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.
Highlights
INTRODUCTIONThe movement of a geographical object between two locations (e.g., the daily commute from dwelling to workplace [1], immigration between states [2] or delivery services in a city [3]) can be presented as an origin-destination (OD) flow [4], [5]
The movement of a geographical object between two locations can be presented as an origin-destination (OD) flow [4], [5]
In this study, we propose a clustering method for arbitrarily shaped flow clusters based on the density domain decomposition of flows
Summary
The movement of a geographical object between two locations (e.g., the daily commute from dwelling to workplace [1], immigration between states [2] or delivery services in a city [3]) can be presented as an origin-destination (OD) flow [4], [5]. In hierarchical clustering methods for flow data, the distance of an OD flow should be defined according to the OD locations [29], [30] and, sometimes, the attributes of the flows [6], [31]; an agglomerative or divisive strategy should be used to organize each flow into a hierarchy [32], [33] These methods can identify flow clusters at different spatial scales, and they are usually proposed to solve problems associated with flow cluster identification, generalization and visualization [34]–[37]. Determination of an unknown number of flow clusters is always a troublesome problem, and some other scale parameters, such as distance threshold and neighborhood range, are hard to set in clustering algorithms To solve these problems, in this study, we propose a clustering method for arbitrarily shaped flow clusters based on the density domain decomposition of flows.
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