A geovisual analytics approach for mouse movement analysis
Ali Tahir,
Gavin McArdle and
Michela Bertolotto
International Journal of Data Mining, Modelling and Management, 2014, vol. 6, issue 4, 315-332
Abstract:
The use of web maps has created opportunities and challenges for map generation and delivery. While volunteered geographic information has led to the development of accurate and inexpensive web maps, the sheer volume of data generated has created spatial information overload. This results in difficulties identifying relevant map features. Geopersonalisation, which adapts map content based on user interests offers a solution to this. The technique is especially powerful when implicit indicators of interest are used as a basis for personalisation. This article describes the design and features of VizAnalysisTools, a suite of tools to visualise and interpret users' implicit interactions with map content. While traditional data mining techniques can be used to identify trends and preferences, visual analytics, in particular geovisual analytics, which assists the human cognition process, has proven useful in detecting interesting patterns. By identifying salient trends, areas of interest on the map become apparent. This knowledge can be used to strengthen the algorithms used for geopersonalisation.
Keywords: geographical information systems; GIS; geovisual analytics; geovisual analysis; geoweb; web GIS; geopersonalisation; geovisualisation; web architecture; map personalisation; web mapping; heat map; speed map; mouse movements; open source; data mining; web maps; map content; user interests; personalisation. (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=66761 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:6:y:2014:i:4:p:315-332
Access Statistics for this article
More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().