Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering

Abstract

Extracting meaningful information from the growing quantity of spatial data is a challenge. The issues are particularly evident with spatial temporal data describing movement. Such data typically corresponds to movement of humans, animals and machines in the physical environment. This article considers a special form of movement data generated through human-computer interactions with online web maps. As a user interacts with a web map using a mouse as a pointing tool, invisible trajectories are generated. By examining the spatial features on the map where the mouse cursor visits, a user's interests and experience can be detected. To analyse this valuable information, we have developed a geovisual analysis tool which provides a rich insight into such user behaviour. The focus of this paper is on a clustering technique which we apply to mouse trajectories to group trajectories with similar behavioural properties. Our experiments reveal that it is possible to identify experienced and novice users of web mapping environments using an incremental clustering approach. The results can be used to provide personalised map interfaces to users and provide appropriate interventions for completing spatial tasks.

What Went Wrong for Bad Solvers during Thematic Map Analysis? Lessons Learned from an Eye-Tracking Study

“…Some authors find the (results of) collected data useful for defining participants' proficiency. Çöltekin et al [13] chose answer time as a criterion for division into groups and their further comparison (see also [39]) in terms of sequence analysis of viewing delimited AOIs (areas of interest), whereas Opach et al [14] relied on answer correctness in order to distinguish more and less effective solvers (see also [16]) when comparing visual behavior when viewing a multi-component animated map.…”

The impact of Planned Special Events (PSEs) on urban traffic congestion

“…To show various attributes (such as crowd type, vehicle type, event occurrence details, etc.) in different positions of the track, colour, dots, geometric shapes or specially designed icons can also be added on the track lines [72].The polyline encodes the vehicle's position in space and time dimensions, where the colour from red to green encodes the speed of movement and some traces of traffic jams are easily discernible.…”

Mouse Tracking as a Method for Examining the Perception and Cognition of Digital Maps

“…Previous studies highlight that mouse-tracking and -logging techniques have been utilized for the examination of usability issues in the map-reading process [33], for the interpretation of the existing patterns and the identification of differences between novice and expert users [34]. Mouse events, in conjunction with other inputs that may reveal user behavior (e.g., eye movements), have been employed for the examination of specific GUIs or cartographic interfaces (see, e.g., the work presented by [35]), which are also characterized by interactivity [36].…”