A Bayesian geostatistical model for spatial data fusion
P. Moraga
Spatially misaligned data are becoming increasingly common due to advances in data collection and management. Here, we present a Bayesian geostatistical model for fusion of data obtained at point and areal resolutions. The model assumes that underlying all observations there is a spatially continuous variable that can be modeled using a Gaussian random field process. The model is fitted using the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches. In order to allow the combination of spatially misaligned data, a new SPDE projection matrix for mapping the Gaussian Markov random field from the observations to the triangulation nodes is proposed. We show the performance of the new approach by means of simulation and an application of PM2.5 prediction in USA. The approach presented provides a useful tool in a wide range of situations where information at different spatial scales needs to be combined.
Keywords: Spatial modeling, Geostatistics, Gaussian process, INLA, SPDE, air pollution
Scheduled
Spatial Statistics and Temporal Space I
June 7, 2022 3:30 PM
Conference hall
Other papers in the same session
C. Comas, P. Llagostera Blasco, N. López Lorenzo
J. Castillo-Mateo, A. E. Gelfand, A. C. Cebrián Guajardo, J. Asín Lafuente, J. Abaurrea
F. J. Alonso, J. M. Angulo, F. J. Esquivel, M. C. Bueso
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