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基于多产业生产总值的甘肃省经济评价与分区 被引量:2

Economic evaluation and division of Gansu province based on gross production value of multiple industries
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摘要 利用4阶样条基将甘肃生产总值指数转化为函数,绘制相平面图,从函数一阶导数与二阶导数深入探讨甘肃生产总值指数的动态规律.对于2012年、2017年与2018年甘肃各地市的多产业人均生产总值,选取自适应加权主成分得分距离对各地市进行系统聚类,以主成分综合得分进行排名并构造新的地区差异测度.研究结果表明,甘肃生产总值指数整体呈现上升趋势,但近年来第二产业形势严峻,具有很大下行压力,尽管2018年各项经济指标有一定好转,但国内外形势依然严峻;甘肃省人均经济发展较好城市除兰州外都是矿产资源型城市,可持续性较差;整体而言,甘肃省的经济系统稳定,各地市排名变化很小,地区差异显著存在,但呈缩小趋势. The observed data of Gansu′s GDP index are transformed into functions by using the 4th-order spline base,and the phase plane diagram is drawn.The dynamic law of Gansu′s GDP index is discussed in depth from the first derivative and the second derivative of functions.About per capital GDP of the multi-industry for all cities in Gansu Province in 2012,2017and 2018,the self-adaptive weighted principal component distance is selected to cluster systematically,and the principal component comprehensive score is used to rank and construct a new regional difference measure CVX.The results show that Gansu′s GDP index shows an upward trend as a whole and has a significant positive correlation between the secondary industry and the tertiary industry.However,in recent years,the form of the secondary industry is severe and has a great downward pressure.Although various economic indicators have improved in 2018,the situation at home and abroad is still grim.The cities with better per capita economic development in Gansu Province are mineral resource-based cities with poor sustainability except Lanzhou;Overall,Gansu′s economic system is stable,regional rankings change little,and the regional differences are significant,but it is shrinking.
作者 魏艳华 王丙参 WEI Yanhua;WANG Bingcan(School of Mathematics and Statistics,Tianshui Normal University,Tianshui Gansu 741001;School of Statistics,Capital University of Economics and Business,Beijing 100070)
出处 《宁夏师范学院学报》 2020年第7期80-90,共11页 Journal of Ningxia Normal University
基金 国家自然科学基金资助(11665019).
关键词 生产总值 函数型数据 主成分得分 聚类 综合评价 Gross product Functional data Principal component score Clustering Comprehensive evaluation
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