大数据时代的人类移动性研究
Research on Human Mobility in Big Data Era
查看参考文献55篇
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文摘
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人类个体/群体移动特征是多学科共同关注的研究主题。移动定位、无线通讯和移动互联网技术的快速发展使得获取大规模、长时间序列、精细时空粒度的个体移动轨迹和相互作用定量化成为可能。同时,地理信息科学、统计物理学、复杂网络科学和计算机科学等多学科交叉也为人类移动性研究的定量化提供了有力支撑。本文首先系统总结了大数据时代开展人类移动性研究的多源异构数据基础和多学科研究方法,然后将人类移动性研究归纳为面向人和面向地理空间两大方向。面向人的研究侧重探索人类移动特性的统计规律,并建立模型解释相应的动力学机制,或分析人类活动模式,并预测出行或活动;面向地理空间的研究侧重从地理视角分析人类群体在地理空间中的移动,探索宏观活动和地理空间的交互特征。围绕这两大方向,本文评述了人类移动性的研究进展和存在问题,认为人类移动性研究在数据稀疏性、数据偏斜影响与处理、多源异构数据挖掘、机器学习方法等方面依然面临挑战,对多学科研究方法的交叉与融合提出了更高要求。 |
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其他语种文摘
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Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future. |
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来源
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地球信息科学学报
,2014,16(5):665-672 【核心库】
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关键词
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人类移动性
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大数据
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数据挖掘
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统计物理学
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复杂网络
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地址
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中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京, 100101
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1560-8999 |
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学科
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测绘学 |
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基金
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国家自然科学基金资助项目
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国家863计划
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文献收藏号
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CSCD:5217390
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参考文献 共
55
共3页
|
|
1.
徐赞新. 基于随机矩阵理论的城市人群移动行为分析.
物理学报,2011,60(4):46-52
|
被引
3
次
|
|
|
|
|
2.
刘瑜. 基于位置感知设备的人类移动研究综述.
地理与地理信息科学,2011,27(4):8-13
|
被引
45
次
|
|
|
|
|
3.
丁益民. 考虑人类流动行为的动态复杂网络研究.
物理学报,2012,61(23):551-556
|
被引
1
次
|
|
|
|
|
4.
Rhee I. On the levy-walk nature of human mobility.
IEEE/ACM Transactions on Networking,2011,19(3):630-643
|
被引
21
次
|
|
|
|
|
5.
Zheng Y. GeoLife: A collaborative social networking service among user, location and trajectory.
IEEE Data(base) Engineering Bulletin,2010,33(2):32-39
|
被引
2
次
|
|
|
|
|
6.
郑宇. 基于用户轨迹挖掘的智能位置服务.
中国计算机学会通讯,2010,6(6):23-30
|
被引
9
次
|
|
|
|
|
7.
申悦. 基于GPS数据的北京市郊区巨型社区居民日常活动空间.
地理学报,2013,68(4):506-516
|
被引
51
次
|
|
|
|
|
8.
Liu Y. Understanding intra-urban trip patterns from taxi trajectory data.
Journal of Geographical Systems,2012,14(4):463-483
|
被引
47
次
|
|
|
|
|
9.
Veloso M. Sensing urban mobility with taxi flow.
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks,2011:41-44
|
被引
3
次
|
|
|
|
|
10.
Yuan J. T-drive: driving directions based on taxi trajectories.
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems,2010:99-108
|
被引
25
次
|
|
|
|
|
11.
郑宇. 城市计算与大数据.
中国计算机学会通讯,2013,9(8):6-16
|
被引
4
次
|
|
|
|
|
12.
Gonzalez M C. Understanding individual human mobility patterns.
Nature,2008,453(7196):779-782
|
被引
200
次
|
|
|
|
|
13.
Csaji B C. Exploring the mobility of mobile phone users.
Physica A: Statistical Mechanics and its Applications,2013,392(6):1459-1473
|
被引
11
次
|
|
|
|
|
14.
Noulas A. An empirical study of geographic user activity patterns in Foursquare.
ICWSM,2011,11:70-573
|
被引
1
次
|
|
|
|
|
15.
Liu Y. Uncovering patterns of inter-urban trip and spatial interaction from social media check-In data.
PloS ONE,2014,9(1):e86026
|
被引
38
次
|
|
|
|
|
16.
龙瀛. 利用公交刷卡数据分析北京职住关系和通勤出行.
地理学报,2012,67(10):1339-1352
|
被引
124
次
|
|
|
|
|
17.
Roth C. Structure of urban movements: polycentric activity and entangled hierarchical flows.
PloS one,2011,6(1):e15923
|
被引
25
次
|
|
|
|
|
18.
Froehlich J. Sensing and predicting the pulse of the city through shared bicycling.
International Joint Conferences on Artificial Intelligence,2009:1420-1426
|
被引
2
次
|
|
|
|
|
19.
汪秉宏. 人类行为,复杂网络及信息挖掘的统计物理研究.
上海理工大学学报,2012,34(2):103-117
|
被引
11
次
|
|
|
|
|
20.
周涛. 人类行为时空特性的统计力学.
电子科技大学学报,2013,42(4):481-540
|
被引
66
次
|
|
|
|
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