地理科学进展 ›› 2020, Vol. 39 ›› Issue (8): 1397-1411.doi: 10.18306/dlkxjz.2020.08.013
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
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: 基金资助:
YANG Yanjie1,2(), YIN Dan1,2, LIU Ziwen1,2, HUANG Qingxu1,2,*(
), HE Chunyang1,2, WU Kang3
Received:
2019-07-15
Revised:
2019-10-31
Online:
2020-08-28
Published:
2020-10-28
Contact:
HUANG Qingxu
Supported by:
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。
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.
表1
高被引的中文和英文论文"
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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 2008 | Understanding individual human mobility patterns | Nature | 2042 |
Song等[ | 2010 | Limits of predictability in human mobility | Science | 1015 | |
Lu等[ | 2012 | Predictability of population displacement after the 2010 Haiti earthquake | PNAS | 144 | |
Liu等[ | 2014 | Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from social media Check-In data | PLoS One | 122 | |
Long等[ | 2015 | Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing | Environment and Urban Systems | 55 |
表2
基于大数据的流空间研究中主要涉及的大数据"
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[ |
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[ |
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[ |
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[ |
表3
基于大数据的流空间研究中主要涉及的方法"
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[ 高性能空间聚类[ |
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[ 网络密度和中心度[ |
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[ 时空语义模型[ |
[1] | Castells M. The informational city: Information technology, economic restructuring and the urban-regional progress[M]. New York, USA: Blackwell, 1989. |
[2] | 魏冶. 流空间视角的沈阳市空间结构研究[D]. 长春: 东北师范大学, 2013. |
[ Wei Ye. Spatial structure of Shenyang in the perspective of space of flows. Changchun, China: Northeast Normal University, 2013. ] | |
[3] | 孙中伟, 路紫. 流空间基本性质的地理学透视[J]. 地理与地理信息科学, 2005,21(1):109-112. |
[ Sun Zhongwei, Lu Zi. A geographical perspective to the elementary nature of space of flows. Geography and Geo-information Science, 2005,21(1):109-112. ] | |
[4] | 甄峰, 秦萧, 席广亮. 信息时代的地理学与人文地理学创新[J]. 地理科学, 2015,35(1):11-18. |
[ Zhen Feng, Qin Xiao, Xi Guangliang. The innovation of geography and human geography in the information era. Scientia Geographica Sinica, 2015,35(1):11-18. ] | |
[5] | 刘瑜, 康朝贵, 王法辉. 大数据驱动的人类移动模式和模型研究[J]. 武汉大学学报信息科学版, 2014,39(6):660-666. |
[ Liu Yu, Kang Chaogui, Wang Fahui. Towards big data-driven human mobility patterns and models. Geomatics and Information Science of Wuhan University, 2014,39(6):660-666. ] | |
[6] | 陆锋, 刘康, 陈洁. 大数据时代的人类移动性研究[J]. 地球信息科学学报, 2014,16(5):665-672. |
[ Lu Feng, Liu Kang, Chen Jie. Research on human mobility in big data era. Journal of Geo-information Science, 2014,16(5):665-672. ] | |
[7] | 蒋小荣, 汪胜兰. 中国地级以上城市人口流动网络研究: 基于百度迁徙大数据的分析[J]. 中国人口科学, 2017(2):35-46, 127. |
[ Jiang Xiaorong, Wang Shenglan. Research on China's urban population mobility network: Based on Baidu Migration big data. Chinese Journal of Population Science, 2017(2):35-46, 127. ] | |
[8] |
Barchiesi D, Preis T, Bishop S, et al. Modelling human mobility patterns using photographic data shared online[J]. Royal Society Open Science, 2015,2(8):150046. doi: 10.1098/rsos.150046.
