基于大数据的流空间研究进展

doi:10.18306/dlkxjz.2020.08.013

地理科学进展 ›› 2020, Vol. 39 ›› Issue (8): 1397-1411.doi: 10.18306/dlkxjz.2020.08.013

基于大数据的流空间研究进展

杨延杰¹^,²(), 尹丹¹^,², 刘紫玟¹^,², 黄庆旭¹^,²^,^*(), 何春阳¹^,², 吴康³

1. 北京师范大学地表过程与资源生态国家重点实验室,人与环境系统可持续研究中心,北京 100875
2. 北京师范大学地理科学学部自然资源学院,土地资源与区域发展研究中心,北京 100875
3. 首都经济贸易大学城市群系统演化与可持续发展的决策模拟研究北京市重点实验室,北京 100070

收稿日期:2019-07-15 修回日期:2019-10-31 出版日期:2020-08-28 发布日期:2020-10-28
通讯作者: 黄庆旭
作者简介:杨延杰(1992— ),男,山西太原人,硕士生,主要从事大数据和城市景观过程模拟研究。E-mail: yjyang@mail.bnu.edu.cn
基金资助:
北京市科技新星项目(Z181100006218049)

Research progress on the space of flow using big data

YANG Yanjie¹^,²(), YIN Dan¹^,², LIU Ziwen¹^,², HUANG Qingxu¹^,²^,^*(), HE Chunyang¹^,², WU Kang³

1. Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
2. School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3. Beijing Key Laboratory of Megaregions Sustainable Development Simulation, Capital University of Economics and Business, Beijing 100070, China

Received:2019-07-15 Revised:2019-10-31 Online:2020-08-28 Published:2020-10-28
Contact: HUANG Qingxu
Supported by:
Beijing Nova Program(Z181100006218049)

摘要/Abstract

摘要：

流空间是认识城市网络结构和演化的重要手段。近年来大数据的快速发展为流空间研究提供了新的机遇和挑战。论文系统综述了基于大数据的流空间研究进展。首先,论文梳理了基于大数据流空间研究的背景和历史,然后总结了基于大数据的流空间研究的主题、数据类型、方法和主要发现,最后展望了未来的研究挑战。2011年以后,基于大数据的流空间研究呈指数增长趋势,中英文论文年均发表量从2010年的11篇增长到2018年的106篇。大数据主要从提供新的数据源、激发新的分析方法和提供新的研究视角三方面推进了流空间研究。常用于流空间研究的大数据主要包括手机信令数据、社交媒体签到数据、公共交通刷卡数据和出租车轨迹数据,它们比传统统计数据更能直接提供人流、物流和信息流的时空动态信息。研究方法也从传统的基于距离的重力模型发展为网络分析方法。未来在交叉学科研究、大数据和传统数据的耦合、大数据与深度学习和云计算等新方法的结合方面仍需进一步探索,从理论、数据和方法上全面深化流空间研究。

关键词: 流空间, 大数据, 城市网络, 人员移动, 城市可持续性

Abstract:

The studies on the space of flow play an important role in understanding the structure and change of urban networks. The rapid development of big data provides new opportunities and challenges for the studies on the space of flow in recent years. This article systematically reviewed the research progress on the space of flow based on big data. First, we retrospect the background and history of the studies on the space of flow, then summarized the themes, the types of big data, the methods used for the studies and the major findings, as well as discussed the research challenges. We found an exponential growth of studies on the space of flow using big data after 2011. The annual number of published papers increased from 11 to 106 during 2011-2018. Big data deepen the research on the space of flow by providing new data sources, inspiring new analytical methods, and new research perspectives. Four types of big data—mobile phone, social media, smart card, and taxi trajectory data are commonly used in the studies on the space of flow, which can provide information on spatiotemporal flows (such as population flow, material flow, and information flow) directly. Research methods have also evolved from distance-based gravity models to network analysis. In the future, the research on the space of flow using big data can be further improved by validating the effectiveness and representativeness of the big data, the integration of big data and traditional data, and the information mining from big data using new methods such as deep learning and cloud computing.

