基于多维数据的特大城市建设用地类型识别*
Classifying Development-land Type of the Megacity through the Lens of Multisource Data
赵渺希
华南理工大学建筑学院 亚热带建筑科学国家重点实验室 教授
郭振松
深圳市城市规划设计研究院有限公司 硕士
梁景宇
华南理工大学建筑学院 硕士研究生
摘要: 随着信息技术和大数据应用的普及，城市规划编制技术也面临着进一步升级，如何将多维数据应用到城市规划成为当前
的热门议题。试图利用网络开放的地址解析数据和传统的现场踏勘校核结合的方式，通过快速的计算分析得到城市各类
建设用地的功能强度测定，进而实现城市各类建设用地性质的综合评定。以广州市天河区为实证案例，选取百度开放平台
数据、新浪微博签到数据和企业名录数据作为主要数据源，以现状道路中心线形成的街坊作为主要空间落位单元构建工
作底图，利用熵值赋权法和均方差法实现对天河区现状建设用地的功能强度测定和用地性质的综合评定。
Abstract: With the popularization of information technology and big-data application, urban planning technology is facing a further upgrade.
How to apply multidimensional data to megacities’ planning has become a hot topic. Traditional urban land use type analysis is
drawn through the way of field survey, which takes a lot of time and manpower. This paper attempts to calculate and analyze the
functional strength of various types of urban development land, and then to achieve the comprehensive assessment of various types of
development land by the network address analysis data and the traditional address analysis data. Taking Tianhe District of Guangzhou
as an empirical case, this paper selects Baidu LBS data, Sina microblog sign-in data and enterprise directory data as the main data
sources. Taking neighborhoods formed by the current road centerline as the main space unit, entropy weight method and mean square
deviation method are used to measure the functional strength of development land and make a comprehensive assessment of the land
type in Tianhe District.
关键词：特大城市、 建设用地、用地类型、多维数据、大数据
Keyword: Megacity， Development land，Classification of land use， Multisource data，Big data
中图分类号：TU981
文献标识码: A
资金资助
广东省科技计划项目 “基于大数据的城市商业游憩区（RBD）规划勘察技术集成应用” 2016A040403041
[1]李郇. 面临新型城镇化的三个规划转型问题[J]. 城
市与区域规划研究，2015，7（1）：1-15.
LI Xun. Three issues concerning planning transformation
in the context of new urbanization[J]. Journal of urban
and regional planning, 2015, 7 (1): 1-15.
[2]RATTI C, FRENCHMAN D, PULSELLI R M, et
al. Mobile landscapes: using location data from cell
phones for urban analysis[J]. Environment & Planning
B Planning & Design, 2006, 33 (5): 727-748.
[3]GRANT-MULLER S M, GAL-TZUR A, MINKOV
E, et al. Enhancing transport data collection through
social media sources: methods, challenges and
opportunities for textual data[J]. Iet Intelligent
Transport Systems, 2015, 9 (4): 407-417.
[4]MARK R. ALTAWEEL, LILIAN N, et al. Monitoring
land use: capturing change through an information
fusion approach[J]. Sustainability, 2010, 25.
[5]AHAS R, AASA A, ÜLAR MARK, et al. Seasonal
tourism spaces in Estonia: case study with mobile
positioning data[J]. Tourism Management, 2007, 28
(3): 898-910.
[6]AHAS R, AASA A, ROOSE A, et al. Evaluating passive mobile positioning data for tourism surveys:
an Estonian case study[J]. Tourism Management,
2008, 29 (3): 469-486.
[7]RUBIO A, SANCHEZ A, FRIAS-MARTINEZ E.
Adaptive non-parametric identification of dense
areas using cell phone records for urban analysis[J].
Engineering Applications of Artificial Intelligence,
2013, 26 (1): 551-563.
[8]WIDHALM P, YANG Y, ULM M, et al. Discovering
urban activity patterns in cell phone data[J].
Transportation, 2015, 42 (4): 597-623.
[9]裘炜毅，刘杰，张颖. 手机大数据视角下的工业区职
住平衡分析方法[C]//2015中国城市规划年会，2015.
QIU Weiyi, LIU Jie, ZHANG Ying. Analysis of job
balance in industrial area from the perspective of large
data of mobile phone[C]//Annual National Planning
Conference, 2015.
[10]丁亮，钮心毅，宋小冬. 上海中心城就业中心体系
测度——基于手机信令数据的研究[J]. 地理学
报，2016（3）：484-499.
DING Liang, NIU Xinyi, SONG Xiaodong.
Measuring the employment center system in Shanghai
central city: a study using mobile phone signaling
data[J]. Acta Geography Sinica, 2016 (3): 484-499.
