When we started the very first iteration of GeoCommons in 2005 folksonomies were all the rage and we jumped on board using tags to organize the geospatial data that was pushed into the new platform. During the time we had the prototype deployed we ran into many of the same issues other applications have found with folksonomies

1) people’s tags may be difficult for others to understand,
2) people may have tagged items inappropriately for others’ needs.

In short your users will not always implement tags in ways that are productive for the community – in the extreme resulting in Flickr’s 20 million unique tags. How many of those 20 million tags are misspelled words or so off the path they never get found.

In addition to the problems you encounter with folksonomies in general you have the further complications of geopspatial data. All geospatial data sets have location tags, but adding them in an unstructured way creates enough chaos that it is very difficult to leverage location tags in a thorough way. Secondly many potential users do not know the variety of geodata available. Put more simply they do not know what to search for, and having the ability to browse through data by topics is appealing.

Despite the downsides of folksonomies they are incredibly powerful and have been hugely effective in organizing vast amount of data on the web. So, as we worked on the next iteration of GeoCommons we started looking at possible hybrid approaches to folksonomies and hierarchies.

Specifically we looked at the two problems specific to geospatial data listed above 1) place tags and 2) organizing data for browsing. Solving the problems required both short term and long term solutions.

Fortunately we had a small advantage over many crowd sourced project in that we have a full time data team. They are a great group of folks that spend their day finding cool geodata and coming up with clever ways to organize it.

Through the data team and the other community members that contributed data to the first iteration of GeoCommons we had a big pool of data with a wide variety of tags to examine. What we found were some distinct trends in the tagging and titling of data. Across the data there were a commons set of tags that broke the data up into a useful set of distinct categories, but there were also many data sets that were tagged with elements that made them often indiscoverable. After the analysis we started to look at structures we could establish to help create self similarity in tagging that still had the flexibility to be adaptive.

The result was the creation of a location and topical taxonomy based on our existing corpus of data that has the intelligence to adapt as the content grows and evolves. I can’t go into the technical details in depth, but fundamentally the concept is to intelligently leverage the taxonomies and structures to provide suggestions to users to tag their data better.

In many cases this can be very simple – like providing tips on how to tag and title effectively to make your data more valuable to the community. For instance with titles we found across GeoCommons there were four key pieces of information used for datasets in the past.

1) Source name, 2) Original Name of Dataset from Source (or short description of dataset) 3) Geographic Area, 4) Time period of data

Examples:

Communicating this effectively to users is a great way to get better consistency across data contributions, while still allowing flexibility for users to be creative and bring in information that does fit the rigid mold of a hierarchy. Of course this is the most simple and you can get far more clever.

Del.icio.us for instance has a great feature that notifies a user they are putting in a new tag no one has used before and asking if that is what they meant to do. You can also suggest tags from your taxonomy that are semantically related to the data the user is contributing. This creates a consistency across tags that makes data easier to find as the system scales to larger volumes.

The nice thing about taxonomies as opposed to folksonomies is that they can be structured as trees, which means you can compute across them quite easily. With a solid and adaptive taxonomy in place you can go a long ways in intelligently guiding users towards creating better and more consistent tags. At least that is what we think and it will be fun to see how it works out after the launch.

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Popularity: 11% [?]

First off want to thanks the folk that commented on the last post. Lots of useful feedback and it also highlighted a bit of confusion I created with the first post. The purpose of the first post was not a proposal to create a new metadata standard. Instead it was simply a proposal of how we could map the metadata we collect in GeoCommons to existing standards.

From that standpoint the proposal is for an implementation not a standard. We have just about 5,000 unique datasets and about 70,000 data layers, and it would be great to expose useful metadata for the data. The data covers the gambit, from EPA toxic release sites to the number of Facebook users by city. The system and metadata requirements needs to be flexible enough to accommodate both a user uploading Facebook data and one uploading EPA data.

While GIS users might not be intimidated by a metadata form with 75 or even 335 elements your average Web/GeoWeb user definitely will be. The goal with GeoCommons is to provide a destination where both communities can consume and share data, and I think both communities will find tools and data that are useful.

In regard to the metadata elements we proposed to map to in the last post, we were looking for those that both technical and non-technical users would understand, and also automatically trap as many additional elements as possible. To cover technical users, that have a full compliment of metadata, the plan is to have an element where you can you can provide a link to a full metadata specification.

