Mapping emotions to pathways

Having the DGRs autonomously wander around their defined territory is one of the project visions I wanted to get to fast. Having the bots leave a trail of data or locations based upon their character definitions, which is feed by social media, seemed like a nice way to get to a mobile Augmented Reality.

Engineering this has been the work of Nick Renny, and his first iteration of this is now live.

Here's Nick to explain what's happening here:

DGR location is using a combination of the existing character traits defined in the Character YAML file, and Google local search and Google maps api to find locations to visit. These destinations are stored in the appengine datastore and also tweeted as a 'My Destination' tweet via the DGR sibling's twitter account. Also, the DGR sibling's twitter profile location is changed with lat, lng co-ordinates each time a destination is tweeted.

Once a new destination is tweeted and stored in the datastore the real world location of the previous destination is posted as the profile location of the sibling, this means that the physical location of the sibling is a real world place, and matches the last destination.

Each sibling's journey can be viewed on google maps as a set of walking directions via the dgr/sibling url, which will be the profile website setting on the sibling's twitter profile.

The journey can be viewed in realtime, and a street view trail can be watched via a link from the DGR sibling page - if you want to follow the sibling.

Each destination search is begun on local search by centreing the search on the last destination then searching that locale for the matched keywords. This means that the sibling is constantly on the move, to achieve a degree of random selection the resultset from Local Search is shuffled ( as Google always returns the same set ) and the new destination is found.

Because the search results are limited by Google and generally don't change for a given search in a given location, I have run up against the situation that journey's at the moment can tend to be somewhat cyclical and I am looking to refine the search algorithm to produce better results more in line with the character.

Here's the live Google map mashup from Nicks DGRs '@kidbeats'.

This may vanish as it's on a dev server - the screen shot above was taken today.

The locations are all in a datastore, so we can now reuse this for all kinds of odd investigations, visualizations and narratives. You can too - just grab all the @kidbeats traveling tweets.

For example, as Nick mentioned, using Google Streetmap, you can 'live' the journey of the bot. As seen here.

What I'm really liking the idea of, is the 'cow paths' of demographics, an aggregation of streams represented as a new path, within a new context, is being built up daily in real time. As the DGRs are built upon keyword semantics, the mapping data is a hyper-local visualisation of the conversation semantics of social media, in real time.

Cow path analysis in (web) search is often used to find the optimized route between two things. Not sure what our two things are here - reality and narrative? people and person? Destination or arrival? Only one way to really find out is to keep playing.

In the meanwhile, @kidbeats is wandering around somewhere.

This is sure to tickle the @herdmeister.

Respect to Nick!