Paper Info
Title
BiFlowAMOEBA for the identification of arbitrarily shaped clusters in bivariate flow data
Abstract
A bivariate flow cluster is a group of two types of spatial flows, where both types of flows have high (or low) values, or one type of flow has a high value while the other has a low value. Identifying bivariate flow clusters aids in understanding the complex interactions between different flow patterns. Detecting bivariate flow clusters remains challenging because statistics for quantitatively assessing bivariate flow clusters are lacking and the shapes and sizes of clusters vary. This study proposes a novel bivariate flow clustering method (BiFlowAMOEBA) by improving a multidirectional optimum ecotope-based algorithm (AMOEBA) which embeds local Getis-Ord statistic in an iterative procedure to detect irregular-shaped clusters. We define a bivariate local Getis-Ord statistic for quantitatively assessing bivariate flow clusters, use a hierarchical clustering strategy to construct clusters, and evaluate the statistical significance of clusters using a Monte Carlo simulation. Experimental results of simulated datasets show that BiFlowAMOEBA can identify bivariate flow clusters of different shapes more accurately and completely, compared with two state-of-the-art methods. Two case studies show that BiFlowAMOEBA helps not only unveil the interactions between public transport and taxi services but also identifies competition patterns between taxis and ride-hailing services.
Year
DOI
Venue
2022
10.1080/13658816.2022.2072850
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
DocType
Volume
Bivariate flow data, clustering, spatial association, spatial data mining
Journal
36
Issue
ISSN
Citations 
9
1365-8816
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Qiliang Liu102.37
Jie Yang21392157.55
Min Deng35123.80
Wenkai Liu400.68
Rui Xu500.68