BiFlowLISA: Measuring Spatial Association for Bivariate Flow Data
Document Type
Article
Publication Date
9-2020
Keywords
Spatial statistics, Bivariate flow data, Network autocorrelation
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.compenvurbsys.2020.101519
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.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Computers, Environment and Urban Systems, v. 83, art. 101519
Scholar Commons Citation
Tao, Ran and Thill, Jean-Claude, "BiFlowLISA: Measuring Spatial Association for Bivariate Flow Data" (2020). School of Geosciences Faculty and Staff Publications. 2247.
https://digitalcommons.usf.edu/geo_facpub/2247
