丁亮 钮心毅 宋小冬

Ding Liang, Niu Xinyi, Song Xiaodong

关键词：移动定位；大数据；城市空间研究；城市规划

Keywords：Mobile Location; Big Data; Urban Spatial Studies; Urban Planning

摘要：

聚焦城市空间的移动定位大数据研究可分为空间现象描述、空间功能识别、理论模型验证、中心体系分析4 种类型。通过文献梳理，发现大数据为空间研究提供了丰富的样本，但当前的数据存在非全样本、缺少社会经济属性、非随机缺失的缺陷。大数据研究的广度和深度正在不断扩展，呈现出多学科参与的特点，但研究结论还缺少新的理论探索和解决实际问题的应用。据此提出当前的大数据只是传统数据的有益补充，适用于描述、分析空间现象和规律，适宜于两方面研究：①验证理论模型、提出研究问题；②分析空间现状、评估空间规划。这两方面研究可通过统计汇总和空间计算的方法实现。

Abstract：

The study on the big data of mobile location in urban space can be decomposed into four types, including description of spatial phenomena, identification of spatial function, verification of theoretical models, and analysis of center system. The analysis on relevant literature finds that big data have provided abundant samples for space research, but present data face the defects including incomplete samples, lack of socio-economic property, and not missing at random. Besides, though big data study expands in both depth and breadth and shows the characteristic of multi-disciplinary cooperation, the research conclusion pays little attention to exploring new theories and addressing practical problems. Thus, present big data are only the beneficial supplement to traditional data, applicable to describing and analyzing space phenomena and rule. Meanwhile, the big data are suitable not only to verify theoretical models and raise research problems, but also to analyze current space situation and evaluate spatial planning. The study on these two aspects can be implemented through statistical summary and spatial computation.

丁亮，同济大学建筑与城市规划学院，博士研究生。1310147dl@tongji.edu.cn
钮心毅，同济大学建筑与城市规划学院，副教授。niuxinyi@tongji.edu.cn
宋小冬，同济大学建筑与城市规划学院，教授，博士生导师。spt@tongji.edu.cn