pmid: 26361545 |
[9] | 沈丽珍, 甄峰, 席广亮. 解析信息社会流动空间的概念、属性与特征[J]. 人文地理, 2012,27(4):14-18. |
[ Shen Lizhen, Zhen Feng, Xi Guangliang. Analyzing the concept, attributes and characteristics of the attributes of space of flow in the information society. Human Geography, 2012,27(4):14-18. ] | |
[10] | 秦萧, 甄峰, 熊丽芳, 等. 大数据时代城市时空间行为研究方法[J]. 地理科学进展, 2013,32(9):1352-1361. |
[ Qing Xiao, Zhen Feng, Xiong Lifang, et al. Methods in urban temporal and spatial behavior research in the big data era. Progress in Geography, 2013,32(9):1352-1361.] | |
[11] | Castells M. The rise of the network society[M]. The information age: Economy, society, and culture Vol 1. New York, USA: Blackwell, 1996: 407-459. |
[12] | Appadurai A. Cultural dimensions of globalization [M]. Minnesota, USA: the University of Minnesota Press, 1996: 27-85. |
[13] | 甄峰. 信息时代新空间形态研究[J]. 地理科学进展, 2004,23(3):16-26. |
[ Zhen Feng. Researches on new spatial forms in information era. Progress in Geography, 2004,23(3):16-26. ] | |
[14] | 高鑫, 修春亮, 魏冶. 城市地理学的“流空间”视角及其中国化研究[J]. 人文地理, 2012,27(4):32-36, 160. |
[ Gao Xin, Xiu Chunliang, Wei Ye. Study on the sinicization of space of flows basing on the visual angle of urban geography. Human Geography, 2012,27(4):32-36, 160. ] | |
[15] | 沈丽珍, 顾朝林. 区域流动空间整合与全球城市网络构建[J]. 地理科学, 2009,29(6):787-793. |
[ Shen Lizhen, Gu Chaolin. Integration of regional space of flows and construction of global urban network. Scientia Geographica Sinica, 2009,29(6):787-793. ] | |
[16] | Alvin T. The third wave[M]. New York, USA: Bantam Books, 1980. |
[17] | Laney D. 3D data management: Controlling data volume, velocity, and variety [R]. META Group Research Note, 6. Stamford, USA: META Group, 2001. |
[18] | Ji C Q, Li Y, Qiu W M, et al. Big data processing in cloud computing environments [R]. 12th International Symposium on Pervasive Systems, Algorithms and Networks. San Marcos, USA, 2012: 17-23. |
[19] |
Graham-Rowe D, Goldston D, Doctorow C, et al. Big data: Science in the petabyte era[J]. Nature, 2008,455:8-9.
doi: 10.1038/455008a pmid: 18769400 |
[20] | Sagiroglu S, Sinanc D. Big data: A review [R]. 2013 International Conference on Collaboration Technologies and Systems (CTS). San Diego, USA, 2013: 42-47. |
[21] | Hashem I A T, Yaqoob I, Anuarx N B, et al. The rise of "big data" on cloud computing: Review and open research issues[J]. Information Systems, 2015,47:98-115. |
[22] | 甄峰, 王波. “大数据”热潮下人文地理学研究的再思考[J]. 地理研究, 2015,34(5):803-811. |
[ Zhen Feng, Wang Bo. Rethinking human geography in the age of big data. Geographical Research, 2015,34(5):803-811. ] | |
[23] | 裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质[J]. 地理学报, 2019,74(3):586-598. |
[ Pei Tao, Liu Yaxi, Guo Sihui, et al. Principle of big geodata mining. Acta Geographica Sinica, 2019,74(3):586-598. ] | |
[24] | 宋长青. 地理学研究范式的思考[J]. 地理科学进展, 2016,35(1):1-3. |
[ Song Changqing. On paradigms of geographical research. Progress in Geography, 2016,35(1):1-3. ] | |
[25] | 程昌秀, 史培军, 宋长青, 等. 地理大数据为地理复杂性研究提供新机遇[J]. 地理学报, 2018,73(8):1397-1406. |
[ Cheng Changxiu, Shi Peijun, Song Changqing, et al. Geographic big-data: A new opportunity for geography gomplexity study. Acta Geographica Sinica, 2018,73(8):1397-1406. ] | |
[26] | 龙瀛, 孙立君, 陶遂. 基于公共交通智能卡数据的城市研究综述[J]. 城市规划学刊, 2015(3):70-77. |
