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Crowdsourcing Spatial Phenomena Using Trust-Based Heteroskedastic Gaussian Processes

Venanzi, Matteo, Rogers, Alex and Jennings, N. R. (2013) Crowdsourcing Spatial Phenomena Using Trust-Based Heteroskedastic Gaussian Processes At First Conference on Human Computation and Crowdsourcing (HCOMP), United States. , pp. 182-189.

Record type: Conference or Workshop Item (Paper)


Many crowdsourcing applications require spatial modelling of data to make sense of location-based observations provided by multiple users. In this context, We propose a new spatial function modelling approach to address the problem of fusing multiple spatial observations reported by possibly untrustworthy users in the domains of participatory sensing and crowdsourcing applications. Specifically, we use a heteroskedastic Gaussian process model to incorporate user trust modelling into Bayesian spatial regression. In particular, by training the model with the reports gathered from the crowd, we are able to estimate the spatial function at any location of interest and also learn the level of trustworthiness of each user. We show that our method outperforms other standard homoskedastic and heteroskedastic Gaussian processes by up to 23% on a crowdsourced radiation dataset collected during the 2011 Fukushima earthquake in Japan. We also show that our method is able to improve the quality of spatial predictions on synthetic data by up to 70% and is robust in settings of up to 30% presence of untrustworthy users within the crowd.

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Accepted/In Press date: November 2013
Published date: 2013
Venue - Dates: First Conference on Human Computation and Crowdsourcing (HCOMP), United States, 2013-11-01
Organisations: Agents, Interactions & Complexity


Local EPrints ID: 354861
ISBN: 978-1-57735-607-3
PURE UUID: 76a83a7e-74cc-4dbe-92da-4f960a964fc0

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Date deposited: 26 Jul 2013 13:37
Last modified: 18 Jul 2017 03:52

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Author: Matteo Venanzi
Author: Alex Rogers
Author: N. R. Jennings

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