龙瀛 刘伦

Long Ying, Liu Lun

关键词：大数据；开放数据；城市规划；大模型；众包

Keywords：Big Data; Open Data; Urban Planning; Mega-model; Crowd-sourcing

摘要：

本文阐述了近年来新数据环境下的城市研究变革。首先介绍了大数据和开放数据形成的新数据环境和国内外定量城市研究概况，然后围绕典型案例对当前定量城市研究的四项变革及相关实践展开讨论，最后提出相关思考。本文认为，新数据环境推动了定量城市研究的四大变革：（1）空间尺度上由小范围高精度、大范围低精度到大范围高精度的变革；（2）时间尺度上由静态截面到动态连续的变革；（3）研究粒度上由“以地为本”到“以人为本”的变革；（4）研究方法上由单一团队到开源众包的变革。在变革的同时，当前定量城市研究也面临着数据有偏、多现状研究少远景判断、多客观认识少规划启示，以及规划理论和学科发展相关问题。

Abstract：

The paper provides an overview on the transformation of Chinese urban study driven by the emergence of new data environment in China in recent years. We firstly give a brief introduction on the new data environment, which has been made possible by the availability of big data and open data in recent years, as well as a review on the research progress both in China and abroad. It is followed by an analysis on four major transformations in quantitative urban study, supported by typical research cases, which are (1) transformation in spatial scale from high resolution but small coverage or wide coverage but low resolution to wide coverage with high resolution; (2) transformation in temporal scale from static cross-sectional to dynamic consistent; (3) transformation in granularity from land-oriented to human-oriented; (4) transformation in methodology from conventional research group to crowd-sourcing. The paper also points out that quantitative urban research is faced with problems like data bias, lack of long term analysis, lack of linkage to planning practice, etc.

龙瀛（通信作者），博士，清华大学建筑学院，副教授。longying1980@gmail.com
刘伦，剑桥大学土地经济系，博士研究生。ll454@cam.ac.uk

