Exploratory spatio-temporal visualisation

The last paper I reviewed discussed and evaluated different visual querying techniques for general relational databases. Today's paper, Exploratory spatio-temporal visualisation: an analytical review by N. Andrienko et al., narrows the discussion to just spatio-temporal data and focuses less on querying specifically than on evaluating a variety of exploratory techniques specific to spatio-temporal data.

The paper begins with a long discussion intent on classifying the kinds of questions one can ask of spatio-temporal data. The authors point to spatio-temporal data having three major dimensions: what, where, and when, and that a classification system natural falls out from this observation. Questions about the data are usually posed about one dimension given the other two (e.g. when did x happen at y?) with answers needed at various levels of aggregation (e.g. at specific points, over a range of points, or overall; they refer to this as the "search level") and with various types of comparisons/relations done on the results (they're rather vague about this bit). At the end of their discussion the authors decide to explore only those questions that focus on the time dimension: given a time (or range) what happened where?; or when did what happen where. Here's their handy graphical representation of all of this:



The paper then turns to briefly surveying existing exploratory techniques: querying (they focus on dynamic querying and filtering), map animation, and other visualisations to explore changes in locations, events, or attributes.

With these techniques in hand the authors get to the real meat of the paper and evaluate how each technique serves to answer the two general kinds of questions. In the process of doing so they to break down the two question types into more specific questions types, detailing various search levels and cognitive operations. For instance, an elementary when -> what + where question might involve comparing behaviours over the same time interval (e.g. "compare the movements of stork X and Y") or at distinct intervals (e.g. "compare the migration behaviours of the stork X in the years 2001 and 2002"). Each of these question subtypes are explained and matched to appropriate exploratory techniques, often with references to existing implementations.

This paper is another (seemingly b/c what do I know?) decent survey of existing software and techniques. It presents a slightly more principled classification (the question types) and evaluation criteria but is fairly light-weight on the analysis (it all seems rather ad-hoc), and provides no empirical backup for many claims.

There is one thing I can take away from this paper: that visual querying is more than just a visual represention of the query question. It's also exploration. This paper implicitly assumes this with its highlighting of techniques which blur the visualisation of the query and the results, à la dynamic queries.

The other take away is dead obvious but I'll state it anyway: the type of questions you're asking tell you (or contstrain, or inform) what exploratory/query techniques to use. Creating a novel SQL query visualiser might not be what climate scientists or grade 10 students need to answer their types of questions. It's worth mentioning all of this only because it highlights an important point for me: I need to get a handle on what questions folks can't answer easily (or aren't asking, but ought to).