[ Long Ying, Sun Lijun, Tao Sui. A review of urban studies based on transit smart card data. Urban Planning Forum, 2015(3):70-77. ] | |
[27] | Ilieva R T, McPhearson T. Social-media data for urban sustainability[J]. Nature Sustainability, 2018,1:553-565. |
[28] | Martí P, Serrano-Estrada L, Nolasco-Cirugeda A. Social media data: challenges, opportunities and limitations in urban studies[J]. Computers, Environment and Urban Systems, 2019,74:161-174. |
[29] | 艾少伟, 苗长虹. 从“地方空间”、“流动空间”到“行动者网络空间”: ANT视角[J]. 人文地理, 2010,25(2):43-49. |
[ Ai Shaowei, Miao Changhong. "Space of places", "space of flows" and “"space of actor-networks": From the perspective of ANT. Human Geography, 2010,25(2):43-49. ] | |
[30] | 许学强, 周一星, 宁越敏. 城市地理学 [M]. 北京: 高等教育出版社, 2009: 191-240. |
[ Xu Xueqiang, Zhou Yixing, Ning Yuemin. Urban geography. Beijing, China: Higher Education Press, 2009: 191-240. ] | |
[31] | Taylor P J. Specification of the world city network[J]. Geographical Analysis, 2001,33(2):181-194. |
[32] | Beaverstock J V, Boardwell J T. Negotiating globalization, transnational corporations and global city financial centres in transient migration studies[J]. Applied Geography, 2000,20(3):277-304. |
[33] |
Taylor P J. Leading world cities: Empirical evaluations of urban nodes in multiple networks[J]. Urban Studies, 2005,42(9):1593-1608.
doi: 10.1080/00420980500185504 |
[34] | 赵渺希, 刘铮. 基于生产性服务业的中国城市网络研究[J]. 城市规划, 2012,36(9):23-28, 38. |
[ Zhao Miaoxi, Liu Zheng. Research on China's city network based on production service industry. City Planning Review, 2012,36(9):23-28, 38. ] | |
[35] |
李仙德. 基于上市公司网络的长三角城市网络空间结构研究[J]. 地理科学进展, 2014,33(12):1587-1600.
doi: 10.11820/dlkxjz.2014.12.002 |
[ Li Xiande. Spatial structure of the Yangtze River Delta urban network based on the pattern of listed companies network. Progress in Geography, 2014,33(12):1587-1600. ] | |
[36] |
吴康, 方创琳, 赵渺希. 中国城市网络的空间组织及其复杂性结构特征[J]. 地理研究, 2015,34(4):711-728.
doi: 10.11821/dlyj201504010 |
[ Wu Kang, Fang Chuanglin, Zhao Miaoxi. The spatial organization and structure complexity of Chinese intercity networks. Geographical Research, 2015,34(4):711-728. ] | |
[37] | 赵渺希, 钟烨, 徐高峰. 中国三大城市群多中心网络的时空演化[J]. 经济地理, 2015,35(3):52-59. |
[ Zhao Miaoxi, Zhong Ye, Xu Gaofeng. Polycentric progress of the three major city regions in China. Economic Geography, 2015,35(3):52-59. ] | |
[38] | 王垚, 钮心毅, 宋小冬. “流空间”视角下区域空间结构研究进展[J]. 国际城市规划, 2017,32(6):27-33. |
[ Wang Yao, Niu Xinyi, Song Xiaodong. Research progress of regional spatial structure under the perspective of space of flow. Urban Planning International, 2017,32(6):27-33. ] | |
[39] |
Liu Y, Kang C G, Gao S, et al. Understanding intra-urban trip patterns from taxi trajectory data[J]. Journal of Geographical Systems, 2012,14(4):463-483.
doi: 10.1007/s10109-012-0166-z |
[40] |
Liu Y, Sui Z W, Kang C G, et al. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data[J]. PLoS One, 2014,9(1):e86026. doi: 10.1371/journal.pone.0086026.
doi: 10.1371/journal.pone.0086026 pmid: 24465849 |
[41] |
Long Y, Thill J C. Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing[J]. Computers, Environment and Urban Systems 2015,53:19-35.
doi: 10.1016/j.compenvurbsys.2015.02.005 |
[42] |
Barbosa H, Barthelemy M, Ghoshal G, et al. Human mobility: Models and applications[J]. Physics Reports, 2018,734:1-74.
