With the growing availability of high-resolution spatial data, such as high-definition images, three-dimensional point clouds of light detection and ranging (LIDAR) scanners, or communication and sensor networks, it might become challenging to detect changes and simultaneously account for spatial interactions in a timely manner. To detect local changes in the mean of isotropic spatiotemporal processes with locally constrained dependence structures, we have proposed a monitoring procedure that can be completely run on parallel processors. This allows for fast detection of local changes (i.e., in the case that only a few spatial locations are affected by the change). Due to parallel computation, high-frequency data could also be monitored. Hence, we additionally focused on the processing time required to compute the control statistics. Finally, the performance of the charts has been analyzed using a series of Monte Carlo simulation studies.
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