• [1] 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, 09: 1352-1361.
  [2] 叶宇, 魏宗财, 王海军. 大数据时代的城市规划响应[J]. 规划师, 2014,
  08: 5-11.
  [3] 冉斌, 邱志军, 裘炜毅，等. 大数据环境下手机定位数据在城市规划中
  实践[C] // 中国城市规划学会. 2013 中国城市规划年会论文集, 2013.
  [4] 任颐, 毛荣昌. 手机数据与规划智慧化——以无锡市基于手机数据的
  出行调查为例[J]. 国际城市规划, 2014, 6: 66-71.
  [5] Ahas R, Mark U. Location Based Services - New Challenges for Planning
  and Public Administrations? [J] Futures, 2005, 37: 547-561.
  [6] Ratti C, Frenchman D, Pulselli R M, et al. Mobile Landscapes: Using
  Location Data from Cell Phones for Urban Analysis [J]. Environment and
  Planning B-Planning & Design, 2006, 33 (5): 727-748.
  [7] Vieira M R, Frias-Martinez V, et al. Characterizing Dense Urban Areas from
  Mobile Phone-Call Data: Discovery and Social Dynamics [C]. IEEE. Second
  International Conference on Social Computing(SocialCom), Minneapolis:
  (IEEEXplore), 2010.
  [8] Sevtsuk A, Ratti C. Does Urban Mobility Have a Daily Routine? Learning
  from the Aggregate Data of Mobile Networks [J]. Journal of Urban
  Technology, 2010, 17 (1): 41-60.
  [9] Krisp J M. Planning Fire and Rescue Services by Visualizing Mobile
  Phone Density [J]. Journal of Urban Technology, 2010, 17(1): 61-69.
  [10] Becker R A, Caceres R, Hanson K, et al. A Tale of One City: Using Cellular
  Network Data for Urban Planning [J]. IEEE Pervasive Computing, 2011(4): 18-26.
  [11] Sagl G，Resch B，Hawelka B, et al. From Social Sensor Data to
  Collective Human Behavior Patterns: Analysing And Visualising Spatio-
  Temporal Dynamics in Urban Environments [C] // Proceedings of the GIForum
  2012: Geovisualization，Society and Learning，2012: 54-63.
  [12] Sagl G，Delmelle E，Delmelle E. Mapping Collective Human Activity in
  an Urban Environment Based on Mobile Phone Data [J]. Cartography and
  Geographic Information Science, 2014, 41(3): 272-285.
  [13] Manfredini F, Pucci P, Tagliolato P. Toward a Systemic Use of Manifold
  Cell Phone Network Data for Urban Analysis and Planning [J]. Journal of
  Urban Technology, 2014, 21(2): 39-59.
  [14] John D, Peter H, Ronan F, et al. Population Mobility Dynamics Estimated
  from Mobile Telephony Data [J]. Journal of Urban Technology, 2014,
  21(2):109-132.
  [15] Kang C G, Liu Y, Ma X J, et al. Towards Estimating Urban Population
  Distributions from Mobile Call Data [J]. Journal of Urban Technology,
  2012, 19(4): 3-21.
  [16] Reades J, Calabrese F, Sevtsuk A, et al. Cellular Census: Explorations in
  Urban Data Collection [J]. IEEE Pervasive Computing, 2007, 6(3): 30-38.
  [17] Reades J, Calabrese F, Ratti C. Eigenplaces: Analysing Cities Using the
  Space-Time Structure of the Mobile Phone Network [J]. Environment And
  Planning B-Planning & Design, 2009, 36 (5): 824-836.
  [18] Qi G D, Li X L, Li S J, et al. Measuring Social Functions of City
  Regions from Large-Scale Taxi Behaviors [C] // 2011 IEEE International
  Conference on Pervasive Computing and Communications Workshops
  (PERCOM Workshops), 2011: 384-388.
  [19] Liu Y, Wang F H, Xiao Y. Urban Land Uses and Traffic 'Source-Sink
  Areas': Evidence from GPS-Enabled Taxi Data in Shanghai [J]. Landscape
  and Urban Planning, 2012, 106 (1): 73-87.
  [20] Pei T, Sobolevsky S, Ratti C, et al. A New Insight into Land Use
  Classification based on Aggregated Mobile Phone Data [J]. International
  Journal of Geographical Information Science, 2014(5): 1-20.[21] Ratti C, Sobolevsky S, Calabrese F, et al. Redrawing the Map of Great Britain
  from a Network of Human Interactions [J]. Plos One, 2010, 5 (12): 1-6.
  [22] Calabrese F, Di L G, Liu L, et al. Estimating Origin-Destination Flows
  Using Mobile Phone Location Data [J]. IEEE Pervasive Computing. 2011,
  10 (4): 36-44.
  [23] Kang C G, Zhang Y, Ma X J, et al. Inferring Properties and Revealing
  Geographical Impacts of Intercity Mobile Communication Network of
  China Using a Subnet Data Set [J]. International Journal of Geographical
  Information Science, 2013, 27 (3): 431-448.
  [24] Gao S, Liu Y, Wang Y L, et al. Discovering Spatial Interaction
  Communities from Mobile Phone Data [J]. Transactions in GIS, 2013, 17
  (3): 463-481.
  [25] Calabrese F, Diao M, Di L G. Understanding Individual Mobility Patterns
  from Urban Sensing Data: A Mobile Phone Trace Example [J]. Transportation
  Research Part C-Emerging Technologies, 2013, 26(1): 301-313.
  [26] Liu L, Biderman A, Ratti C. Urban Mobility Landscape: Real Time
  Monitoring of Urban Mobility Patterns [C] // 11th International Conference
  on Computers in Urban Planning and Urban Management, 2009: 16-18.
  [27] Roth C, Kang S M, Batty M, et al. Structure of Urban Movements: Polycentric
  Activity and Entangled Hierarchical Flows [J]. Plos One, 2011, 6 (1): 1-8.

  [28] Zhong C, Arisona S M, Huang X F, et al. Detecting the Dynamics of Urban
  Structure through Spatial Network Analysis [J]. International Journal of
  Geographical Information Science, 2014, 28(1): 2178-2199.
  [29] Batty M. The New Science of Cities [M]. The MIT Press, 2013.
  [30] Mayer-Schonberger V, Cukier K. Big Data: The Next Frontier for
  Innovation, Competition, and Productivity [M]. Eamon Dolan / Houghton
  Mifflin Harcourt, 2013.
  [31] Long Y, Thill J C. Combining Smart Card Data and Household Travel
  Survey to Analyze Jobs-Housing Relationships in Beijing. Beijing City
  Lab: Working Paper # 4, 2013.
  [32] Ahas R, Silm S, Jarv O, et al. Using Mobile Positioning Data to Model
  Locations Meaningful to Users of Mobile Phones [J]. Journal of Urban
  Technology, 2010, 17(1): 3-27.
  [33] 钮心毅，丁亮，宋小冬. 基于手机数据识别上海中心城的城市空间结
  构[J]. 城市规划学刊, 2014(6) ：61-67.

• 收起