In this blog post, we'll use examples from the first prototype that was implemented at Salient Works - the set of libraries and applications for decision support in air traveling scenarios. The primary motivation for that project was to address challenges related to long distance air travel, which can be very stressful, even when everything is going according to plan. They usually start with selecting a connection, when a traveler may experience information overload as different options are difficult to compare. During a trip, transfers between flights can be especially overwhelming due to time zones, unfamiliar airports and overall travel fatigue. In that context any unexpected event, like a missed or cancelled flight, can be traumatic, especially for elder or less frequent flyers. Many of these factors cannot be controlled, but we can build technical solutions to keep users comfortably informed, assist them at critical points and facilitate their interactions with other entities (like airlines).

Such solutions require effective presentation of information, usually in a visual form (though other types of interfaces can be also applied). We plan to publish a dedicated post about visualization as an essential part of data analysis, but here we’d like to talk about a more specific scope – role of visualization in decision support. We refer to visual decision support to describe situations when the user experience in a decision scenario is built around a visualization pattern specifically designed to address requirements of this scenario, with all its context and constraints. In practice, it means that all information required to make a correct decision, or series of decisions, should be delivered to a user at the right time and in the form adapted to user’s situation and most probable cognitive state. In our prototype applications, the relevant information is mostly related to time and space orientation and it should be presented in a way that reduces probability of errors (e.g. caused by lack of sleep) and stress related to operating in an unknown environment.

Let’s move to specific examples from our prototype. The main idea was to design a simple and universal visualization pattern that could be consistently used throughout different stages of a travel experience, including planning, the actual trip and dealing with emergencies. An example visualization of a trip between Seattle and Poznan using this pattern is presented in Figure 1. The pattern is built around time as perceived by the traveler (horizontal axis) and we placed a special emphasis on critical time points like beginning of each trip segment, as well as translating time zone differences upon arrival at a destination. The grayed area in the background indicates night time, so it should be easier for a traveler to plan working and resting during a trip. Creating such a visualization is the first step, as it can be customized and personalized (also for accessibility), used in a static itinerary (see example from our prototype) or in a dynamic companion application and updated with current details, such as a departure gate.

Figure 1. An example visualization if a trip between Seattle and Poznan

One key property of this pattern that may not be immediately obvious is the dimension of vertical axis - in the configuration of our examples it is based on latitude of visited airports. This property was introduced in order to create unique shapes for different trip options and to make a selected one look familiar and recognizable. After all, the same visual representation is about to be used during different stages of a trip, starting with its planning. This is actually the stage when the uniqueness of shapes turned out to be the most useful since it made comparison of available options much simpler and cleaner. Figure 2 contains examples of 5 different options for a trip from Seattle to Paris. As you can see, they are all presented using the same time range, so they are much easier to compare, including departure and arrival times, as well as layovers’ durations. We conducted limited usability tests and found out that this approach works also for comparing a significant number of options (see multiple results for the same query), especially when combined with multistage selection. Using our visual pattern, we were able to build a fully visual experience for trip searching, comparing and selection.

Figure 2. Comparison of 5 different options for a trip from Seattle to Paris

This was our first big project at Salient Works and we spent way too much time on its design and prototyping. In addition to core and support libraries, we built a visual search portal (integrated with Google QPX), functionality for generating personalized itineraries and even a proof of concept for a contextual app with a demo for re-scheduling a missed or cancelled connection. Unfortunately, we were not able to establish working monetization scenarios or find partners to introduce our prototypes into production. But we gained a lot of experience, which we later used in development of our framework, where we implement concept of visual decision support in a more flexible way, through application of analysis artifacts associated with different domain libraries and templates. And our prototypes may still find their way into production environments, as we recently came back to the project and adapted this pattern to visualization of flight time limitations, with pilots and other flying personal as intended users.