Moxie designed a demonstration to show how integrated geo spatial service, RIA technology, location based service and digital mapa can make life easier. From that, a geo spatial service was developed that enabled Flex Yahoo AS3 map application.

Virtual Earth has been updated to include new imagery, new 3D buildings, direct support for MapCruncher, movie capture, export to KML and GPX files, and more.

Geomantic shares some coverage of geospatial topics in the Washington Post the past week including a story on Yahoo! Maps Live and a mashup from the Center for Neighborhood Technologies.

GISLounge and the Daily ACK announced that KML is now an Open Geospatial Consortium Standard. This means that Google will no longer be responsible for maintaining the KML file format which, instead, will be handled by OGC. KML (Keyhole Markup Language) is a file format that uses XML-based language to manage geographic information.

Flowing Data provides a list of data visualization blogs you may not know about, including Strange Maps, Well-formed Data, Random Etc, Serial Consign, and AnyGeo.

Popularity: 11% [?]

We got a bit of good news this week making the cut as a Red Herring Top 100 finalist. Considering there were over 800 companies in the running maybe this is not so bad. The folks in marketing are earning their ducats for sure. All the details can be found here.

In the bigger picture I think this is a good indicator of how the GeoWeb is becoming more mainstream and embraced by a larger business community. Hopefully this is one of many indicators pointing towards more business opportunities and investment dollars for the GeoWeb.

Popularity: 8% [?]

I got an email from the geowanking mailing list this morning that Mike Liebhold had posted about a talk at UC Berkeley on “Digital Neighborhood Mapping”. The talk is to be given by Factle Maps on their 15 step approach to creating neighborhood boundaries. I did a little digging into Factle Maps and found some interesting tidbits revolving around a dispute between them and Maponics about copyright infringement on data that landed in federal court. Another illustration of how ownership rights to data can get very messy and one of many reason we and many other have gone the open data route.

When it comes to neighborhoods Zillow created a great resource by releasing all their neighborhood boundaries as shapefiles under a creative commons license. The geowanking email got my attention because we’ve been mapping several attributes like political campaign contribution data to the Zillow neighborhood boundaries. As I did more searching I saw more feedback on the community’s frustrations with access to neighborhood data. Zillow addressed much of this with their release but there is still an issue with keeping it up to date and evolving the growth of it.

Seems like a natural opportunity for an Open Street Maps type approach fused with some easy polygon creation tools like ShapeWiki. We’ve kicked around having a general set of geometry editing tools, but this seems like a specific enough of a project to have a dedicated project built around it. Then you could map a variety of data to the new boundaries to provide context. Creating a tool to map data to arbitrary boundaries would be doable as long as the right math and rule set was implemented. Random thought for the day.

Popularity: 9% [?]

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.

Popularity: 24% [?]