Annotated trajectories and the Space-Time-Cube

Movement data is collected by nearly everyone at any time. This data is not limited the trajectories of people, today’s technology also allows the simultaneous collection of trip related annotations like photos, video’s, voice, and texts. The combination of trajectories and annotations is a rich source to monitor movement in a context and discover known and unknown patterns. Often the annotations are implicitly geotagged by the gps-enabled devices like phones and cameras which are used to collect the annotations. This allows a match between the track and annotation based on coordinates. Otherwise the trajectories and annotations can be matched based on their respective time stamps. The geotagged material is often used on social media sites to exchange the whereabouts of people. The annotations are place on dedicated site such as Flickr and Panoramio. Via mash-ups it is also possible to publish the annotation together with the trajectories.
However, the publication of the material is virtually without limits. The resulting annotated maps tent to be unbalanced with a high diversity of data density, and overlapping annotations. In other word these map struggle with visual clutter, which makes it difficult to see relevant patterns. The visual clutter is due to two main reasons. The annotations are often concentrated near points-of-interest, like multiple photographs and videos of touristic objects. The trajectories are often ‘multi-scale’, e.g. there is a high density of points near points-of-interest because people tent to walk around and look at objects from different perspectives and fewer point in between points-of-interest, sometime even due to different modes of transport in between. The default map backgrounds such as Google earth or open street map contribute to this problem too, because these have not been adopted to the purpose.
A suitable solution to display and study movements is the Space-Time-Cube (STC), the graphic representation of Hägerstrand’s Time Geography. This paper answers the question of how suitable the STC is to display the above describe combination of trajectories and annotations to avoid the visual clutter. Although the STC will be described here as a stand-alone solution it is part of a wider geovisual analytics environment and is used next to maps and other graphics to be able to answer user questions. As a case study data set the travel log data of the authors’ to the MobileTartu2010 is used. The solution consists of two tightly coupled approaches which are based on the design of the STC content and the use of the interactive toolset. This second approach refers to the application Shneiderman’s visual information seeking mantra based on the specific user questions in mind. The first approach is related to how to visualize the annotations considering their quantity and type, and how to visualize the trajectory considering the data density and modes of transport, and their relation to the base map. The effectiveness of the suggested solution will be evaluated in an extensive usability test and preliminary results are to be reported.