Uncertainty Representation: a Bayesian approach to crowdsourced hazard data

Authors: Carolynne Hultquist*, Pennsylvania State University and Columbia University
Topics: Spatial Analysis & Modeling, Hazards and Vulnerability, Cyberinfrastructure
Keywords: citizen science, big data, hazards, uncertainty
Session Type: Virtual Paper
Day: 4/11/2021
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 9
Presentation File: No File Uploaded

Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. However, the sampling method for crowdsourced projects is often unstructured and the statistical variations in the datasets are typically not assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources. Crowdsourced citizen science data does not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. However, a Bayesian approach can be used to model the spatial distribution of datasets and to communicate uncertainty as a result of data characteristics. The integration of citizen science with other traditional sources of Earth observation can improve situational awareness by providing higher spatio-temporal resolution data in urban areas. This beneficial aspect of the data is particularly useful for understanding environmental phenomena such as the dispersion of radiation and the extent of flooding from hurricanes as these phenomena have critical dimensions over time and space. Uncertainty analysis of diverse novel data sources can result in timely contextualized information that can improve decision-making during hazards.

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