Strength-weighted flow cluster method considering spatiotemporal contiguity to reveal interregional association patterns - Haiping Zhang; Xingxing Zhou; Xinyue Ye; Guoan Tang; Haoran Wang; Shangjing Jiang (GIScience & Remote Sensing, 2023)
One of the most crucial topics in spatial interaction studies is mining patterns from extensive origin-destination (OD) flow data to capture interregional associations. However, prevailing methodologies tend to disregard the importance of using the relative closeness of interregional connections as weights, treat spatial and temporal dimensions independently, or overlook the temporal dimension completely. Consequently, the identified patterns are susceptible to inaccuracies, and the precise identification of pattern occurrence time and duration, despite their fundamental importance, remains elusive. In light of these challenges, this study proposes a strategy to calculate and combine the strength of weighted spatiotemporal flows, and develops a clustering method and evaluation metrics based on this framework. Compared to alternative density-based methods, the strength-based calculation approach demonstrates a capacity to identify flow patterns characterized by relatively high interregional closeness. Thus, the identification of flow patterns expands beyond density-based approaches, encompassing strength-based considerations and a shift from absolute to relative closeness between regions. Experiments using synthetic datasets conducted in this research demonstrate the effectiveness, efficiency, and extraction accuracy of the proposed method. Furthermore, a case study using real Chinese population migration data demonstrates the efficacy of the method in revealing implicit spatiotemporal association patterns between regions. The present study implements an interaction strength-based flow clustering and evaluation method that considers spatiotemporal continuity, making it applicable to spatial flow data analysis involving interaction volume and time attributes. As a result, this method holds promise for facilitating the modeling of intricate spatial flows within various contexts of study.