doi: 10.1016/j.physrep.2018.01.001 |
[43] | 李山, 邱荣旭, 陈玲. 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[J]. 地理与地理信息科学, 2008,24(6):102-107. |
[ Li Shan, Qiu Rongxu, Chen Ling. Cyberspace attention of tourist attractions based on Baidu Index: Temporal distribution and precursor effect. Geography and Geo-information Science, 2008,24(6):102-107. ] | |
[44] |
甄峰, 王波, 陈映雪. 基于网络社会空间的中国城市网络特征: 以新浪微博为例[J]. 地理学报, 2012,67(8):1031-1043.
doi: 10.11821/xb201208003 |
[ Zhen Feng, Wang Bo, Chen Yingxue. China's city network characteristics based on social network space: An empirical analysis of Sina micro-blog. Acta Geographica Sinica, 2012,67(8):1031-1043. ] | |
[45] | 熊丽芳, 甄峰, 王波, 等. 基于百度指数的长三角核心区城市网络特征研究[J]. 经济地理, 2013,33(7):67-73. |
[ Xiong Lifang, Zhen Feng, Wang Bo, et al. The research of the Yangtze River Delta core area's city network characteristics based on Baidu Index. Economic Geography, 2013,33(7):67-73. ] | |
[46] |
王波, 甄峰, 席广亮, 等. 基于微博用户关系的网络信息地理研究: 以新浪微博为例[J]. 地理研究, 2013,32(2):380-391.
doi: 10.11821/yj2013020018 |
[ Wang Bo, Zhen Feng, Xi Guangliang, et al. A study of cybergeography based on micro-blog users' relationship: With a case of Sina micro-blog. Geographical Research, 2013,32(2):380-391. ] | |
[47] |
董超, 修春亮, 魏冶. 基于通信流的吉林省流空间网络格局[J]. 地理学报, 2014,69(4):510-519.
doi: 10.11821/dlxb201404007 |
[ Dong Chao, Xiu Chunliang, Wei Ye. Network structure of 'space of flows' in Jilin Province based on telecommunication flows. Acta Geographica Sinica, 2014,69(4):510-519. ] | |
[48] |
González M C, Hidalgo C A, Barabási A L. Understanding individual human mobility patterns[J]. Nature, 2008,453:779-782.
pmid: 18528393 |
[49] |
Song C M, Qu Z H, Blumm N, et al. Limits of predictability in human mobility[J]. Science, 2010,327:1018-1021.
pmid: 20167789 |
[50] |
Lu X, Bengtsson L, Holme P. Predictability of population displacement after the 2010 Haiti earthquake[J]. PNAS, 2012,109(29):11576-11581.
pmid: 22711804 |
[51] |
Long Y, Thill J C. Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing[J]. Environment and Urban Systems, 2015,53:19-35.
doi: 10.1016/j.compenvurbsys.2015.02.005 |
[52] | 陈映雪, 甄峰, 王波, 等. 基于微博平台的中国城市网络信息不对称关系研究[J]. 地球科学进展, 2012,27(12):1353-1362. |
[ Chen Yingxue, Zhen Feng, Wang Bo, et al. A study of internet information asymmetry relations among Chinese cities based on the micro-blog platform. Advances in Earth Science, 2012,27(12):1353-1362. ] | |
[53] | 熊丽芳, 甄峰, 席广亮, 等. 我国三大经济区城市网络变化特征: 基于百度信息流的实证研究[J]. 热带地理, 2014,34(1):34-43. |
[ Xiong Lifang, Zhen Feng, Xi Guangliang, et al. Characteristics of the city network in the three major economic zones of China: A study based on Baidu information flow. Tropical Geography, 2014,34(1):34-43. ] | |
[54] | Xia F, Wang J Z, Kong X J, et al. Exploring human mobility patterns in urban scenarios: A trajectory data perspective[J]. IEEE Communications Magazine, 2018,56(3):142-149. |
[55] | Devriendt L, Boulton A, Brunn S, et al. Searching for cyberspace: The position of major cities in the information age[J]. Journal of Urban Technology, 2011,18(1):73-92. |
