利用地理流时空相关性分析揭示人群移动规律  

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

作　　者：周洋 孙潇萌 陶然 刘鹏程 郑文升[1,2] ZHOU Yang;SUN Xiaomeng;TAO Ran;LIU Pengcheng;ZHENG Wensheng(Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province,Wuhan 430079,China;College of Urban and Environmental Sciences,Central China Normal University,Wuhan 430079,China;School of Geosciences,University of South Florida,Tampa 33606,USA)

机构地区：[1]地理过程分析与模拟湖北省重点实验室,湖北武汉430079 [2]华中师范大学城市与环境科学学院,湖北武汉430079 [3]南佛罗里达大学,美国坦帕33606

出　　处：《测绘通报》2024年第10期125-131,共7页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(42001399,42071455,42471486);广东省城市空间信息工程重点实验室开放基金(2023002)。

摘　　要：人群移动流具有时空依赖特征,识别和定量刻画其时空相关性是揭示人群移动规律和对其进行模拟预测的基础。本文通过对比和讨论流的空间自相关和时空自相关分析方法,探索在城市内部和省际两个不同空间尺度下的人群移动流时空依赖特征及其集聚模式。结果表明:(1)城市内部和省际的人群移动流均表现出强时空依赖性;(2)城市内部和省际人群移动流的高-高模式均以短距离流为主,低-低模式则是跨组团的远距离流;(3)相比于仅考虑空间依赖,同时考虑时间和空间依赖可以有效捕捉跨区域移动流,这在省际移动流的集聚模式分析中尤为重要;(4)时空相关性中的高-低和低-高模式可以识别流量随时间变化剧烈的流,有助于进一步揭示局部区域的异常流模式。本文结论进一步强调了时空相关性的优势及特点,可丰富地理流的探索性分析方法,服务于人群动态观测的移动规律和模式特征挖掘。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:(1)Spatio-temporal dependencies are significant in both intra-urban and inter-provincial human mobility flows.(2)Notably,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.(3)Specifically,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.(4)Furthermore,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.

关 键 词：人群移动流 时空依赖 时空相关性 集聚模式 

分 类 号：P208[天文地球—地图制图学与地理信息工程]