Visualization Tools for Understanding Big Data by James Cheshire.
From the post:
I recently co-wrote an editorial (download the full version here) with Mike Batty (UCL CASA) in which we explored some of the current issues surrounding the visualisation of large urban datasets. We were inspired to write it following the CASA Smart Cities conference and we included a couple of visualisations I have blogged here. Much of the day was devoted to demonstrating the potential of data visualisation to help us better understand our cities. Such visualisations would not have been possible a few years ago using desktop computers their production has ballooned as a result of recent technological (and in the case of OpenData, political) advances.
In the editorial we argue that the many new visualisations, such as the map of London bus trips above, share much in common with the work of early geographers and explorers whose interests were in the description of often-unknown processes. In this context, the unknown has been the ability to produce a large-scale impression of the dynamics of London’s bus network. The pace of exploration is largely determined by technological advancement and handling big data is no different. However, unlike early geographic research, mere description is no longer a sufficient benchmark to constitute advanced scientific enquiry into the complexities of urban life. This point, perhaps, marks a distinguishing feature between the science of cities and the thousands of rapidly produced big data visualisations and infographics designed for online consumption. We are now in a position to deploy the analytical methods developed since geography’s quantitative revolution, which began half a century ago, to large datasets to garner insights into the process. Yet, many of these methods are yet to be harnessed for the latest datasets due to the rapidity and frequency of data releases and the technological limitations that remain in place (especially in the context of network visualisation). That said, the path from description to analysis is clearly marked and, within this framework, visualisation plays an important role in the conceptualisation of the system(s) of interest, thus offering a route into more sophisticated kinds of analysis.
Curious if you would say that topic maps as navigation artifacts are “descriptive” as opposed to “explorative?”
What would you suggest as a basis for “interactive” topic maps that present the opportunity for dynamic subject identification, associations and merging?