A1294
Title: Identifying localized dynamic changes in large-scale spatio-temporal data through generalized nonparametric regression
Authors: Shan Yu - University of Virginia (United States) [presenting]
Yuda Shao - University of Virginia (United States)
Abstract: The rapid advancement of modern technology has led to the generation of vast amounts of large-scale spatial-temporal datasets, which offer valuable insights into human behaviour. The task of identifying when and where changes occur in spatial-temporal processes has gained significant attention in recent years. A generalized spatial-temporal modelling framework is proposed that captures the trends in the spatiotemporal process and identifies regions with quick changes. To achieve this, tensor product splines over triangular prismatic partitions are utilized to approximate the unknown spatial-temporal trend. A piecewise-penalty function is then imposed to efficiently identify the regions with dynamic changes, employing a computationally efficient algorithm. Simulation studies are conducted, and the proposed method is applied to mobile location data in Baltimore, demonstrating its effectiveness in efficiently recovering regions with dynamic changes over time.
