Bulletin of Surveying and Mapping ›› 2024, Vol. 0 ›› Issue (10): 125-131.doi: 10.13474/j.cnki.11-2246.2024.1021.

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Measuring spatio-temporal autocorrelation in flow data to explore human mobility patterns

ZHOU Yang1,2, SUN Xiaomeng1,2, TAO Ran3, LIU Pengcheng1,2, ZHENG Wensheng1,2   

  1. 1. Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan 430079, China;
    2. College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China;
    3. School of Geosciences, University of South Florida, Tampa 33606, USA
  • Received:2024-05-28 Published:2024-11-02

Abstract: Human mobility flows are spatio-temporal dependent. The identifying and measuring the spatio-temporal autocorrelation in flows are critical in uncovering human mobility patterns and building prediction models. This study compares and discusses methods of spatial auto-correlation (SFlowLISA) and spatio-temporal auto-correlation (STFlowLISA) to explore spatio-temporal dependencies and aggregation patterns buried in intra-urban and inter-provincial human mobility flows. The results show that:①Spatio-temporal dependencies are significant in both intra-urban and inter-provincial human mobility flows. ②Notably,we observe that flows show high-high (HH) patterns are those short-distance travels,while flows with low-low (LL) patterns are those long-distance travels across regions. ③Specifically,involving both temporal and spatial dependence can effectively capture inter-regional mobility flows than merely measuring spatial dependence. This is particularly important in aggregation patterns of inter-provincial human mobility flows. ④Furthermore,flows with high-low (HL) and low-high (LH) patterns show sharp temporal fluctuation. This characteristic is helpful to identify local outliers in massive flows. Overall,this study emphasizes the advantage and importance of measuring spatio-temporal autocorrelation when analyzing flow data using two typical case studies. The results will benefit the understanding of human mobility patterns and unveiling the auto-correlation characteristics using effective exploratory analysis of STFlowLISA.

Key words: human mobility flow, spatio-temporal dependence, spatio-temporal autocorrelation, aggregation patterns

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