The comments directing us to the ISO 19115 standard were very useful and we are looking to see what elements we are missing to map to that standard as we evolve. The thing we want to make sure we get right is finding to best set of metadata elements to request from users. Balancing the fact that if we have a huge number of elements, most people are going to go running for the hills.

Right now it looks like we’ll have 17-20 elements that will map to Dublin Core, FGDC, and in a next release ISO 19115. So, for each data set in Geocommons you’ll have a page that lists those 17-20 elements in the metadata format technical folks are used to seeing. This should also provide a means by which to explore federating the data with other applications and search approaches.

The goal here is to create a bridge between content being created for the GeoWeb and content created for the GIS world and make both usable and remixable by the web community as a whole. I fully respect the motivations and requirements for the GIS metadata specifications out there, and I hope we can leverage them to create an implementation that will see a high level of adoption.

Without adoption standards are pretty hollow as we’ve seen with all the work that went into GML versus the much lighter specifications for KML and GeoRSS. While both have their place it is clear what the market is supporting. As more geospatial data is created outside of the government we are not going to have the government mandate to force metadata creation and what the market accepts is going to become increasing critical – IMHO. Look forward to getting more feedback as we get ready to launch.

Popularity: 13% [?]

One of the criticisms we received when we launched GeoCommons was the lack of metadata for the content we had collected. Since then we’ve been looking into what would be a reasonable approach to implement metadata for the GeoWeb.

When it comes to GIS data the existing standard is the FGDC’s Content Standard for Digital Geospatial Metadata (CSDGM). The standard calls for 335 metadata elements to describe a geospatial data set, which covers a wide variety of descriptions for the data. The one thing that came clear very quickly was that the FGDC CSDGM is far too onerous and outdated for the GeoWeb. For instance in the FAQ provided by the USGS they recommend you hire a full time person to create your CSDGM compliant metadata:

Who should create metadata?
“Data managers who are either technically-literate scientists or scientifically-literate computer specialists. Creating correct metadata is like library cataloging, except the creator needs to know more of the scientific information behind the data in order to properly document them. Don’t assume that every -ologist or -ographer needs to be able to create proper metadata. They will complain that it is too hard and they won’t see the benefits. But ensure that there is good communication between the metadata producer and the data producer; the former will have to ask questions of the latter….”

In a GeoWeb where self publication is a key innovation the model of having a full time metadata guru is antiquated. A specification with 335 elements is antiquated. The mantra that “certainly if there is no pain, there will likely be no gain” when it comes to metadata is antiquated. The end result of these draconian approaches to metadata is about a zero likelihood the GeoWeb will implement them.

This is a shame because metadata is very useful, especially when it comes to describing, finding and federating data. This is one of the shortcomings of KML – little/no metadata (although several argue it has no place in either of these formats). GeoRSS has limited metadata support with “Feature Type Tag” and “Relationship Tag” which are useful, but fairly confined.

The question we faced with rebuilding GeoCommons – is there a middle ground between 335 elements and two elements? Fortunately we were not the first to look at this issue. In 1995 a bunch of librarians got together to devise an approach that “provides a simple and standardized set of conventions for describing things online in ways that make them easier to find”. The fifteen elements standard they devised is called Dublin-Core and is widely implemented across the web. If the librarians could come up with 15 core elements then surely the GeoWeb can, and even make those map to the Dublin-Core standard and the FGDC CSDGM standard. So, after a good bit of work here is what we would like to implement as a lightweight core set of metadata for GeoWeb data:

This covers seventeen elements about half of which we trap automatically. You can map them to either FGDC or Dublin Core thus giving you the ability to expose your data to the GIS world and general web community in a straightforward manner. As with any metadata standard you do not need all seventeen elements, but the more you populate the more useful the data becomes. The metadata could be exposed as microformats enabling a number of possibilities for discovery and potential federation. This could be particularly interesting with Yahoo! opening up their search to support Dublin Core vocabularies and microformats. Our feeling is that the more data we can make available on the web the more problems everyone can solve. We’ll be testing this out when we launch the next iteration of GeoCommons at Where 2.0 and would be great to get feedback and thoughts on the approach.

Popularity: 16% [?]