Modeling spatial uncertainty in opportunistically collected citizen science data
Abstract
Citizen science projects are framed as providing direct societal benefits to the public and opportunities for scientific discovery. Contributed data can provide unique informational content that cannot be found even in comparable traditional sources. However, users of contributed datasets need to evaluate aspects of citizen science collection for the usefulness of the data within specific applications. The value of environmental monitoring data are dependent not only on the validity of the observations, but also on the alignment of research questions with the data that was collected opportunistically. Contributed data are typically collected opportunistically without spatial sampling methodologies and may exhibit unique spatial patterns due to its collection instead of necessarily depicting the trend of the phenomenon of interest. This spatial uncertainty can be represented using Bayesian methods that provide metrics for evaluating uncertainty in the spatial modeling of opportunistic environmental data. Spatial evaluation and quantitative approaches can address these challenges in the representation of uncertainty in opportunistic contributions and make citizen science data appropriate for many research applications.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN22A..06H
- Keywords:
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- 1694 Instruments and techniques;
- GLOBAL CHANGE;
- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS;
- 1994 Visualization and portrayal;
- INFORMATICS;
- 4315 Monitoring;
- forecasting;
- prediction;
- NATURAL HAZARDS