论文标题
通过主要组件建模在俄罗斯联邦中对区域群集结构进行分析
Analysis of Regional Cluster Structure By Principal Components Modelling in Russian Federation
论文作者
论文摘要
在本文中，证明主组件分析在区域群集建模和分析中的应用在几个参数之间具有显着多重共线性的情况下至关重要，尤其是当在数十个中测量区域数据的维度时。提出的主成分模型允许对区域聚类的同样质量表示。实际上，群集变得更加独特，明显的离群值会随着组件模型聚类而变得更加明显，或者通过各自的层次结构群集缓解。因此，在俄罗斯联邦和19个社会经济参数的85个区域获得了一个五组分模型。主组件允许描述大约75％的初始参数变化，并在研究变量上进行进一步的模拟。对主要组成部分建模的集群分析使俄罗斯联邦的区域结构和经济发展中的差异更好，包括四个主要集群：少数数字最高的最高发展区域，中较低和低经济发展的群集以及“最贫穷”地区。可以观察到，大多数地区的发展依赖于资源经济，工业潜力和区域间基础设施潜力并未充分实现，而只有最富有的地区显示出高度发达的经济，而其他地区的行业则显示出停滞的迹象，这是由于经济避难所所带来的，这是进一步扩展的，这是由于经济避难所所带来的，COVID-COVID-CODID-COVID-CID-19 PANDECTIC。大多数俄罗斯地区都需要额外的公共支持和工业发展，因为它们的资本资产潜力受到阻碍，并且，尽管拥有足够的劳动资源，但其捐助力将增加。
In this paper it is demonstrated that the application of principal components analysis for regional cluster modelling and analysis is essential in the situations where there is significant multicollinearity among several parameters, especially when the dimensionality of regional data is measured in tens. The proposed principal components model allows for same-quality representation of the clustering of regions. In fact, the clusters become more distinctive and the apparent outliers become either more pronounced with the component model clustering or are alleviated with the respective hierarchical cluster. Thus, a five-component model was obtained and validated upon 85 regions of Russian Federation and 19 socio-economic parameters. The principal components allowed to describe approximately 75 percent of the initial parameters variation and enable further simulations upon the studied variables. The cluster analysis upon the principal components modelling enabled better exposure of regional structure and disparity in economic development in Russian Federation, consisting of four main clusters: the few-numbered highest development regions, the clusters with mid-to-high and low economic development, and the "poorest" regions. It is observable that the development in most regions relies upon resource economy, and the industrial potential as well as inter-regional infrastructural potential are not realized to their fullest, while only the wealthiest regions show highly developed economy, while the industry in other regions shows signs of stagnation which is scaled further due to the conditions entailed by economic sanctions and the recent Covid-19 pandemic. Most Russian regions are in need of additional public support and industrial development, as their capital assets potential is hampered and, while having sufficient labor resources, their donorship will increase.
