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Dynamical Surveying Adjustments for Crowd-sourced Data Observations
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Datum
2011
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Shaker Verlag
Zusammenfassung
Advances in positioning, imaging, location-based services capabilities, and broadband connectivity enable public participation in environmental monitoring and decision making in a manner previously only possible for professional scientists. Data collected by volunteers has long been an important factor in environmental programmes, but the difficulties in applying quality control measures and in ensuring an appropriate sample size of observations for a given area limit their scientific value. This paper addresses this challenge in describing a surveying architecture, allowing efficient in-field data collection from a GPS enabled ubiquitous device. 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 from the surveying engine allows one to modify the sets of questions for each surveyor volunteer as the survey goes along, and even to try to recruit volunteers in the area. 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 surveying modelling technique insuring optimised data collection and personalised feedback to users. A plug-in architecture gives the possibility to extend using RSS, Twitter and Mappiness data sources, with other real time feeds, then allowing the adaptive surveying engine to use multilevel multi-sourced sets of information. The paper describes this conceptual architecture and a technical solution for its implementation, independent of the mobile hardware producer, tablets, smart phones, netbooks, laptops, in order to allow the widest public participation opportunity possible. The implementation was tested on Google Android and Apple iPhone devices with a use case coming from the Tranquillity Report of the Campaign to Protect Rural England (CPRE).