Identifying Flow Clusters Based on Density Domain Decomposition

Abstract

Flow clustering is one of the most important data mining methods for the analysis of origin-destination (OD) flow data, and it may reveal the underlying mechanisms responsible for the spatial distributions and temporal dynamics of geographical phenomena. Existing flow clustering approaches are based mainly on the extension of traditional clustering methods to points by redefining basic concepts or some spatial association indictors of flows and the implementation of classic clustering processes, such as aggregating, collecting or searching. However, current techniques still suffer from two main problems: poor identification accuracy and complicated parameter selection processes. To resolve these problems, a new clustering method is proposed in this study for arbitrarily shaped flow clusters based on the density domain decomposition of flows. Simulation experiments based on our method and existing methods show that our method outperforms the three most commonly used methods in terms of the overall identification rate and almost all F1 measures, and it does not require any manual adjustments during the parameter selection process. Finally, a case study is conducted on taxi trip data from Beijing. Several flow clusters are identified to represent different types of residents' travel behaviors, including daily commuting, return travel, tourism and behaviors on special days. INDEX TERMS Origin-destination (OD) flow, flow space, flow clustering, density domain decomposition, point process.

Estimating spatiotemporal aggregation scales by revisiting the spatiotemporal L‐function

“…The local version of the L‐function can be used to identify aggregations (Shu et al, 2021; Song et al, 2019). Specifically, with the estimated aggregation scales ( s , t ) obtained from the L‐function, the points with the highest local values and their neighbors contained in a cylinder of diameter s and temporal height t are identified as the dominant aggregation.…”

“…The local version of the L-function can be used to identify aggregations (Shu et al, 2021;Song et al, 2019).…”