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User-uncertainty : A human-centred uncertainty taxonomy for VGI through the visual analytics workflow


Diehl, Alexandra; Yang, Bin; Das, Rahul Deb; Chen, Siming; Andrienko, Gennady; Andrienko, Natalia; Dransch, Doris; Keim, Daniel (2018). User-uncertainty : A human-centred uncertainty taxonomy for VGI through the visual analytics workflow. In: VGI Geovisual Analytics Workshop, colocated with BDVA 2018, Konstanz (DE), 19 October 2018 - 19 October 2018.

Abstract

The emergence of Web 2.0 and ubiquitous mobile platforms makes it possible to collect a vast amount of information contributed by people (VGI). For example, crowdsourcing applications collect information from domains such as biodiversity, urban planning, and risk management, and other sources such as social media connect citizens that exchange voluntarily huge amount of posts on platforms like Twitter, Flickr, and Facebook. VGI differs from data coming from sensors, simulations, and mathematical models. It is highly dependent on the human wills to share the information, and the background and knowledge of the user, which introduces uncertainy. In this paper, we explore different dimensions of VGI uncertainty from the perspective of the human that contributes with the data, as well as the technology and systems used to collect the data. Our contributions include a new taxonomy that explicitly differentiates among the uncertainty introduced by the humans, we named User-Uncertainty and analyzed it at different steps of the Visual Analytics Workflow, and several use cases that illustrate our approach for the case of User-Uncertainty coming from the producers. We conclude our paper with a discussion about the potential uses and future work to be done to understand User-Uncertainty.

Abstract

The emergence of Web 2.0 and ubiquitous mobile platforms makes it possible to collect a vast amount of information contributed by people (VGI). For example, crowdsourcing applications collect information from domains such as biodiversity, urban planning, and risk management, and other sources such as social media connect citizens that exchange voluntarily huge amount of posts on platforms like Twitter, Flickr, and Facebook. VGI differs from data coming from sensors, simulations, and mathematical models. It is highly dependent on the human wills to share the information, and the background and knowledge of the user, which introduces uncertainy. In this paper, we explore different dimensions of VGI uncertainty from the perspective of the human that contributes with the data, as well as the technology and systems used to collect the data. Our contributions include a new taxonomy that explicitly differentiates among the uncertainty introduced by the humans, we named User-Uncertainty and analyzed it at different steps of the Visual Analytics Workflow, and several use cases that illustrate our approach for the case of User-Uncertainty coming from the producers. We conclude our paper with a discussion about the potential uses and future work to be done to understand User-Uncertainty.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:19 October 2018
Deposited On:18 Jan 2019 09:27
Last Modified:18 Jan 2019 19:30
Publisher:Universität Konstanz
Additional Information:Buchtitel: VGI Geovisual Analytics Workshop
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://kops.uni-konstanz.de/handle/123456789/43913

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