Measuring spatio-temporal autocorrelation in flow data to explore human mobility patterns
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.
human mobility flowspatio-temporal dependencespatio-temporal autocorrelationaggregation patterns
万方数据
维普
