关键词: 人群移动流, 时空依赖, 时空相关性, 集聚模式

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