Exploring the geographic uncertainty associated with crowdsourced crisis information: a geovisualisation approach

Dillingham, Iain (2013). Exploring the geographic uncertainty associated with crowdsourced crisis information: a geovisualisation approach. (Unpublished Doctoral thesis, City University London)

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Abstract

New information and communications technologies, such as mobile phones and social media, have presented the humanitarian community with a dilemma: how should humanitarian organisations integrate information from crisis-affected communities into their decision-making processes whilst guarding against inaccurate information from untrustworthy sources? Advocates of crisis mapping claim that, under certain circumstances, crowdsourcing can increase the accuracy of crisis information. However, whilst previous research has studied the geography of crisis information, the motivations of people who create crisis map mashups, and the motivations of people who crowdsource crisis information, the geography of, and the uncertainty associated with, crowdsourced crisis information has been ignored. As such, the current research is motivated by the desire to explore the geographic uncertainty associated with, and to contribute a better understanding of, crowdsourced crisis information.

The current research contributes to the fields of GISc (Geographic Information Science) and crisis informatics; crisis mapping; and geovisualisation specifically and information visualisation more generally. These contributions can be summarised as an approach to, and an understanding of, the geographic uncertainty associated with crowdsourced crisis information; three geovisualisation software prototypes that can be used to identify meaningful patterns in crisis information; and the design, analysis, and evaluation model, which situates the activities associated with designing a software artefact-and using it to undertake analysis-within an evaluative framework. The approach to the geographic uncertainty associated with crowdsourced crisis information synthesised techniques from GISc, geovisualisation, and natural language processing. By following this approach, it was found that location descriptions from the Haiti crisis map did not 'fit' an existing conceptual model, and, consequently, that there is a need for new or enhanced georeferencing methods that attempt to estimate the uncertainty associated with free-text location descriptions from sources of crowdsourced crisis information.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: City University London PhD theses
School of Engineering & Mathematical Sciences
School of Informatics > Department of Computing
School of Informatics > giCentre
URI: http://openaccess.city.ac.uk/id/eprint/17897

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