地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1339-1348.doi: 10.12082/dqxxkx.2020.190608

• 大数据与城市管理 • 上一篇    下一篇

基于POI数据的城市建筑功能分类方法研究

曹元晖1, 刘纪平1,2,*(), 王勇1, 王良杰3, 吴文周4, 苏奋振4   

  1. 1. 中国测绘科学研究院,北京 100830
    2. 河南省科学院地理研究所,郑州 450052
    3. 清华大学环境学院,北京 100084
    4. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
  • 收稿日期:2019-10-17 修回日期:2019-12-09 出版日期:2020-06-25 发布日期:2020-08-25
  • 通讯作者: 刘纪平 E-mail:liujp@casm.ac.cn
  • 作者简介:曹元晖(1995— ),女,安徽合肥人,硕士生,从事空间数据分析与挖掘研究。E-mail: caoyuanhui17@mails.ucas.ac.cn
  • 基金资助:
    国家重点研发计划项目(2017YBF0503601);国家重点研发计划项目(2017YFB0503502);中国测绘科学研究院基本科研业务费项目(AR1904)

A Study on the Method for Functional Classification of Urban Buildings by Using POI Data

CAO Yuanhui1, LIU Jiping1,2,*(), WANG Yong1, WANG Liangjie3, WU Wenzhou4, SU Fenzhen4   

  1. 1. Chinese Academy of Surveying and Mapping, Beijing 100830, China
    2. Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China
    3. School of Environment, Tsinghua University, Beijing 100084, China
    4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2019-10-17 Revised:2019-12-09 Online:2020-06-25 Published:2020-08-25
  • Contact: LIU Jiping E-mail:liujp@casm.ac.cn
  • Supported by:
    National Key Research and Development Program of China(2017YBF0503601);National Key Research and Development Program of China(2017YFB0503502);The Basal Research Fund of Chinese Academy of Surveying and Mapping(AR1904)

摘要:

对建筑物进行建模与分析是智慧城市建设的重要任务之一。将城市中数量庞大的建筑物按功能分类,辅助认知城市内部空间结构,对政府部门开展人口估计,土地管理,城市规划等工作具有重要意义。本文以蕴含丰富语义信息的兴趣点(POI, Point of Interest )作为主要信息源,针对POI分布稀疏导致大量建筑物无法识别出功能的问题,改进了传统的城市功能区定量识别方法。该方法为建筑物内部及周边一定区域范围内的POI赋予反距离权重,通过计算不同类型POI的加权频数密度比例来识别建筑物功能类型。文中以北京市西四环中路附近5000多栋建筑物为例进行实验验证,实现了将目标区域内的建筑物按功能类型划分为居住、商业、公服和3种混合类型,识别率达93.04%,与人工判别的结果对比得出总体分类精度达91.18%。该方法采用易于获取的互联网POI数据,可以实现大范围建筑物功能类型的快速自动化识别,丰富了城市建筑模型语义属性,扩展了POI数据的应用范围。

关键词: 建筑功能分类, 兴趣点, 指标频数密度, 反距离加权, 城市建筑物, 城市规划, 城市功能区, 空间分布

Abstract:

As the carrier of human activity and social development, buildings are the most important geographical entities that constitute the spatial structure of a city. It is one of the urgent tasks in the construction of smart cities in China to build elaborate digital models of urban buildings. Classifying a large amount of buildings by their functions facilitates urban functional area division and urban spatial cognition, thus assisting the government in population estimation, land management, urban planning, and smart city construction. In this paper, POI (Point of Interest) with rich semantic information including name, address, and types was used as the main data source, because it was more accessible and updated more frequently than the traditional geographic information data. The process of finding out the functional type of a building was similar with identifying urban functional areas by using POI data, but there existed the problem of low classification rate due to the sparsity of POI. Therefore, to improve the traditional quantitative identification of urban functional areas, this study attempted to calculate the weighted frequency density ratio of each type of POIs inside and within a certain range around a building. Experimenting on more than 5000 buildings near South Shawo Bridge in the west of Beijing, the study found that 93.04 percent of the buildings were effectively classified into different functional types: residential, commercial, public service, and other three mixed types. The classification rate has been greatly improved compared with that of the traditional method. These classified buildings showed the spatial distribution of functional areas more clearly and precisely than blocks used in identifying urban functional areas, since too many multi-functional blocks with very limited practical meaning were identified by using the traditional method. In order to calculate the classification accuracy, more than 2000 randomly selected buildings were manually divided into functional classes with the assistance of POI and AOI data. The overall classification accuracy reached 91.18 percent compared with the manually classified result. The classification error was mainly caused by the shortage of POI and the poor data quality, which could be avoided by merging multi-source POI to improve the data quality or applying various Internet location information, such as the social media data and the real estate transaction data. However, by using easily accessible web POI data, the proposed method, which can replace manual classification in an automated way, has greatly improved the effectiveness of classifying large number of buildings into different functional types, and shown higher accuracy than existing researches.

Key words: the functional classification of building, Point of Interest, frequency density, inverse distance weight, urban buildings, urban planning, urban functional districts, spatial distribution