地理科学进展 ›› 2017, Vol. 36 ›› Issue (9): 1099-1110.doi: 10.18306/dlkxjz.2017.09.006

• 专辑:城市文化感知与计算 • 上一篇    下一篇

基于微博数据的北京市热点区域意象感知

谢永俊1(), 彭霞2,*, 黄舟1, 刘瑜1   

  1. 1. 北京大学遥感与地理信息系统研究所,北京 100871
    2. 北京联合大学旅游信息化协同创新中心,北京 100101
  • 出版日期:2017-09-27 发布日期:2017-09-27
  • 通讯作者: 彭霞 E-mail:afatpig@pku.edu.cn
  • 作者简介:

    作者简介:谢永俊(1993-),男,福建长汀人,本科生,主要研究方向为社交媒体地理数据挖掘、高性能地学计算,E-mail: afatpig@pku.edu.cn

  • 基金资助:
    国家自然科学基金项目(41501162);北京市社会科学基金项目(17JDGLB002);北京联合大学人才强校优选计划(BPHR2017DS08)

Image perception of Beijing's regional hotspots based on microblog data

Yongjun XIE1(), Xia PENG2,*, Zhou HUANG1, Yu LIU1   

  1. 1. Institute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China
    2. Collaborative Innovation Center of Tourism, Beijing Union University, Beijing 100101, China
  • Online:2017-09-27 Published:2017-09-27
  • Contact: Xia PENG E-mail:afatpig@pku.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No.41501162;Beijing Philosophy and Social Science Foundation, No.17JDGLB002;Premium Funding Project for Academic Human Resources Development in Beijing Union University, No.BPHR2017DS08

摘要:

“城市意象”研究对城市文化感知、城市管理与规划、旅游资源开发等具有重要意义。近年来,随着智能移动终端和社交媒体的普及,产生了大量城市内包含有文本和地理位置等信息的社交媒体数据,涉及城市的各个区域,为开展城市意象的综合感知研究提供了新的途径。本文以2016年北京市带位置签到的新浪微博数据为例,在空间聚类发现热点区域的基础上,采用词频—逆文件频率(TF-IDF)与文档主题生成模型LDA两类典型的文本分析的方法,挖掘城市不同热点区域的主题,以感知北京市不同热点区域的社会文化功能和人群行为,并在此基础上通过对热点区域高频主题词进行共词聚类分析,深度挖掘北京市的总体意象。研究表明,运用文本挖掘及地理大数据分析的城市意象研究方法,能及时感知人群在城市不同场所的活动、态度、偏好,从而揭示城市的社会文化及功能特征,是对刻画城市物质形态的城市意象五要素模型的重要补充。此外,以北京市热点区域为例的实证研究结果对现实中的城市特色传承与空间品质塑造等有一定的启发意义。

关键词: 地理空间数据, 社交媒体, 微博数据, 文本分析, 热点区域, 城市意象

Abstract:

Research on "city image" can facilitate urban culture perception, urban management and planning, and tourism resource development. In recent years, as intelligent mobile terminals and social media apps became increasingly popular, a large number of social media geo-tagged data containing text and location information have been generated, providing a new solution for city image perception studies. This article uses the social media geo-tagged data (Sina weibo check-in data in Beijing, 2016) to explore regional hotspots through spatial clustering, and mining the topics of different hotspots through two typical methods— term frequency-inverse document frequency (TF-IDF) and latent Dirichlet allocation (LDA). The results reflect the topics that users were concerned about and discussed in different places, revealing the culture, functions, and characteristics of diverse places of Beijing in great depth. The proposed city image abstraction approach by integrating text mining and spatiotemporal big data analysis can promptly expose the differences on themes of activities, attitudes, and preferences in different places in Beijing, thus reveal the social and cultural characteristics of the city. Our method is an important complement to the five-element model of city image, which focuses on the urban material form. In addition, the case study results of Beijing regional hotspots facilitate the preservation of city characteristics and shaping of space quality.

Key words: geospatial data, social media, microblog data, text mining, regional hotspot, city image