Flow colocation quotient: Measuring bivariate spatial association for flow data. (January 2023) - Zhou, Mengjie; Yang, Mengjie; Chen, Zhe
Abstract: Bivariate association analyses of spatial flows can reveal the spatial dependence of two types of spatial flows. However, existing studies involving the detection of the bivariate associations of spatial flows focused on two distinct populations without considering the effect of joint distribution patterns. In this paper, we propose the flow colocation quotient (FCLQ), which is extended from the colocation quotient, to measure the bivariate association between categories of spatial flows considering the joint distribution. We developed two versions of the FCLQ: the global FCLQ, which is applied to measure the overall spatial association pattern, and the local FCLQ, which is used to identify the spatial heterogeneity of spatial association. To further test the statistical significance of the FCLQ values, we perform a Monte Carlo simulation under the null hypothesis with random labeling. Six synthetic datasets with different preset patterns are applied to verify the effectiveness of the FCLQ approach. A case study of bike-sharing trip data from Xiamen Island demonstrates the usefulness of the FCLQ in comparative analyses of three bike sharing platforms. Highlights: The FCLQ is proposed to measure the bivariate spatial association of discrete flow data considering the joint distribution. The FCLQ method mitigates the significant bias of the distribution pattern of the underlying population when a clustering pattern is observed. The global FCLQ is applied to measure the overall spatial association pattern, and the local FCLQ is used to identify the spatial heterogeneity of spatial association. The synthetic test demonstrates the effectiveness of the FCLQ in identifying spatial association patterns. The case study verifies the practicality of the FCLQ in comparative analyses of three bike sharing platforms.