基于知识图谱分析的人口数据空间化研究进展

Advancements in spatial analysis of population data based on knowledge graphs

以1990~2020年736篇中国知网期刊论文和606篇Wo S核心数据库期刊论文作为研究样本,利用CiteSpace和VOSviewer,从现状和热点两方面系统梳理了人口数据空间化的研究进展。结果表明:(1)1990~2020年,关于人口数据空间化的研究数量总体呈稳步增长趋势,尤其是2015年以来,中英文文献的年均发表数量超过40篇,人口数据空间化研究主题得到国内外学者的广泛关注;1990~2020年,中文文献作者与机构合作网络均呈多中心、小聚集的特征,英文文献作者与机构合作网络则更为紧密。(2)从研究热点看,多源数据与大数据等成为新兴的关键词,以机器学习为代表的智能化研究方法逐渐成为主流方向。

The spatial decomposition of demographic data is a classic and crucial problem in the field of geographical information science.While some literature reviews exist,they are dated and fail to reflect the latest hotspots and research trends.Thus,there is a need to review the latest international progress on spatial decomposition of demographic data.This study aims to depict the research trajectories of spatial decomposition of demographic data by employing scientometrics method,such as literature citation network and keyword co-occurrence network.The study utilizes the knowledge graph tools like CiteSpace and VOSviewer.A total of 736 CNKI journal papers and 606 WoS core database journal papers published between 1990 and 2020 were selected as research samples.The main conclusions are as follows.From 1990 to 2020,the number of research results on the spatialization of population data exhibited a steady growth trend and reached its peak in 2020,indicating widespread interest from scholars both domestically and internationally in this topic.In the Chinese literature,the author cooperation network appeared more decentralized,whereas in the English literature,it displayed greater aggregation.Links between research institutions in the Chinese literature were characterized by polycentricity and small aggregation,while the cooperation between research institutions in the English literature was closer.Collaboration among research institutions from different regions and disciplines would undoubtedly foster innovation and progress.Therefore,it is recommended that China strengthens exchanges and cooperation with both domestic and foreign research institutions to jointly promote progress in the field of research on the spatialization of demographic data.In terms of data sources,compared to traditional data sources like land use data,remote sensing data,and DEM data,multi-source big data,such as mobile phone data,taxis trajectory data,Mobike trip trajectory data,TenCent LBS big data,has emerged as prominent keywords.Moreover,intelligent research methods such as random forests and deep neural networks were gradually replacing traditional modeling approaches like multiple regression models and land use inversion.Fine-scale research,global population distribution mapping,and demographic sociological problems have become priority areas within the spatialization of population data.Its applications were shifting from traditional areas such as urban planning and disaster assessment to newer areas such as population exposure,equity,and health.Currently,China is confronted with social problems such as the equalization of public health services,population aging,and policy adjustments,presenting new challenges and opportunities for the study of spatialization of demographic data.Therefore,it is recommended that issues such as equity in population well-being and population health should be priority study areas for Chinese scholars through cross-disciplinary research by integrating sociology,demography,geography,and public health.