[11]PUCCI P. Identifying communities of practice
through mobile phone data[J]. Urbe Revista Brasileira
De Gestão Urbana, 2014, 6 (524): 17.
[12]张鸿辉，黄军林，王柱，等. 基于大数据的城市设计
方法研究[C]//2016中国城市规划年会，2016.
ZHANG Honghui, HUANG Junlin, WANG Zhu, et
al. Research on urban design method based on large
data[C]//Annual National Planning Conference, 2016.
[13]陈映雪，甄峰. 基于居民活动数据的城市空间功
能组织再探究——以南京市为例[J]. 城市规划学
刊，2014（5）.
CHEN Yingxue, ZHEN Feng. Further investigation
into urban spatial function organization based on
residents’ activity data: a case study of Nanjing[J].
Urban Planning Forum, 2014(5).
[14]STEVEN D, SHULTZ, DAVID A, et al. The use of
census data for hedonic price estimates of open-space
amenities and land use[J]. The Journal of Real Estate
Finance and Economics, 2001, 222.
[15]钮心毅，李时锦，宋小冬，等. 城市工业用地调整
的空间决策支持——以广州为例[J]. 城市规划，
2011（7）：24-29.
NIU Xinyi, LI Shijin, SONG Xiaodong, et al. Spatial
decision support to adjustment of urban industrial
land: a case of Guangzhou[J]. CITY PLANNING
REVIEW, 2011 (7): 24-29.
[16]WANG Shuliang, WANG Xinzhou, SHI Wenzhong.
Development of a data mining method for land
control[J]. Geo-spatial Information Science, 2001, 41.
[17]VICTOR S, ENRIQUE FRIAS-MARTINEZ.
Automated land use identification using cell-phone
records[C]//Proceedings of the 3rd ACM international
workshop on MobiArch. ACM, 2011: 17-22.
[18]MILANO P D. Land use identification using mobile
phone data[J]. Italy.
[19]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.
NIU Xinyi, DING Liang, SONG Xiaodong.
Understanding urban spatial structure of Shanghai
central city based on mobile phone data [J]. Urban
Planning Forum, 2014 (6): 61-67.
[20]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 (21): 39-59.
[21]姚凯，钮心毅. 手机信令数据分析在城镇体系规
划中的应用实践——南昌大都市区的案例[J]. 上
海城市规划，2016（4）：91-97.
YAO Kai, NIU Xinyi. Practical analysis of mobile
phone signaling data in urban system planning: a case
study of Nanchang metropolis[J]. Shanghai Urban
Planning Review, 2016 (4): 91-97.
[22]FEICK R, ROBERTSON C. A multi-scale
approach to exploring urban places in geotagged
photographs[J]. Computers, Environment and Urban
Systems, 2013.
[23]ZHAN X, Ukkusuri S V, ZHU F. Inferring urban land
use using large-scale social media check-in data[J].
Networks and Spatial Economics, 2014, 14 (3): 647-667.
[24]曹瑞，涂伟，巢佰崇，等. 基于智能卡数据的地铁
周边职住用地识别与分析[J]. 测绘地理信息，
2016（3）：74-78 .
CAO Rui, TU Wei, CHAO Baichong, et al.
Identification and analysis of home and work regions
in the vicinity of metro stations using smart card
data[J]. Journal of Ueomatics, 2016 (3): 74-78 .
[25]韩昊英，于翔，龙瀛. 基于北京公交刷卡数据和兴趣
点的功能区识别[J]. 城市规划，2016，40（6）：52-60.
HAN Haoying, YU Xiang, LONG Ying. Identifying
urban functional zones using bus smart card data and
points of interest in Beijing[J]. City Planning Review,
2016, 40 (6): 52-60.
[26]孙利娟，邢小军，周德群. 熵值赋权法的改进[J]. 统
计与决策，2010，2010（21）：153-154.
SUN Lijuan, XING Xiaojun, ZHOU Dequn.
Improvement of entropy weighting method[J].
Statistic & Decision, 2010, 2010 (21): 153-154.
[27]王明涛. 多指标综合评价中权数确定的离差、均方
差决策方法[J]. 中国软科学，1999（8）：100-101.
WANG Mingtao. Deviation and weighted variance
decision method for weight determination in multiindex
comprehensive evaluation[J]. China Soft
Science, 1999 (8): 100-101.
[28]赵渺希，郭振松. 一种基于地址解析数据
的建设用地类型快速识别方法[P]. 广东：
CN104750799A，2015-07-01.
ZHAO Miaoxi, GUO Zhensong. A method for
rapid identification of construction land type
based on address analysis data[P]. Guangdong:
CN104750799A, 2015-07-01.
