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基于CiteSpace的国内夜间灯光数据研究知识图谱分析 被引量:5

Analysis of Knowledge Graph of Domestic Night Light Data Research Based on CiteSpace
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摘要 借助CiteSpace软件工具梳理国内夜间灯光数据研究进展与热点。研究发现:1)大数据时代的来临助推了夜间灯光数据研究,国内夜间灯光数据研究成果主要集中于地理类、测绘类、经管类三大类期刊,作者间表现出“大分散、小聚合”特征,彼此间联系较为分散,国内研究机构间成果差异比较明显;2)DMSP/OLS是目前夜间灯光数据研究领域中的主流数据源,当前夜间灯光数据主要集中于城市发展、社会经济指标、人口发展、生态环境评估等领域,但研究方法比较单一,尤其是在技术处理上有待加强。NPP/VIIRS数据源对DMSP/OLS数据源有替代与延续的意义,未来应注重学科间的交叉融合,回归于数据技术处理,加强DMSP/OLS与NPP/VIIRS等其余夜间灯光数据源的融合。 with the help of CiteSpace software tools,this paper reviews the research progress and hot spots of nighttime lighting data in China.The research findings are as follows:(1)the coming of big data era boosts the research of nighttime lighting data.Domestic nighttime lighting data research mainly focuses on three categories of journals:geography,surveying and mapping,and economic management.The authors show the characteristics of"large dispersion and small aggregation",and their relations are relatively scattered.There are obvious differences among domestic research institutions;(2)DMSP/OLS is the mainstream data source in the field of nighttime lighting data research.At present,nighttime lighting data mainly focus on urban development,socio-economic indicators,population development,ecological environment assessment and other fields,but the research methods are relatively single,especially in the field of data technology processing,which needs to be strengthened.NPP/VIIRS data source has the significance of substitution and continuation for DMSP/OLS data source.In the future,we should pay attention to interdisciplinary cross fusion,return to data technology processing,and strengthen the fusion of other nighttime light data sources such as DMSP/OLS and NPP/VIIRS.
作者 曾冰 ZENG Bing(Jiangxi Institute of Economic Development,Jiangxi University of Finance and Economics,Nanchang 330013,China)
出处 《测绘与空间地理信息》 2021年第3期7-11,共5页 Geomatics & Spatial Information Technology
基金 国家自科基金项目——省际交界区空间结构形成演进与优化整合研究(71703061)资助。
关键词 夜间灯光数据 CITESPACE 关键词 共现 图谱 nighttime light data CiteSpace keywords co-occurrence mapping
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