摘要
为深入挖掘西藏无线电频率使用率测试数据与区域经济发展的相关性,推进西藏自治区释放频谱资源使用经济价值,充分考虑频谱资源的空域、时域、频域和用户特征,结合西藏人口、面积等基本数据,构建了无线电频率使用率和区域经济发展关系指标体系进行相关性分析。采用主要成分分析方法,结合2018年西藏频率使用率专项测试数据,从宏观角度分析频谱资源利用与经济发展的关系,提取4个主要成分取代原12个指标,并计算指标权重,对西藏各地市的无线电频率利用率进行综合评分和排名,并与西藏地区生产总值(GDP)排名进行对比分析。分析表明,西藏自治区频率使用率情况与区域经济发展状况呈现一种正相关关系,其中,区域覆盖率和年时间占用度对经济发展有较大影响。
In order to deeply explore the correlation between the test data of Tibet's radio frequency utilization rate and regional economic development,and promote the release of the economic value of spectrum resources in the Tibet Autonomous Region,this paper fully considers the air domain,time domain,frequency domain and user characteristics of spectrum resources,and combines the basic data of Tibet's population and area to construct an index system for the relationship between radio frequency utilization rate and regional economic development for correlation analysis.Based on the major component analysis method and the special test data of frequency utilization rate in Tibet in 2018,this paper analyzes the relationship between frequency utilization rate and economic development from a macro perspective,extracts four major components instead of the original 12 indicators,calculates the weight of each principal component index,and comprehensively scores and ranks the radio frequency utilization rate of various cities in Tibet.And a comparative analysis of Tibet's GDP ranking.The analysis shows that there is a positive correlation between frequency utilization rate and regional economic development in Tibet Autonomous Region,in which regional coverage rate and annual time occupation have a great impact on economic development.
作者
扎西尼玛
高茹
Tashi Nyima;Gao Ru(Department of Economy and Information Technology of Tibet Autonomous Region,Lasa Tibet 850033,China)
出处
《现代工业经济和信息化》
2023年第7期279-281,共3页
Modern Industrial Economy and Informationization
基金
西藏自治区经济和信息化厅政府咨询服务项目,频谱使用与区域经济的相关性分析软课题项目(项目编号:JZD-AFXZDL-20221591)。
关键词
无线电频率使用率
经济发展
相关性
主成分分析
radio frequency utilization rate
economic development
correlation
principal component analysis