Geographic Information Systems (GIS) and related technologies such as remote sensors, satellite
imaging and portable devices that are capable of collecting precise positioning information, even
on portable hand-held devices, have spawned massive amounts of spatial-temporal databases.
Spatial "data science" broadly refers to the use of technology, statistical methods, computational
algorithms to extract knowledge and insights from spatially referenced data. Applications of
spatial-temporal data science are pervasive in the natural and environmental sciences; economics;
climate science; ecology; forestry; and public health. With the abundance of spatial BIG DATA
problems in the sciences and engineering, GIS and spatial data science will likely occupy a
central place in the data revolution engulfing us. This talk will discuss construction and
implementation of scalable Gaussian processes and the importance of conjugate Bayesian models
in carrying out Bayesian inference for spatially and temporally oriented massive data sets
exhibiting complex dependencies in diverse applications. We will elucidate recent developments
in Bayesian statistical science such as geosketching and predictive stacking that can harness high
performance scientific computing methods for spatial-temporal BIG DATA analysis and
emphasize how such methods can be implemented on modest computing architectures. The talk
will include specific examples of Bayesian hierarchical modeling in Light Detection and Ranging
(LiDAR) systems and other remote-sensed technologies; environmental sciences; and public
health.
6 Maggio 2022
Sudipto Banerjee
Dept. of Biostatistics. UCLA Fielding School of Public Health