Hansi Senaratne

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The presented work helps users of spatio-temporal uncertainty visualisation methods to select suitable methods according to their data and requirements. For this purpose, an extensive web-based survey has been carried out to assess the usability of selected methods for users in different domains, such as GIS and spatial statistics. The results of the survey(More)
With the increased availability of user generated data, assessing the quality and credibility of such data becomes important. In this article, we propose to assess the location correctness of visually generated Volunteered Geographic Information (VGI) as a quality reference measure. The location correctness is determined by checking the visibility of the(More)
Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in(More)
Actions Figure 1: The role of uncertainty and trust along the visual analytics process related to data and analytic provenance. Uncertainty builds up from data source to the system output that is perceived by users. Human user's sensemaking involves trust in order to arrive at valid knowledge in the end. Such activities in visual analytics systems can be(More)
With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information (VGI). During the past decade VGI has been used as a data source supporting a wide range of services, such as(More)
Today, a tremendous source of spatio-temporal data is user generated, so-called volunteered geographic information (VGI). Among the many VGI sources, microblogged services, such as Twitter, are extensively used to disseminate information on a near real-time basis. Interest in analysis of microblogged data has been motivated to date by many applications(More)
Visualization of spatial data uncertainties is crucial to the data understanding and exploration process. Scientific measurements, numerical simulations, and user generated content are error prone sources that gravely influence data reliability. When exploring large spatial datasets, we face two main challenges: data and uncertainty are two different sets(More)
This paper presents the results of a web based survey assessing the usability of main uncertainty visualisation methods for users belonging to different key domains such as GIS and Climate change research. We assess the usability of the visualisation methods based on the user's performance in selected learnability tasks, in addition to assessing user(More)