Clare Davies

This talk had four main areas: the lessons we've been learning from visual attention experiments in recent years (upper left); our growing understanding of the geospatial knowledge that people bring to bear about an experienced environment (lower left); specific issues around users and tasks, specifically concerning expertise and age (lower right), and some tentative research agenda items that arise from all this (upper right).

Clare Davies

So, the basic idea was to try to see some new hints or insights about the person holding the mobile device, or selecting among those layers of GI data.

Clare Davies

Part 1: human visual attention, as applied to geographic visualisations. (Most of my own work on this particular area has been with aerial imagery, often combined with an outline map overlay.)

Clare Davies

There's been a lot of interest in visual salience maps over the past 15 years, but they seem to predict only the first few hundred ms after someone first sees an image, without expecting what would be in it, and without having a particular task to do with it. Clearly, that's not the scenario of most of our users, most of the time - and they also have more time to find what they need.

Clare Davies

The backlash against visual salience has perhaps gone too far the other way in some respects: De Graef's statement is one I'd very much disagree with. Except when watching movies or TV, or walking through a strange town, most of the scenes we see are pretty much what we expect to see next: we don't have to explore them for meaning at all (and may not even pay much attention to them). It's not like we're going "Hey look, a street! I'm suddenly in a forest! Oh, there's a map on my GIS screen!"However, the three stages suggested by DeGraef as what we do when we are faced with an unexpected image or scene are quite useful: we probably do apply top-down 'schemas' from our past knowledge, in deciding where to look and what to pull out of the scene.

Clare Davies

It's tempting, then, to start seeing human visual attention as entirely top-down (expectation)-driven. However, visual salience (in other words, good design - highlighting the important stuff with colour, contrast etc. as we've all been taught to do) does help even in expert scenarios, as these graphs from one study show. If the item that people had to spot change in, or recall later, was close to a cluster of more visually salient items, even expert performance improved (this was photogrammetrists who do such change detection as a major part of their job, versus relatively novice viewers of aerial imagery).

Clare Davies

At this point, I showed a brief video of an expert change-detection photogrammetrist, looking over some British suburban aerial imagery to spot changes (comparing both building outlines and street features). I showed that although he chose to scan systematically up and down the streets, a lot of the work he was doing used his peripheral rather than foveal vision: this is an important feature of attention that eyetracking studies can't capture. Also, a common problem with dynamic attention tasks such as this is that people aren't completely reliable in recalling where they've already looked (hence this user's attempt at a systematic strategy - but this falls down when there is multiple panning among screenfuls, as here). Thus things can be missed, even by expert users. Still, visually salient items can help to efficiently direct the eye around a visualisation.

Clare Davies

And so to part 2, the spatial cognition that may lie behind/above our basic visual processing.

Clare Davies

Of course, our visualisations are 'geo' - that's what makes them so interesting… so what do we know about human understanding of (experienced) environmental space?

Clare Davies

In 2014, the Nobel prize for medicine and physiology was shared between scientists who had discovered place cells in the rat hippocampus - which 'learn' to fire only when the rat reaches a particular location within a well-learned maze - and grid cells, which fire at a regular series of locations across the space, as if forming some sort of coordinate system. Left out of the Nobel laureate, but potentially equally important, were 'head direction' cells, which only fire when the rat is looking in a certain direction.

Clare Davies

How the international media reported the story… but how come we're actually not very GPS-like? What else is going on? First, it's important to remember that place and grid cells have that amazing accuracy only in very, very over-learned spaces. Prior to (and as well as) that, we need other sources of help.

Clare Davies

Paul Dudchenko's book is a useful summary of some of this. In particular, and unlike a GPS, there's a larger focus in human navigation on visual landmarks, and on the route topology (not always accompanied by accurate metric knowledge).

Clare Davies

There seems to be a particularly strong brain pathway that simply links what you see right here, with where to go next. There are even 'conjunction' cells that link head direction with place - i.e., that fire at a particular spot *only* if looking in a specific direction there. So, until our place cells are fully tuned for a given environment, this may be one key source of help.

Clare Davies

OK, so we could crudely think of our spatial knowledge as only loosely metric (preserving distance and direction), but more strongly topological. Reminds us not of topographic 'maps', but of...

Clare Davies

Topological maps deliberately (unlike us?) lose some of the metric accuracy of the real space. If you look at this version of it, the dotted lines show stations which are actually only 5-10 mins' walk from each other.

Clare Davies

But look what happens when someone tries to make the map closer to the real spatial distances and directions among stations (though this version still had to distort to leave room for the names). It immediately looks more complex to us, and this hints a reason why our brains rely on topological simplifications of space: the principle of 'cognitive economy'. It's a lot more efficient! Thus, good visualisations of a space that users have/will also experience (which, of course, isn't always the case in geovisualisation) need to reflect the key aspects we use to orientate: landmarks, and connections.

Clare Davies

Part 3 of the presentation considered some human factors issues which are easily overlooked in our user studies and design processes.

Clare Davies

Users may, or may not, know the actual space being depicted, as discussed above. Even if they don't, they may have (perhaps *should* have) some expertise in its geography: the types of features it contains, and the ways they do and don't relate together. Outsourcing GI data creation or visualisation to cheaper workers in another country, for instance, has been known to lead to expensive disasters where the cultural knowledge of the geography was lost. On the other hand, expertise in the type of visualisation may often be overplayed, though sometimes important. In my own work I've sometimes *not* seen any expert-novice differences with a specialist type of mapping, just because people adapt to visual representations surprisingly quickly. Finally, while academics have liked to muse over what's "special about spatial", the USIS work of the early 1990s (Davies & Medyckcyj-Scott 1994, 1996, in IJGIS) showed that often users are more cognitively overloaded by their complex GIS or other task and system issues, than by the map.

Clare Davies

Age is an aspect of individual differences which has often been relegated to studies of the elderly. But work such as that by Tim Salthouse at Virginia has shown more recently that actually, many of the effects shown here (especially cognitive abilities) tend to start falling off in our 20s, and in the past this was masked by test-retest practice effects - i.e., you'd done the test before (or similar tasks) so you seemed to do just as well as before, even though age was actually making you slower. Thus we should be designing, and user testing, NOT just with 20-year old undergraduate students.

Clare Davies

A few more, slightly random, thoughts based on my experience of studies in this area. To my knowledge, few geovisualisation-using tasks in the real world need to be done in just a few seconds, yet a lot of what we've done to establish rules of visual attention, perception and cognition was done by comparing reaction times in milliseconds (as with the visual salience work above). Screen size and zoom level, on the other hand, are an under-researched issue: there's a bit of evidence that people working constantly at a zoomed-in level won't build such an integrated overview of the space (unsurprisingly). The user interface is often more of a problem for users than the map is, if poorly matched to the task, and often the task that users have to do with geovisualisations is a rather mundane one of matching geometry, colour etc. to a paper plan: these tasks aren't actually 'geographic' at all in their requirements. HCI has many useful tools for task and user modelling (see any HCI textbook): I'd recommend considering these for geovisualisation design too.

Clare Davies

Finally, just a short 'wishlist' of research I'd like to see, that would help us to see where the issues I've raised here do, and perhaps don't so much, matter for particular types of visualisation and application.