Hi everyone!
I'm Vicky and I'll present our work on space, time and choice, on behalf of myself and my
collaborators Elaine, Karan, and Fanny, at the University of Toronto.
Our work is about personal planning, so let's see what that looks like:
Imagine I have an appointment with my financial advisor that I’ll go to straight from work
I'll bike there… mmmh, 12minutes.
There’s probably enough time afterwards for me to pick up food for the picnic later...
some fruit from the la fontaine farmer’s market, perhaps...
or maybe some fresh baked goods would be nice?
You know, a big bottle of cold brew tea from @matcha sounds really great right now...
I should try to be at the picnic by 5, though!
These are screenshots of me ping-ponging through Google Maps, which may suffice to satisfy
my planning needs, but • I don't actually have a clear visual of
my time • it's hard to make direct comparisons between
options • and I lose all my work: though these bits
and pieces of routes add up to a schedule, there's no way for me to actually turn them
into one.
This is the kind of location-based personal schedule creation & exploration that we aim
to support through our research.
Recognizing that our lives are planned in both space and time, we seek to bridge the
gap between route-mapping and time-scheduling tools to support a general-purpose, spatio-temporal
planning workflow.
Now, let me show how we can make this process effective using our approach.
Here I see all the timings and the locations in context, including all the different travel
times and respective event durations and I can experiment with adding the tea
shop to my schedule Or compare all my different options in-context,
in a flexible manner.
We'll get back to the prototype later.
What I hope you caught from this quick overview, is that our workflow accommodates flexible
planning in a direct manipulation interface that also supports space-time reasoning.
Let's see the foundations behind our approach...
To unpack what flexible personal planning entails, we developed a framework that captures
the factors involved in planning, and how these are impacted by change in plans.
Planning is the attempt to satisfy some set of motives by allocating adequate time and
determining an available environment through which these motives can be fulfilled.
It's an interplay between….
user-defined requirements and ... externally-enforced restrictions.
The sum of these, we call our Space-Time Constraints, which are the input to our space-time-planning
workflow.
Let’s consider my motives from earlier: I want to go to my bank appointment at 3,
then stop by some place to pick up treats, to be at the picnic by 5.
My requirements are: • To see my financial advisor
• Obtain fresh snacks • And arrive at the picnic by 5PM
Some externally-defined restrictions are: • Hours of operation of the locations I
want to visit • The schedule of my financial advisor
• Travel times between locations These constraints fall along various spectrums.
They have some degree of spatiality, temporality, or both,
Importantly, they often express some degree of flexibility.
For example • My appointment at the bank has fully fixed
temporal and spatial requirements, • But getting snacks involves a semi-flexible
spatial requirement, as several potential locations can satisfy my motive.
We grouped space-time constraints into some other categories, too - there’s a full break-down
in the paper, if you’re curious!
Now with the constraints defined, we can complete the Space Time Planning Workflow.
The aggregated requirements and restrictions can have one of 3 outcomes.
1.
An under-constrained set of schedules 2.
An over-constrained lack of schedule 3.
A single solution
One way to reach a final, single solution is to use automated optimizations, leaving
it to the system to prioritize or relax different criteria.
Another approach is to leave it up to the users themselves -- to re-evaluate their own
motives, considering preferences, trade-offs, or compromises, and to re-create their requirements
in a potentially iterative process.
We posit that a hybrid approach is optimal, providing users with assistance but ultimately
supporting them in making their own decisions -- sometimes user subjectivities just don’t
easily fit as input into algorithms, but users should still have the opportunity to leverage
them on their own terms.
Note also that both external restrictions and user-motives are prone to change at any
time -- a space-time planning system should be robust to such changes.
We distilled our framework into design goals for tools in practice:
First, to support spatio-temporal reasoning, primarily using visualization
Second, to enable workflow flexibility Third, to minimize busywork, via both ease
of input and use of automation And fourth, to all-the-while maintain user
agency, leveraging technology without sacrificing user control.
We implemented core features of our space-time-planning workflow in a Space-Time-Planner prototype
tool that runs on mobile devices.
! Drag a place from the place palette to the schedule, and it shows up in the map.
Notice that the hours of operation are visible in context
And that travel time updates automatically in the schedule too.
! We prioritize travel time in this implementation by preventing conflicts through the pushing
feature.
Now, let’s see something a little less defined than the locations currently in our schedule.
Remember, we’re not yet sure which of the places we’d like to grab treats from.
! Our tool includes a group mechanism, the "one-of group” which allows to indicate
the system that any of these locations would work, and one would be enough.
In other words, the one-of group represents both flexibility and mutual exclusivity between
a set of candidate locations.
! By default, we set up these groups to automatically select the option that results in the least
travel-time, even during future schedule updates.
! The group can also be used to make comparisons between locations in the context of the schedule,
and the manual selection can be locked in at any time.
Group constructs, like the one-of-group we just saw, represent shared or related requirements
among a group of locations.
Let’s imagine that in my scenario, I actually have a bit more time before the picnic, and
so I want to visit as many of the listed locations, to put together a real feast.
! Another type of group, the "as-many-of group" is useful when you have a list of things to
do of roughly similar priority, like a list of errands, or tourist attractions, and you
want to fit as many of these as possible.
! As we drag the group or modify it’s duration, the auto-optimization picks the solution that
maximizes the number of visited locations while minimizing travel time — though it
would be trivial to consider other algorithms to prioritize solutions.
And of course, the as-many-of group also allows manual exploration of different solutions.
We conducted a user evaluation to gather feedback on the space-time-planning concept and prototype,
It included a structured replication task, and a freeform task in which we asked participants
to create a travel itinerary for a friend in a city of their choice.
Participants were able to complete the tasks successfully, creating rich schedules such
as these for the free-form tasks, in a few minutes.
I'll go over some highlights from the study, reflecting on our goals, but you can find
more in the paper.
We found that participants valued the unified spatio-temporal visualizations and workflow
while completing the tasks, indicating that, for example, it increased their conceptualization
of the /actual/ time they had available when creating multi-destination plans.
We also witnessed participants take a variety of different approaches to schedule-building.
Most liked to create a skeleton, that they were then able to modify, tweak, and reevaluate
as they went.
Some had a highly iterative approach, experimenting with dragging and dropping events, and taking
the system's calculated solutions as suggestions.
With respect to automation, participants appreciated the assistance from the computer in doing
calculations for them, But they also highly valued the level of agency they had
over their planning…
They commented not to move towards a more automated or more manual system overall...
but rather to increase capacities in both directions, described by one participant as
“fine-tuned control over automation” -- which we find very exciting this avenue of research!
There is much great work out there on the automation side of things, solvers for multi-criteria
decision making problems, that could be integrated into the vision we share here.
What we believe requires additional attention in HCI is to continue improving the interaction
designs that will best alleviate friction in this human-computer collaborative process.
Our work focuses on establishing a unified and user-centered space-time planning ideology.
We dive, particularly, into the user-motivated aspects of the workflow -- and already envision
many ways that our work thus-far can be extended.
There is much to do: in expanding the supported space-time requirement vocabulary, adding
just-in-time conflict resolution, and working across larger space-and-time scales -- check
out the paper for mockups and additional scenarios, like that of a film-set assistant who’s
space-time schedule is constantly challenged by her demanding job.
With that, I thank you for your attention, and am happy to take questions.