[56] | 赵梓渝, 魏冶, 庞瑞秋, 等. 基于人口省际流动的中国城市网络转变中心性与控制力研究: 兼论递归理论用于城市网络研究的条件性[J]. 地理学报, 2017,72(6):1032-1048. |
[ Zhao Ziyu, Wei Ye, Pang Ruiqiu, et al. Alter-based centrality and power of Chinese city network using inter-provincial population flow. Acta Geographica Sinica, 2017,72(6):1032-1048. ] | |
[57] | 刘望保, 石恩名. 基于ICT的中国城市间人口日常流动空间格局: 以百度迁徙为例[J]. 地理学报, 2016,71(10):1667-1679. |
[ Liu Wangbao, Shi Enming. Spatial pattern of population daily flow among cities based on ICT: A case study of "Baidu Migration". Acta Geographica Sinica, 2016,71(10):1667-1679. ] | |
[58] | 龙瀛, 张宇, 崔承印. 利用公交刷卡数据分析北京职住关系和通勤出行[J]. 地理学报, 2012,67(10):1339-1352. |
[ Long Ying, Zhang Yu, Cui Chengyin. Identifying commuting pattern of Beijing using bus smart card data. Acta Geographica Sinica, 2012,67(10):1339-1352. ] | |
[59] | 钟炜菁, 王德, 谢栋灿, 等. 上海市人口分布与空间活动的动态特征研究: 基于手机信令数据的探索[J]. 地理研究, 2017,36(5):972-984. |
[ Zhong Weijing, Wang De, Xie Dongcan, et al. Dynamic characteristics of Shanghai's population distribution using cell phone signaling data. Geographical Research, 2017,36(5):972-984. ] | |
[60] | 黄洁, 王姣娥, 靳海涛, 等. 北京市地铁客流的时空分布格局及特征: 基于智能交通卡数据[J]. 地理科学进展, 2018,37(3):397-406. |
[ Huang Jie, Wang Jiaoe, Jin Haitao, et al. Investigating spatiotemporal patterns of passenger flows in the Beijing metro system from smart card data. Progress in Geography, 2018,37(3):397-406. ] | |
[61] | 秦静, 李郎平, 唐鸣镝, 等. 基于地理标记照片的北京市入境旅游流空间特征[J]. 地理学报, 2018,73(8):1556-1570. |
[ Qin Jing, Li Langping, Tang Mingdi, et al. Exploring the spatial characteristics of Beijing inbound tourist flow based on geotagged photos. Acta Geographica Sinica, 2018,73(8):1556-1570. ] | |
[62] | 靳诚, 徐菁, 黄震方, 等. 南京城市内部景点间游客流动特征分析[J]. 地理学报, 2014,69(12):1858-1870. |
[ Jin Cheng, Xu Jing, Huang Zhenfang, et al. Analyzing the characteristics of tourist flows between the scenic spots in inner city based on tourism strategies: A case study in Nanjing. Acta Geographica Sinica, 2014,69(12):1858-1870. ] | |
[63] | 王垚, 钮心毅, 宋小冬, 等. 人流联系和经济联系视角下区域城市关联比较: 基于手机信令数据和企业关联数据的研究[J]. 人文地理, 2018,33(2):84-91, 146. |
[ Wang Yao, Niu Xinyi, Song Xiaodong, et al. The comparison of regional urban relations between people flow and capital flow: A study based on mobile phone signaling data and firm interlock data. Human Geography, 2018,33(2):84-91, 146. ] | |
[64] |
Zhen F, Cao Y, Qin X, et al. Delineation of an urban agglomeration boundary based on Sina Weibo microblog 'check-in' data: A case study of the Yangtze River Delta[J]. Cities, 2017,60:180-191.
doi: 10.1016/j.cities.2016.08.014 |
[65] | Trasarti R, Olteanu-Raimond A M, Nanni M, et al. Discovering urban and country dynamics from mobile phone data with spatial correlation patterns[J]. Telecommunications Policy, 2015,39(3-4):347-362. |
[66] |
Çolak S, Lima A, González M C. Understanding congested travel in urban areas[J]. Nature Communications, 2016,7:10793. doi: 10.1038/ncomms10793.
