Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes

Many crowdsourcing applications require spatial data modelling to make sense of location-based observations provided by multiple users. In this context, we pro-pose a new spatial function modelling approach to ad-dress the problem of fusing multiple spatial observa-tions reported by possibly untrustworthy users in the domains of participatory sensing and crowdsourcing ap-plications. Specifically, we use a heteroskedastic Gaus-sian process model to incorporate user trust modelling into Bayesian spatial regression. In particular, by train-ing the model with the reports gathered from the crowd, we are able to estimate the spatial function at any lo-cation of interest and also learn the level of trustwor-thiness of each user. We show that our method outper-forms other standard homoskedastic and heteroskedas-tic 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 syn-thetic data by up to 70 % and is robust in settings of up to 30 % presence of untrustworthy users within the crowd