B0871
Title: Big spatial data learning: A parallel solution
Authors: Shan Yu - University of Virginia (United States) [presenting]
Guannan Wang - College of William & Mary (United States)
Lily Wang - George Mason University (United States)
Abstract: Nowadays, we are living in the era of Big Data. A significant portion of big data is big spatial data captured through advanced technologies or large-scale simulations. Explosive growth in spatial and spatiotemporal data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Parallel statistical computing has proved to be a handy tool when dealing with big data. However, it is hard to execute the conventional spline regressions in parallel. We will present a novel parallel smoothing technique for generalized partially linear spatially varying coefficient models, which can be used under different hardware parallelism levels. Moreover, conflated with concurrent computing, the proposed method can be easily extended to the distributed system. The newly developed method is evaluated through several simulation studies and an analysis of the US Loan Application Data.
