Identification of Sequential Changes and Investigation of Change Patterns in Dynamic Spatiotemporal Data

Spatiotemporal data generally undergo fluctuations. Identifying a sequence of changes in spatiotemporal data and investigating patterns of the detected changes are commonly required across many domains for analysis, prediction, and decision-making. However, most current analytical tools do not support both functions, so there has been a limitation in comprehensively analyzing dynamics in ongoing spatiotemporal data. This research suggests an integrated change analysis framework to support the overall analysis process, from detecting a sequence of changes to investigating patterns of the detected changes, as a prototype of a decision support tool. To identify a sequence of multiple changes in ongoing spatiotemporal data, where the spatiotemporal process is unknown, this research develops a recursive change detection procedure that applies a nonparametric cumulative sum method designed to signal a shift repeatedly. The recursive change detection procedure diagnoses and estimates the actual changepoint following a signal of a shift and uses the mean and variance of all observations accumulated from the estimated actual shift as the subsequent in-control mean and variance for detecting the next change. After detecting a sequence of changes, the identified changes are interpreted as explicit change types by domain-dependent definition of the change types. Spatial and/or temporal patterns of explicit changes are visualized on a set of event bands by flexibly assigning controlled dimensions to event bands, stacks, and panels, which support investigating similarities and differences of explicit changes according to the controlled space and/or time. The proposed integrated analysis of sequential changes in spatiotemporal data is applied