pmid: 26978719 |
[67] | Zhang W Y, Derudder B, Wang J H, et al. Using location-based social media to chart the patterns of people moving between cities: The case of Weibo-users in the Yangtze River Delta[J]. Journal of Urban Technology, 2016,23(3):91-111. |
[68] |
Li L, Yang L, Zhu H H, et al. Explorative analysis of Wuhan intra-urban human mobility using social media check-in data[J]. PLoS One, 2015,10(8):e0135286. doi: 10.1371/journal.pone.0135286.
pmid: 26288273 |
[69] | Zhou J P, Murphy E, Long Y. Commuting efficiency in the Beijing metropolitan area: An exploration combining smartcard and travel survey data[J]. Journal of Transport Geography, 2014,41:175-183. |
[70] |
Wang Z Y, Hu Y X, Zhu P, et al. Ring aggregation pattern of metro passenger trips: A study using smart card data[J]. Physica A: Statistical Mechanics and Its Applications, 2018,491:471-479.
doi: 10.1016/j.physa.2017.08.105 |
[71] |
Zhong C, Batty M, Manley E, et al. Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data[J]. PLoS One, 2016,11(2):e0149222. doi: 10.1371/journal.pone.0149222.
doi: 10.1371/journal.pone.0149222 pmid: 26872333 |
[72] | 杨喜平, 方志祥. 移动定位大数据视角下的人群移动模式及城市空间结构研究进展[J]. 地理科学进展, 2018,37(7):880-889. |
[ Yang Xiping, Fang Zhixiang. Recent progress in studying human mobility and urban spatial structure based on mobile location big data. Progress in Geography, 2018,37(7):880-889. ] | |
[73] | Zhang S, Tang J J, Wang H X, et al. Revealing intra-urban travel patterns and service ranges from taxi trajectories[J]. Journal of Transport Geography, 2017,61:72-86. |
[74] | Tang J J, Liu F, Wang Y H, et al. Uncovering urban human mobility from large scale taxi GPS data[J]. Physica A: Statistical Mechanics and Its Applications, 2015,438:140-153. |
[75] | 赖建波, 潘竟虎. 基于腾讯迁徙数据的中国“春运”城市间人口流动空间格局[J]. 人文地理, 2019,34(3):108-117. |
[ Lai Jianbo, Pan Jinghu. Spatial pattern of population flow among cities in China during the Spring festival travel rush based on "Tencent Migration" data. Human Geography, 2019,34(3):108-117. ] | |
[76] | 魏冶, 修春亮, 王绮, 等. 中国春运人口流动网络的富人俱乐部现象与不平衡性分析[J]. 人文地理, 2018,33(2):124-129. |
[ Wei Ye, Xiu Chunliang, Wang Qi, et al. Rich-club phenomenon and disequilibrium of china's population flow network during spring festival travel period. Human Geography, 2018,33(2):124-129. ] | |
[77] | Xi G L, Zhen F, He J L, et al. City networks of online commodity services in China: Empirical analysis of Tmall clothing and electronic retailers[J]. Chinese Geographical Science, 2018,28(2):231-246. |
[78] | 李鲁奇, 孔翔. “双十一”期间中国快递流通的时空结构与效率: 基于时间地理学视角[J]. 地理研究, 2019,38(8):1891-1904. |
[ Li Luqi, Kong Xiang. The tempo-spatial structure and efficiency of China's express service during the "Double Eleven Shopping Carnival": A time-geographic approach. Geographical Research, 2019,38(8):1891-1904. ] | |
[79] | Lin J Y, Li X. Simulating urban growth in a metropolitan area based on weighted urban flows by using web search engine[J]. International Journal of Geographical Information Systems, 2015,29(10):1721-1736. |
[80] | 秦昆, 罗萍, 姚博睿. GDELT数据网络化挖掘与国际关系分析[J]. 地球信息科学学报, 2019,21(1):14-24. |
[ Qin Kun, Luo Ping, Yao Borui. Networked mining of GDELT and international relations analysis. Journal of Geo-information Science, 2019,21(1):14-24. ] | |
[81] | 潘碧麟, 王江浩, 葛咏, 等. 基于微博签到数据的成渝城市群空间结构及其城际人口流动研究[J]. 地球信息科学学报, 2019,21(1):68-76. |
[ Pan Bilin, Wang Jianghao, Ge Yong, et al. Spatial structure and population flow analysis in Chengdu-Chongqing urban agglomeration based on Weibo Check-in big data. Journal of Geo-information Science, 2019,21(1):68-76. ] | |
[82] | Fan Z D, Pei T, Ma T, et al. Estimation of urban crowd flux based on mobile phone location data: A case study of Beijing, China[J]. Computers, Environment and Urban Systems, 2018,69:114-123. |
[83] |
Yao X, Zhu D, Gao Y, et al. A stepwise spatio-temporal flow clustering method for discovering mobility trends[J]. IEEE Access, 2018,6:44666-44675.
