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

Files in This Item:
File Description SizeFormat 
Article.pdf2.93 MBAdobe PDFDownload
Title: Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering
Authors: McArdle, Gavin
Tahir, Ali
Bertolotto, Michela
Permanent link:
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: ClusteringMap personificationWeb gisGeovisual analyticsSpatio-temporal clusteringBehavioural clusteringMap personalisationDigital globe
DOI: 10.1080/17538947.2014.898704
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

Show full item record

Citations 50

Last Week
Last month
checked on Jun 17, 2019

Page view(s) 10

checked on May 25, 2018

Download(s) 50

checked on May 25, 2018

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.