• [1] 刘伦, 龙瀛, 麦克·巴蒂. 城市模型的回顾与展望——访谈麦克·巴蒂之后的新思考[J]. 城市规划, 2014(8): 63-70.
  [2] 甄峰, 秦萧, 王波. 大数据时代的人文地理研究与应用实践[J]. 人文地理, 2014, 29(3): 1-6.
  [3] Batty M, Axhausen K W, Giannotti F, et al. Smart Cities of the Future[J]. The European Physical Journal Special Topics, 2012, 214(1): 481-518.
  [4] Zhou J, Wang M, Long Y. Big Data for Intrametropolitan Human Movement Studies: A Case Study of Bus Commuters Based on Smart Card Data. Beijing City Lab, Working Paper #50, 2014.
  [5] Long Y, Liu X, Zhou J, et al. Profiling Underprivileged Residents with Mid-term Public Transit Smartcard Data of Beijing[J]. arXiv Preprint arXiv: 14095839, 2014.
  [6] 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(003): 70-77.
  [7] Kang C, Liu Y, Ma X, et al. Towards Estimating Urban Population Distributions from Mobile Call Data[J]. Journal of Urban Technology, 2012, 19(4): 3-21.
  [8] 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(006): 61-67.
  [9] 王德, 朱玮, 谢灿栋, 等. 基于手机信令数据的城市空间分析框架、难点及初步进展. “智能城镇化”第70期可持续发展沙龙, 2014.
  [10] Wang J, Mao Y, Li J, et al. Predictability of Road Traffic and Congestion in Urban Areas. Beijing City Lab, Working Paper #55, 2014.
  [11] 郭文伯, 张艳, 柴彦威, 等. 基于 GPS 数据的城市郊区居民日常活动时空特征——以北京天通苑, 亦庄为例[J]. 地域研究与开发, 2013, 32(6): 159-164.
  [12] Long Y, Liu X. Automated Identification and Characterization of Parcels (AICP) with OpenStreetMap and Points of Interest[J]. Environment and Planning B: Planning & Design, 2015, 38: 498-510.
  [13] Long Y, Han H, Tu Y, et al. Evaluating the Effectiveness of Urban Growth Boundaries Using Human Mobility and Activity Records[J]. Cities, 2015, 46: 76-84.
  [14] 王波, 甄峰, 张浩. 基于签到数据的城市活动时空间动态变化及区划研究[J]. 地理科学, 2015, 35(2): 151-160.
  [15] 陈映雪, 甄峰, 王波, 等. 基于社会网络分析的中国城市网络信息空间结构[J]. 经济地理, 2013, 33(4): 56-63.
  [16] 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.
  [17] 李栋. 基于互联网春运迁徙数据的城镇体系规划思考. 大数据与未来人居研讨会, 2015.
  [18] 秦萧, 甄峰, 朱寿佳, 等. 基于网络口碑度的南京城区餐饮业空间分布格局研究[J]. 地理科学, 2014, 34(7): 810-817.
  [19] Sheng Q, Li M, Zhang X. Location does not Matter in the Informational Age?—A Case Study on the Distribution of Restaurants Listed in ‘Dazhongdianping’ in Beijing. Beijing City Lab, Working Paper #61, 2015.
  [20] Long Y, Wang J, Wu K, et al. Population Exposure to Ambient PM 2.5 at the Subdistrict Level in China[OL]. 2014. http://ssrncom/abstract=2486602.
  [21] 龙瀛. 中国人口密度的时空演变与城镇化空间格局初探：2000-2010. 城乡治理与规划改革——中国城市规划年会. 2014.
  [22] 袁媛, 许学强, 薛德升. 转型时期广州城市户籍人口新贫困的地域类型和分异机制[J]. 地理研究, 2008, 27(3): 672-682.
  [23] 谌丽, 张文忠, 杨翌朝. 北京城市居民服务设施可达性偏好与现实错位[J]. 地理学报, 2013, 68(8): 1071-1081.
  [24] 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): e14248.
  [25] 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, 10(4): 18-26.
  [26] Rozenfeld H D, Rybski D, Gabaix X, et al. National Bureau of Economic Research. 2009.
  [27] Sagl G, Resch B, Hawelka B, et al. From Social Sensor Data to Collective Human Behaviour Patterns: Analysing and Visualising Spatio-Temporal Dynamics in Urban Environments[C] // Proceedings of the Proceedings of the GI-Forum 2012: Geovisualization, Society and Learning. 2012.
  [28] 龙瀛, 吴康, 王江浩, 等. 大模型：城市和区域研究的新范式[J]. 城市规划学刊, 2014(006): 52-60.
  [29] 高晓路, 许泽宁, 牛方曲. 基于“点—轴系统”理论的城市群边界识别[J]. 地理科学进展, 2015, 34(3): 280-289.
  [30] Long Y, Wu K, Mao Q. Simulating Urban Expansion in the Parcel Level for All Chinese Cities[J]. arXiv Preprint arXiv: 14023718, 2014.
  [31] Long Y, Shen Y. Mapping Parcel-level Urban Areas for a Large Geographical Area[J]. arXiv Preprint arXiv: 14035864, 2014.
  [32] Han H-Y, Lai S-K, Dang A-R, et al. Effectiveness of Urban Construction Boundaries in Beijing: An Assessment[J]. Journal of Zhejiang University Science A, 2009, 10(9): 1285-1295.
  [33] Long Y, Gu Y, Han H. Spatiotemporal Heterogeneity of Urban Planning Implementation Effectiveness: Evidence from Five Urban Master Plans of Beijing[J]. Landscape and Urban Planning, 2012, 108(2): 103-111.
  [34] 徐毅松, 石崧, 范宇. 新形势下上海市城市总体规划方法论探究[J]. 城市规划学刊, 2009, 2: 10-15.
  [35] Tian L, Shen T. Evaluation of Plan Implementation in the Transitional China: A Case of Guangzhou City Master Plan[J]. Cities, 2011, 28(1): 11-27.
  [36] Bagchi M, White P. What Role for Smart-card Data from Bus Systems?[J]. Municipal Engineer, 2004, 157(1): 39-46.
  [37] 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): 1339-52.
  [38] Gonzalez M C, Hidalgo C A, Barabasi A-L. Understanding Individual Human Mobility Patterns[J]. Nature, 2008, 453(7196): 779-782.
  [39] 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 and Design, 2009, 36(5): 824-836.
  [40] Phithakkitnukoon S, Horanont T, Di Lorenzo G, et al. Activity-aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data[M] // Salah A A, Gevers T, Sebe N, et al, eds. Human Behavior Understanding. Springer, 2010: 14-25.
  [41] 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): e15923.
  [42] Masucci A P, Stanilov K, Batty M. Limited Urban Growth: London’s Street Network Dynamics Since the 18th Century[J]. PLOS One, 2013, 8(8): e69469.
  [43] Long Y, Jin X, Yang X, et al. Reconstruction of Historical Arable Land Use Patterns Using Constrained Cellular Automata: A Case Study of Jiangsu, China[J]. Applied Geography, 2014, 52: 67-77.
  [44] 林忆南, 金晓斌, 杨绪红, 等. 清代中期建设用地数据恢复与空间网格化重建：方法与实证[J]. 地理研究, 2015 (12): 2329-2342.
  [45] Calabrese F, Pereira F C, Di Lorenzo G, et al. The Geography of Taste: Analyzing Cell-phone Mobility and Social Events[J]. 2010, 6030: 22-37.
  [46] Calabrese F, Smoreda Z, Blondel V D, et al. Interplay Between Telecommunications and Face-to-Face Interactions: A Study Using Mobile Phone Data[J]. PLOS One, 2011, 6(7): e20814.
  [47] Schneider C M, Belik V, Couronn T, et al. Unravelling Daily Human Mobility Motifs[J]. Journal of The Royal Society Interface, 2013, 10(84): 1-8.
  [48] Liu L. C-Image: City Cognitive Mapping Through Geo-tagged Photos[D]. Massachusetts Institute of Technology, 2014.
  [49] 茅明睿, 储妍. 规划云平台：“互联网+”规划公众参与的实践[R]. 北京市城市规划设计研究院, 2015.
  [50] 龙瀛, 沈尧. 数据增强设计——新数据环境下的规划设计回应与改变[J]. 上海城市规划, 2015, 2: 81-87.

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