Desire paths are the worn tracks you sometimes see, cutting across a patch of grass, or through a hedge, where people frequently take a shortcut, rather than the designated path.
In terms of design and usability, perhaps we could begin to think about this when we create websites and applications. If we learn from what users actually do, we can aim to create better paths to data and information, and thus a better user experience.
Getting from A to B
Desire paths, sometimes called social paths or lines, are the routes that people (or animals) take to get from point A to B. These routes, though, might not follow designated paths set out by planners. “The path usually represents the shortest or most easily navigated route between an origin and destination. The width and amount of erosion of the line represents the amount of demand.” [http://en.wikipedia.org/wiki/Desire_path]
We can see evidence of desire paths, here on the Genome Campus. There is a shortcut through the hedge, next to the Portakabins outside the EBI, going to and from the restaurant and underground parking. Originally, this was just a gap that people had forced through the bushes, but now it has been paved, and has become a “real” path.
Planning paths and navigation
So how about actively using these desire lines to help plan paths through a physical space… or a website?
A real-world example of Finnish planners looking at what people do illustrates this very nicely:
In Finland, planners are known to visit their parks immediately after the first snowfall, when the existing paths are not visible.People naturally choose desire lines, which are then clearly indicated by their footprints and can be used to guide the routing of paths.
Observe and respond
How can we apply this to websites?
If we gather together the data and records we have for what users do on our websites – typical tasks and scenarios, common activities and goals – we could use these to dynamically provide better or more appropriate routes to data and information.
This might involve things like:
- analysis of server logs
- looking at common search terms, and which results are followed
- surveys (to find out user attitudes and opinions)
- user testing (to find out about user behaviour, and what they actually do)
Gathering together this quantitative and qualitative data and evidence would allow us to tailor pages and interfaces to user needs in a more dynamic and responsive way. This could be especially powerful in the case of personalised pages / sites. It would also mean that we could avoid trying to second-guess what users might do, and target limited resources more effectively.
An example of this in action might be searching on Google for the name of a restaurant. Google developers know, or have learned, that users want to get information like a phonenumber or a map, and so Google deliberately embeds location-specific results like this, in amongst its regular results.
Amazon is another example you might have seen, where content is displayed dynamically depending on your browsing and buying habits.
Bringing it to bioinformatics
In terms of the kinds of tools and applications we produce, we might want to use these ideas to “learn” from what users do, and then use this knowledge to structure our homepages and other key landing pages in a more responsive way. This doesn’t mean changing everything, every week, but instead providing clear paths to commonly used data and functions, based on what we know about user behaviour.
Unlike a lot of day-to-day websites, our online applications may be used repeatedly by users as part of their work. As a result, we probably have to deal with a range of user abilities, from novice to expert or “power user”. By learning from what these users do, we could design paths that are appropriate to their general skill level, for example.
That’s not to say that we should not also design paths with which users are not yet familiar, but which we think they will find useful; those will be critically important, too, especially with new applications, or new data. Involving the user, and knowing what they do, though, is a key feature of user-centred design, and a major way to promote usability and an improved user experience.
What do you think?
Are their examples of where this is already happening in bioinformatics tools and applications?