Here's a video which summarises some data visualisation I've been doing recently. The rest of this post explains the story behind it.
This is an update of my presentation about art-science as an interdisciplinary activity, and why I like to do it, which I gave at Napier Institute for Informatics and Digital Innovation. Not very text based, so might not be clear on its own.
This page is a record of some of the field recordings I've created. So far they are all created with a Zoom H4 and homemade contact mics and hydrophones. The sounds are only processed to remove noise introduced by the recording equipment, and occasionally EQ'd to bring out certain noises.
I often find that I want to do more advanced queries of airports/cities than search engines allow, e.g. find me large cities which I can fly to from both these places and not have to travel more than 50km at the end. I'm using this as an excuse to learn:
Short version: how to view the geolocated molecules:
This is how I setup the backend for the geolocated molecules, using Hoppala to provide hosting and editing for a Layar layer. Layar indexes Points of Interest (POIs) with associated information and URIs. These can then be picked up by the Layar browser on a mobile device, in order to create the 3D overlay on reality. However, Layar doesn't host the files itself; one option is to use Hoppala, which both hosts and allows for easy editing of POIs and metadata. The stages involved are:
This is a quick mashup, to create an augmented reality layer of molecular models for the PMR2011 Hackfest in Cambridge. The idea is to locate molecules based on an important event in their history - their place of discovery or refinement, for example.
As part of the work I'm doing, we wanted to look at how likely certain areas are to be developed. We wanted to separate out areas which would definitely be developed, definitely not be developed and those which were "up for grabs".
There are several different scenarios about future development, which each provide a coherent set of parameters for the model. The model is also stochastic, so there are 30 runs for each scenario to give probabilistic futures. The task is to take the results from many runs under each condition, and produce a map showing probabilities of development.