doi: 10.1109/Access.6287639 |
[84] |
von Landesberger T, Brodkorb F, Roskosch P, et al. Mobility graphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering[J]. IEEE Transactions on Visualization and Computer Graphics, 2016,22(1):11-20.
doi: 10.1109/TVCG.2015.2468111 pmid: 26529684 |
[85] | 彭卉, 杜云艳, 易嘉伟, 等. 基于手机数据的北京市城市与近郊交互模式挖掘[J]. 地球信息科学学报, 2019,21(1):101-110. |
[ Peng Hui, Du Yunyan, Yi Jiawei, et al. Mining urban-rural spatial interaction pattern from mobile data of Beijing. Journal of Geo-information Science, 2019,21(1):101-110. ] | |
[86] | 宋冬林, 齐文浩. 东北区域经济一体化演变的社会网络分析[J]. 吉林大学社会科学学报, 2018,58(4):97-107, 206. |
[ Song Donglin, Qi Wenhao. The social network in the evolution of regional economic integration of Northeast China. Jilin University Journal Social Sciences Edition, 2018,58(4):97-107, 206. ] | |
[87] | 赵映慧, 高鑫, 姜博. 东北三省城市百度指数的网络联系层级结构[J]. 经济地理, 2015,35(5):32-37. |
[ Zhao Yinghui, Gao Xin, Jiang Bo. The urban network connection of three provinces in Northeast China based on Baidu Index. Economic Geography, 2015,35(5):32-37. ] | |
[88] |
Zhang X, Kloosterman R C. Connecting the 'workshop of the world': Intra-and extra-service networks of the Pearl River Delta city-region[J]. Regional Studies, 2016,50(6):1069-1081.
doi: 10.1080/00343404.2014.962492 |
[89] |
王姣娥, 景悦. 中国城市网络等级结构特征及组织模式: 基于铁路和航空流的比较[J]. 地理学报, 2017,72(8):1508-1519.
doi: 10.11821/dlxb201708013 |
[ Wang Jiao'e, Jing Yue. Comparison of spatial structure and organization mode of inter-city networks from the perspective of railway and air passenger flow. Acta Geographica Sinica, 2017,72(8):1508-1519. ] | |
[90] |
Liu X, Gong L, Gong Y X, et al. Revealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 2015,43:78-90.
doi: 10.1016/j.jtrangeo.2015.01.016 |
[91] | 靳诚, 徐菁. 南京市对外交通节点与酒店之间游客流动空间特征分析[J]. 人文地理, 2016,31(5):55-62. |
[ Jin Cheng, Xu Jing. Study on the tourists flow among external transport nodes and hotels in Nanjing. Human Geography, 2016,31(5):55-62. ] | |
[92] | 刘瑜, 肖昱, 高松, 等. 基于位置感知设备的人类移动研究综述[J]. 地理与地理信息科学, 2011,27(4):8-13. |
[ Liu Yu, Xiao Yu, Gao Song, et al. A review of human mobility research based on location aware devices. Geography and Geo-information Science, 2011,27(4):8-13. ] | |
[93] |
李婷, 裴韬, 袁烨城, 等. 人类活动轨迹的分类, 模式和应用研究综述[J]. 地理科学进展, 2014,33(7):938-948.
doi: 10.11820/dlkxjz.2014.07.009 |
[ Li Ting, Pei Tao, Yuan Yecheng, et al. A review on the classification, patterns and applied research of human mobility trajectory. Progress in Geography, 2014,33(7):938-948. ] | |
[94] |
Huang W, Li S N. Understanding human activity patterns based on space-time-semantics[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016,121:1-10.
