轨迹数据的时间采样间隔对停留识别和出行网络构建的影响  

Impacts of Temporal Sampling Intervals on Stay Detection and Movement Network Construction in Trajectory Data

作　　者：赵志远[1] 尹凌[2] 方志祥[1,3] 萧世伦[4] 杨喜平 ZHAO Zhiyuan;YIN Ling;FANG Zhixiang;SHAW Shihlung;YANG Xiping(State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;Engineering Research Center for Spatiotemporal Data Smart Acquisition and Application,Ministry of Education of China Wuhan 430079,China;Department of Geography,University of Tennessee,Knoxville,Tennessee 37996-0925,USA;School of Geography and Tourism,Shaanxi Normal University,Xi＇an 710119,China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [2]中国科学院深圳先进技术研究院,广东深圳518055 [3]时空数据智能获取技术与应用教育部工程研究中心,湖北武汉430079 [4]美国田纳西大学地理系,田纳西州诺克斯维尔37996-0925 [5]陕西师范大学地理科学与旅游学院,陕西西安710119

出　　处：《武汉大学学报（信息科学版）》2018年第8期1152-1158,共7页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(41231171,41371420,41301440);广东省自然科学基金(2014A030313684);深圳市基础研究项目(JCYJ20140610151856728)~~

摘　　要：个体轨迹数据已经广泛用于人群活动的研究中。在静止的局部空间开展的活动是个体日常生活的基本元素,在轨迹数据中对应停留部分。因此学者常从轨迹数据中识别停留来研究个体活动信息。然而,轨迹数据的时间采样间隔会对停留识别带来影响。针对该问题,首先提出了一个框架,量化不同持续时间长度的活动在不同时间采样间隔的轨迹数据中被识别为停留的概率。其次,考虑到个体出行网络依赖于停留识别结果,基于该框架,研究分析了时间采样间隔对出行网络分析结果的影响。最后,利用该框架分别对深圳市居民出行调查数据和手机轨迹数据进行了分析。研究表明,在面向人群活动的研究和应用中,该框架能支持时间采样间隔的选择决策和面向活动类型的研究结果评价。Trajectory data have been extensively used in human mobility studies.Activities,especially conducted on a static and local space are basic elements of people＇s daily life,and they are represented as stays in trajectories.Hence detecting stays from trajectories has become a base for many activityoriented studies.The temporal sampling interval（TSI）of trajectory data can impact the result of stay detection.However such impacts have not been systematically studied yet.This study proposes a probability-based framework,which aims to quantify the probability of an activity that with a specific duration time can be detected as a stay with different TSIs.Moreover,this framework can support further analysis on the evolution of daily movement network with different TSIs.We demonstrate the impacts of TSIs on stay detection and movement networks construction by using a trip survey dataset and a mobile phone location dataset of Shenzhen,China respectively.This study provides both methodological and empirical guidance on the decision-making of a TSI selection as well as the estimation of the results of activity-oriented studies.

关 键 词：轨迹数据 时间采样间隔 人类活动 停留识别 手机定位数据 

分 类 号：P208[天文地球—地图制图学与地理信息工程]