地球信息科学学报

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (10): 1898-1910.doi: 10.12082/dqxxkx.2022.210838

• 城市活力 • 上一篇    下一篇

基于微博大数据的北京市流动人口情绪与职住分布的关系研究

赵桐1,4(), 李泽峰3,5, 宋柳依1,4, 熊美成2,4, 廖一兰1, 裴韬1,4,*()   

  1. 1.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
    2.中国科学院地理科学与资源研究所 区域可持续发展分析与模拟重点实验室,北京 100101
    3.中国科学院空天信息创新研究院,北京 100094
    4.中国科学院大学 资源与环境学院,北京 100049
    5.中国科学院大学 电子电气与通信工程学院,北京 100049
  • 收稿日期:2021-12-30 修回日期:2022-02-24 出版日期:2022-10-25 发布日期:2022-12-25
  • 通讯作者: *裴韬（1972— ）,男,江苏扬州人,博士,研究员,主要从事地理大数据挖掘等方面的研究。E-mail: peit@lreis.ac.cn
  • 作者简介:赵桐（1998— ）,女,河北石家庄人,硕士生,主要从事空间数据挖掘等方面的研究。E-mail: zhaotong@lreis.ac.cn
  • 基金资助:
    国家自然科学基金项目(42071436)

Research on the Relationship between Floating Population's Sentiments and Distribution of Working and Living in Beijing based on Microblog Data

ZHAO Tong1,4(), LI Zefeng3,5, SONG Liuyi1,4, XIONG Meicheng2,4, LIAO Yilan1, PEI Tao1,4,*()   

  1. 1. State Key Laboratory of Resources & Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. Key Laboratory of Region Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
    3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    5. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-12-30 Revised:2022-02-24 Online:2022-10-25 Published:2022-12-25
  • Contact: PEI Tao
  • Supported by:
    National Natural Science Foundation of China(42071436)

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摘要：

流动人口的职住情绪能够反映其工作与生活状态。本研究首先基于2017年北京市微博大数据,利用jieba分词结合人工筛选得到流动人口发布的微博文本;其次,对Roberta-wwm-ext-large模型进行迁移学习识别北京全人群与流动人口的微博情绪;然后,结合POI数据与微博发布时间得到流动人口的职住分布;最后,基于微博情绪与职住分布得到流动人口的职住情绪,利用Getis-Ord Gi*挖掘职住情绪的空间聚集模式,采用地理探测器分析影响职住情绪热点分布的因素。实验表明,北京流动人口情绪均值（0.56）稍低于北京全人群（0.57）（P<0.01）,但整体表现为积极;从空间分布来看,流动人口在东、西城区情绪均衡,西北部科技创新区情绪相对于流动人口情绪均值较为低落,而东南部中心商务区、文化交流区及国际化社区情绪较高涨;从情绪与职住的关系来看,流动人口的工作情绪与从事的工作类型有关（q=0.03,P<0.05）,高新技术产业园、工业园、物流产业园的流动人口从业人员的工作情绪相对于流动人口工作情绪均值较为消极,健康产业园、文化创意产业园、农业园的流动人口从业人员的工作情绪较积极;流动人口的居住情绪与居住环境有关（q=0.06,P<0.1）,居住在远郊区的流动人口情绪相对于流动人口居住情绪均值较为消极,居住在近郊高密度的流动人口情绪较为积极。因此,相关部门应重点关注从事高新技术产业、工业、物流产业的流动人口以及居住在远郊区的流动人口。

关键词: 流动人口, 情绪分析, 职住分布, 北京市, 微博数据, 自然语言处理, 时空场景, 空间分析

Abstract:

The floating population is an essential part of the urban population, and their working and living status are of great significance to urban stability. The working and living status of the floating population can be directly reflected in their sentiments. On the contrary, their working and living status can also be detected from their sentiments. Firstly, we used jieba word separation technology and manual screening to obtain the microblog texts published by the Beijing floating population based on the microblog big data in 2017. Secondly, we identified the sentimental tendency of microblog texts for the whole population and the floating population in Beijing by transfer learning the natural language processing pre-training model (Roberta-wwm-ext-large). Then, we obtained the working and living distribution of the floating population with POI data and the published time of microblog texts. Finally, we got the floating population's working and living sentiments through their microblog sentiments and working and living distribution, mined the spatial aggregation pattern of their working and living sentiments with spatial analysis methods such as Getis-Ord Gi*, and analyzed the factors that may affect the hot spots' distribution of working and living sentiments of the floating population in Beijing with geodetector. The experiment shows that the average sentiment of the floating population in Beijing (0.56) is lower than that of the whole Beijing population (0.57) at 99.9% confidence level. Overall, the sentiments of the floating population are positive. As for the spatial distribution, the sentiments of the floating population in the core areas such as Dongcheng district and Xicheng district are balanced. The sentiments in the northwest technology and innovation district are more negative relative to the average sentiment of the floating population, while the sentiments in the southeast central business district, cultural exchange district, and the international community are more positive. In terms of the relationship between the floating population's sentiments and distribution of working and living, the working sentiments of the floating population are related to the type of work they are engaged in (q=0.03, P<0.05). In detail, the floating population working in high-tech industrial parks, industrial parks, and logistics industrial parks are more negative relative to the average working sentiment of the floating population, while those working in the health industrial parks, cultural and creative industrial parks, and agricultural parks are more positive. Besides, the living sentiments of the floating population are related to the living environment (q=0.06, P<0.1). The floating population living in the distance suburban residential area are more negative relative to the average living sentiment of the floating population, while those living in the near suburban high-density residential area are more positive. In general, the average living sentiment of the population (0.55) is significantly lower than the average working sentiment (0.58). Therefore, focusing on the floating population engaged in high-tech industry, industry, and logistics industry as well as improving the living satisfaction of the floating population living in the distance suburban residential area is vital for constructing a city with a stable work-life and livable environment.

Key words: floating population, sentiment analysis, working and living distribution, Beijing, microblog data, natural language processing, space-time scenario, spatial analysis