BiFlowLISA: Measuring spatial association for bivariate flow data. (September 2020)

• Record Type: Journal Article
• Authors: Tao, Ran; Thill, Jean-Claude
• Is Part Of: Computers, environment and urban systems. Volume 83(2020)
• Publication Date: 2020-09
• Subjects: Spatial statistics -- Bivariate flow data -- Network autocorrelation; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101519
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Spatial flow data are often used to represent spatial interaction phenomena such as daily commuting trips, human or animal migrations, and the exchanges of commodities, capital, or even information between regions. With the increasingly available large volume of flow data in fine spatiotemporal resolution, exploratory spatial data analysis (ESDA) has become more important than ever to gain understanding of the data and the story behind it. A major group of flow-related ESDA methods focus on measuring spatial associations, which proves useful in improving the prediction power and interpretability of spatial interaction model (SIM), as well as in identifying local clusters and outliers of flow events. This paper introduces a new spatial statistical method called BiFlowLISA—a local indicator of spatial association of bivariate flow data. BiFlowLISA evaluates the association between two types of flows in close proximity, in other words, how the value of type-I flows associate with the value of nearby type-II flows. We develop BiFlowLISA by extending the local bivariate Moran's I to the flow context. We also put forth its global version to measure the global patterns, and another variant of BiFlowLISA to measure both spatial and in-situ correlations at the same time. Several flow-specific issues are discussed and solved, including flow neighbor definition, OD matrix sparsity, and conditional permutation. We experiment with synthetic datasets to verify its functionality and to summarize its characteristics. A case study of taxi and ride-hailing services in New York City demonstrates its usefulness in the comparative analysis of the spatial patterns of two types of travel flows. More applications of BiFlowLISA await to be explored in the future. Highlights: A new method called BiFlowLISA is developed to measure the spatial association for bivariate aggregated flow data. A variant of BiFlowLISA is put forth to measure both spatial and in-situ correlations at the same time. The global variant of BiFlowLISA is introduced to measure the overall bivariate spatial association in the study area. The weighted move-based flow distance (WMBFD) is proposed as a general approach to measure spatial weights of flow data. The case study demonstrates the usefulness of BiFlowLISA in the comparative analysis of competing transportation modes.