GIS app could be getting into more quantitative data.

As you get into quantitative data the power and tools available for asking sophisticated questions increase exponentially. Unfortunately so do the technical challenges, both computational and creating an intuitive user experience for something not intuitive to most people – numbers, math, statistics, etc. Despite the challenges I think this is where some of the greatest potential awaits for the emerging semantic web. That said I do think the new icons are quite nice and serve a useful function – despite the push pin.

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Popularity: 22% [?]

During a late night epiphany we decided the blog had gotten a bit stale. So, to encourage a regular flow of content we figured a new look and pithy title would be just the trick. Welcome to the shiny-new, rebranded, USGS approved “Off the Map”. Now fortified with vitamins, minerals, insight, and elegant prose.

Why change the title to “Off the Map”? Well push pins seemed so 2005 and we needed another reason for an office contest. The winner you’ve now seen, but there were lots of other great entries such as:

1. Geo Me This
2. Plain to the Simple
3. MapRap (bling your map ????)
4. Map This (including middle finger to the man* graphic)
5. Libre la Data
6. The Lat and Long of It
7. Atlas Maximus
8. Adept and Disheveled

Why did we end up picking “Off the Map”. Well speaking for myself I just wanted to be able to use Kyle’s graphic with the dead push pin.

The next reason? As we’ve been developing the second generation of GeoCommons we found the big areas we were having to innovate had nothing to do with the map. The new ideas that were going to change the way people use maps – were literally “off the map”. Whether it was handling large datasets ridiculously quickly in a browser or structuring taxonomies and semantic relationships we were increasingly putting lots of resources into data management. Just so happened that data could be shown on a map.

Don’t get me wrong the map is still the single interface that ties all the data together, but increasingly I think what will make the GeoWeb matter has less to do with maps (including all sorts of crazy 3-D worlds) and more to do with delivering useful data to help people make better decisions. Which happens to be done through a map.

We should be getting a couple of posts up a week explaining this line of thought in more detail. Most likely with several side trips of randomness and entertainment. So, please stay tuned and we promise to keep a regular flurry of GeoWeb bits o’ knowledge.

* “The man” of course being all those evil cabals preventing easy public access to open data

Popularity: 10% [?]

I got a bit wrapped up trying to get a side project finished up yesterday, so I’ll just skip to day three of ETech. The morning opening speakers were better that Day Two, although the session thus far have been a bit below Day Two’s. We kicked off the morning with an abbreviated talk by John McCarthy (father of LISP) on a new language he’s working for several years called Elephant. The elephant name coming from the fact it never forgets, and the broad concept of a semantic programming language that can create structured relationships from natural language. Unfortunately he ran out of time before he really got into the guts of it, but there were some fascinating concepts with how natural language can be leveraged in a structured way to do computation. Definitely something worth looking into more, and it reminded me a lot of our thoughts about a context driven architecture and natural language for data. Although we were looking to turn quantitative data into natural language versus turning natural language into data.

Following McCarthy’s talk there were some interesting bits on open source personal robots, then an informal launch on Yahoo’s Fire Eagle. Fire Eagle has taken some flack in the blogs for having minimal or “zero” functionality. I think this misses the point of what Fire Eagle is intended to do. My impression was that Fire Eagle is not meant to be a stand alone consumer application but a straight forward tool that does a simple thing very well. That simple thing being a platform for sharing your location online. The functionality folks are clamoring for is left to the users and developers and I think there are good number of fun possibilities here. For instance with GeoCommons we have big pile o’ data and would be very useful to personalize that data delivery to a users location, or have user have the ability to comment on that data from their location and have that comment geo-located. This creates a dependency on clever users, but form what I’ve heard floating around ETech there seem to be a good number of clever ideas floating around.

The last session of the day I attended was Mikel and Jesse’s presentation on “Disaster Tech”. I’d seen Mikel’s presentation at the State of the Map conference on open source disaster technology, and it was cool to see how the project has evolved. The whole topic is something close to us, especially getting up close doing disaster response after the London Bombings and Hurricane Katrina. The presentation has some great examples of Open Street Maps, Twitter and Google Maps being used in creative ways during disasters. Mikel gave a nice example of using the USGS GeoRSS earthquake feed, the EU lightweight tsunami propagation model and a feed to republish the resulting polygons as GeoRSS. With this approach they can churn out a polygon warning area in under a minute. A similar concept is seen at the United Nations – GDAC application.

All great stuff for ad hoc implementation that is cost effective and not over engineered. Lots of good discussion of how take the information produced by technology and effectively transmit it to non-technical or completely unconnected people. Also Jesse and Mikel had a nice bit at the end of the presentation on anti-patterns – i.e. what happens when you don’t have a champion for the technology to create repeatable and successful implementations. Specifically the case of the search for Steve Fosset where the crowd sourced help to find him actually slowed down the search and rescue teams having to deal with all the input. Resulting in the emergence of champions like InternetSAR that creates a structure that could be replicated and effective for search and rescue. Lots of good thought on an important topic

Popularity: 14% [?]

We’ve made our way to sunny San Diego for ETech and kicking the conference off with tutorial sessions (where you actually get to learn how to do the stuff presenters are forced to gloss over in 15 minute talks). Today we have six total tutorials in two blocks, so you get to pick two to participate in. I went for Stamen’s “Live, Vast and Deep: Web-native Information Visualization” and “Kitchen Hack Lab: Food Hacking for Techies”. The viz tutorial is right up our alley and the food hacking is purely for fun.

The Stamen presentation covered lots of good ground with several examples for creating useful visualizations of data. One of the first themes was “show all the data” whenever possible, so that you can create context for the user. If the user can see all the data they have a reference to start with – like the Zipdecode project. A great visual example of this is Curtis Jordan’s work visualizing volumes of trash. Here are the 2 million plastic bottle Americans consume every five minutes:

And zoomed in for perspective:

Two great examples of how visualizing large amounts of data can make a very dramatic point. Doing this on the web is more challenging, especially when you start to delve into very large data sets. Stamen has several innovative experiences with this whether it is Cabspotting or Oakland Crime Crimespotting. As we’ve been plugging along on the second generation of GeoCommons we’ve been wrestling many of the same issues – boiling down to how could you enable anyone to make a Crimespotting style map in just a few clicks with any variety of data. Democratizing the data is one side of the equation, but equally challenging is democratizing the ability to make dynamic visualizations of the data without needing to be a cartographer or a very clever Flash developer.

Regardless of the end audience – Stamen pointed out some very useful ground rules for data visualization capabilities:

HTML/Javascript – handles 100-1000 data points – loads in .1 seconds
Flash – handles up to 10,000 data points – loads in 1 second
Java/Processing – handles up to 100,000 points – loads in 10 seconds
OpenGL – handles upwards of 1,000,000 points – loads in 100 seconds

A fairly rough rule on thumb , and indicative to Stamen’s use of Flash and Processing. I’d heard a bit on Processing, and after the discussions and demos will definitely need to investigate more. An open question – is it a wash between Flash and Processing for practical web implementation? If Flash handles 10,000 points in one second and Processing handles 100,000 in 10 seconds does that extrapolate to Processing handling 10,000 points in one second as well? If that is the case is it essentially a wash between the two since no user is going to wait 10 sends for anything (yes we learned that the hard way . We’ll be learning a lot more over the next couple of months and will share what we sort out.

Popularity: 11% [?]