How many years have I been waiting for a fast, powerful, networked geospatial client with an open data format? Maybe this time. Google Earth could wipe other suitors off the proverbial map.
Nature magazine features an article on a dynamic map of Avian Influenza developed by Decalan Butler whose blog is here.
The visualization of avian flu outbreaks is the first online map, to my knowledge, of each of the more than 1800 individual outbreaks of avian flu in birds that have been reported over the past two years. It also provides a geographical overview of confirmed human cases of infection with the H5N1 influenza virus.
Of particular interest is the ‘network links’ feature which keeps the map updated with new information. The Google Earth client on your desktop can be set up to poll the network link periodically for new information. I can imagine this data streaming system supporting a whole new set of popular spatial events, particularly car and ocean races and journeys.
GBIF (Global Biodiversity Information Facility) has been playing with Google Earth too, and enabled searches on museum collection records to be output into a Google Earth formated ‘kml’ file.
Integration with niche modeling
An obvious extension would be to enable prediction of the distribution and overlay on the map as well. There is currently no predictive modelling in the platform, being limited to display of basic types of geographic information. Basic GIS functions would be invaluable.
Ecological niche modelling would be farly easy to inplement in a clunky way, by entering the locations of data files in an html form in another browser window outside Google Earth. It would be much better to be able to stay entirely within the Google Earth client. But from what I have read, the client can only pull down data, not push it out to a server. Kml files can only be saved to disk at this point. Real two way communication would be another great leap forward.
The KML (Keyhole Markup Language), an XML-based file format, would support the necessary types for niche modeling: a raster (called a ground overlay) and points (called a placemark). Here is an example of the code for a ground overlay. Confusingly, the location of the actual raster image is stored in a tag called Icon. Everything else is self-explanatory.
Another useful extension would be to tie it in to web pages, such as the daily updates on Avian Influenza produced news. This listing of new articles, web pages, movies, images and news items that have just appeared in the top ten of yahoo searches could be georeferenced and passed into a kml file, referenced by a link on the web page. The difficult part would be accurate geo-referencing, either from text, IP addresses or a combination of both. I think we have a long way to go yet before there is sufficient metadata for this to be automated reliably.