Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering
|Title:||Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering||Authors:||McArdle, Gavin
|Permanent link:||http://hdl.handle.net/10197/5704||Date:||2015||Online since:||2015-03-26T04:00:13Z||Abstract:||Extracting meaningful information from the growing quantity of spatial data is a challenge. The issues are particularly evident with spatio-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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Taylor and Francis||Journal:||International Journal of Digital Earth||Volume:||8||Issue:||8||Copyright (published version):||2014 Taylor and Francis||Keywords:||Clustering; Map personification; Web gis; Geovisual analytics; Spatio-temporal clustering; Behavioural clustering; Map personalisation; Digital globe||DOI:||10.1080/17538947.2014.898704||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
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