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Title: Dynamic model-based validation of crowd-sourced data
Author: Victor, Serge
ISNI:       0000 0004 5362 3017
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2014
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Advances in positioning, imaging, location-based services capabilities and broadband connectivity enable public participation in environmental monitoring and decision making in a manner previously mostly possible for professional scientists. Data collected by volunteers, Volunteered Geographic Information (VGI), has long been an important factor in environmental programmes, but the difficulties in applying quality control measures has limited their scientific value. This thesis addresses this challenge. It defines and develops a surveying architecture, allowing efficient in-field data collection from a Global Positioning System (c ps) enabled ubiquitous devices. The originality of this architecture comes from a real-time analysis of surveyed responses in order to drive the survey for optimised precision and validity. Spatial awareness of the surveying engine allows the system to monitor and accommodate the sets of questions for each surveyor as the survey progresses. As a result this architecture is able to support the implementation of a dynamic and directed approach to in-field data collection with real-time quality control driven by an adaptive survey modelling technique ensuring improved data collection and personalised feedback to users. Various post-processing methods are proposed for further statistical analysis of collected data. The research defines this conceptual architecture and a technical solution for its implementation based on HTMLS, independent of the mobile hardware producer, tablets, smart phones, netbooks, laptops, in order to allow the widest public participation opportunity possible. The thesis proposes future research topics related to automatic recruitment of volunteers in the surveying areas, automatic supportive knowledge base identification, for example Twitter or RSS feeds, plus more precise and faster post-survey analysis tools. The open-source architecture gives the possibility to extend current knowledge base, allowing the adaptive surveying engine to use multilevel multi-sourced sets of information. The implementation was tested thoroughly on Google Android and Apple iPhone devices with a use case coming from the Tranquillity Report of the Campaign to Protect Rural England (CPRE) 2006.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available