Visualization of uncertainty and analysis of geographical data

Abstract

A team of five worked on this challenge to identify a possible criminal strucutre within the Flitter social network. Initially we worked on the problem individually, deliberately not sharing any data, results or conclusions. This maximised the chances of spotting any blunders, unjustified assumptions or inferences and allowed us to triangulate any common conclusions. After an agreed period we shared our results demonstrating the visualization applications we had built and the reasoning behind our conclusions. This sharing of assumptions encouraged us to incorporate uncertainty in our visualization approaches as it became clear that there was a number of possible interpretations of the rules and assumptions governing the challenge. This summary of the work emphasises one of those applications detailing the geographic analysis and uncertainty handling of the network data. ©2009 IEEE