doi: 10.1016/j.isprsjprs.2016.08.008 |
[95] |
Ling X M, Huang Z R, Wang C C, et al. Predicting subway passenger flows under different traffic conditions[J]. PLoS One, 2018,13(8):e0202707. doi: 10.1371/journal.pone.0202707.
pmid: 30148888 |
[96] |
Wu L, Zhi Y, Sui Z W, et al. Intra-urban human mobility and activity transition: Evidence from social media check-in data[J]. PLoS One, 2014,9(5):e97010. doi: 10.1371/journal.pone.0097010.
doi: 10.1371/journal.pone.0097010 pmid: 24824892 |
[97] |
Duan Z T, Yang Y, Zhang K, et al. Improved deep hybrid networks for urban traffic flow prediction using trajectory data[J]. IEEE Access, 2018,6:31820-31827.
doi: 10.1109/ACCESS.2018.2845863 |
[98] |
Chen Y R, Huang Z, Pei T, et al. HiSpatialCluster: A novel high-performance software tool for clustering massive spatial points[J]. Transactions in GIS, 2018,22(5):1275-1298.
doi: 10.1111/tgis.12463 |
[99] |
蒋大亮, 孙烨, 任航, 等. 基于百度指数的长江中游城市群城市网络特征研究[J]. 长江流域资源与环境, 2015,24(10):1654-1664.
doi: 10.11870/cjlyzyyhj201510006 |
[ Jiang Daliang, Sun Ye, Ren Hang, et al. Analyses on the city network characteristics of middle Yangtze urban agglomeration based on Baidu Index. Resources and Environment in the Yangtze Basin, 2015,24(10):1654-1664. ] | |
[100] |
孙阳, 姚士谋, 张落成. 中国沿海三大城市群城市空间网络拓展分析: 以综合交通信息网络为例[J]. 地理科学, 2018,38(6):827-837.
doi: 10.13249/j.cnki.sgs.2018.06.001 |
[ Sun Yang, Yao Shimou, Zhang Luocheng. Spatial expansion of urban network for the three coastal agglomerations of China: A study based on integrated traffic information network. Scientia Geographica Sinica, 2018,38(6):827-837. ] | |
[101] |
吴志峰, 柴彦威, 党安荣, 等. 地理学碰上“大数据”:热反应与冷思考[J]. 地理研究, 2015,34(12):2207-2221.
doi: 10.11821/dlyj201512001 |
[ Wu Zhifeng, Chai Yanwei, Dang Anrong, et al. Geography interact with big data: Dialogue and reflection. Geographical Research, 2015,34(12):2207-2221. ] | |
[102] |
康朝贵, 刘瑜, 邬伦. 城市手机用户移动轨迹时空熵特征分析[J]. 武汉大学学报(信息科学版), 2017,42(1):63-69, 129.
doi: 10.13203/j.whugis20160203 |
[ Kang Chaogui, Liu Yu, Wu Lun. An analysis of entropy of human mobility from mobile phone data. Geomatics and Information Science of Wuhan University, 2017,42(1):63-69, 129. ] | |
[103] |
Gao S, Li L N, Li W W, et al. Constructing gazetteers from volunteered big geo-data based on Hadoop[J]. Computers, Environment and Urban Systems, 2017,61:172-186.
doi: 10.1016/j.compenvurbsys.2014.02.004 |
[104] |
Yan Y, Zhang S, Tang J J, et al. Understanding characteristics in multivariate traffic flow time series from complex network structure[J]. Physica A: Statistical Mechanics and Its Applications, 2017,477:149-160.
doi: 10.1016/j.physa.2017.02.040 |
[105] |
Song H Y, You D. Modeling urban mobility with machine learning analysis of public taxi transportation data[J]. International Journal of Pervasive Computing and Communications, 2018,14(1):73-87.
doi: 10.1108/IJPCC-D-18-00009 |
[106] |
Liu Z D, Li Z J, Wu K S, et al. Urban traffic prediction from mobility data using deep learning[J]. IEEE Network, 2018,32(4):40-46.
doi: 10.1109/MNET.2018.1700411 |
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