Key words: space of flow, big data, urban network, human mobility, urban sustainability

杨延杰, 尹丹, 刘紫玟, 黄庆旭, 何春阳, 吴康. 基于大数据的流空间研究进展[J]. 地理科学进展, 2020, 39(8): 1397-1411.

YANG Yanjie, YIN Dan, LIU Ziwen, HUANG Qingxu, HE Chunyang, WU Kang. Research progress on the space of flow using big data[J]. PROGRESS IN GEOGRAPHY, 2020, 39(8): 1397-1411.

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大数据	数据概述	特点	流空间类型	文献来源
手机信令数据	记录手机用户在发生通话、收发短信、位置更新等事件时手机连接的基站位置信息	覆盖人群广,时间连续性好;存在精度受基站分布影响、数据质量取决于用户通话的频率和数据跳站等问题	人流、信息流	王垚等^[63]
社交媒体签到数据	来源于社交平台,具有地理位置信息	容易获取,具有语义特征;存在数据不能覆盖所有年龄段的人群、签到数据不完整和签到位置间不合理的快速移动等问题	人流、信息流	Zhen等^[64]
公共交通刷卡数据	记录乘客的卡号、上下车位置和时间等信息	数据获取成本低,覆盖人群广;数据连续性差,空间分辨率低,只能反映用户在不同刷卡位置间的移动	人流、交通流	陆锋等^[6]
出租车轨迹数据	记录地理位置、时间和是否载客等信息	数据精度高,可以详细记录车辆的行驶轨迹;存在覆盖范围集中于城市内部、移动轨迹受路网限制、覆盖人群较小等问题	人流、交通流	Barbosa等^[42]

方法	原理	特点	典型应用
流分析	通过探究流量、方向和强度等特征来揭示流的时空特征,主要包括流量分析、流向分析和流强度分析等	流分析方法可以有效衡量流的时空规律,从而直观地表现流特征	人口通量变化^[82] 高性能空间聚类^[98]
网络分析	通过社会网络分析和复杂网络分析等方法探究网络中各节点的联系程度,并揭示人流、物流和信息流等在不同节点间的流动特征	可以有效揭示流空间的节点特征和网络特征;难以利用大数据中的语义信息	信息流强度^[99] 网络密度和中心度^[100]
人员移动建模	通过建立模型挖掘海量数据中所隐藏的规律,揭示人员移动特征和预测人员移动行为	人员移动模型有助于揭示流空间规律的深层机制,深化人们对流空间的理解;现有模型在模型普适性和机理解释方面存在不足	总体模型^[8] 时空语义模型^[94]

基于大数据的流空间研究进展

Research progress on the space of flow using big data

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文献类型	文献来源	发表年份	论文题目	期刊	引用量/次
中文	李山等^[43]	2008	基于百度指数的旅游景区络空间关注度: 时间分布及其前兆效应	地理与地理信息科学	203
	甄峰等^[44]	2012	基于网络社会空间的中国城市网络特征——以新浪微博为例	地理学报	304
	熊丽芳等^[45]	2013	基于百度指数的长三角核心区城市网络特征研究	经济地理	132
	王波等^[46]	2013	基于微博用户关系的网络信息地理研究——以新浪微博为例	地理研究	81
	董超等^[47]	2014	基于通信流的吉林省流空间网络格局	地理学报	55
英文	Gonzalez等^[48]	2008	Understanding individual human mobility patterns	Nature	2042
	Song等^[49]	2010	Limits of predictability in human mobility	Science	1015
	Lu等^[50]	2012	Predictability of population displacement after the 2010 Haiti earthquake	PNAS	144
	Liu等^[40]	2014	Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from social media Check-In data	PLoS One	122
	Long等^[51]	2015	Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing	Environment and Urban Systems	55

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