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A Study of Multiregional Economic Correlation Analysis Based on Big Data—Taking the Regional Economy of Cities in Shaanxi Province, China, As an Example

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A Study of Multiregional Economic Correlation Analysis Based on Big Data—Taking the Regional Economy of Cities in Shaanxi Province, China, As an Example sustainability Article A Study of Multiregional Economic Correlation Analysis Based on Big Data—Taking the Regional Economy of Cities in Shaanxi Province, China, as an Example Shouheng Tuo 1,* and Hong He 2 1 School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China 2 College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an 710121, China; [email protected] * Correspondence: [email protected] Abstract: To enhance the sustainability of the regional economy, this study attempts to integrate historical big data of multiregional and multi-industry economic indicators, aiming to explore and discover the correlations among regions, industries, or cross-regional economic indicators. In this paper, two correlation analysis models (the 2-order correlation model and the elastic-net regularized generalized linear model) are used to conduct a correlation analysis study of multiregional and multi-industry economies, and 20 years of historical data from 9 prefecture-level cities in Shaanxi (778 indicators in total) are analyzed empirically. The results show that the proposed method can mine complex correlations from economic big data. Citation: Tuo, S.; He, H. A Study of Multiregional Economic Correlation Keywords: big data; multiregions; regional economy; association analysis Analysis Based on Big Data—Taking the Regional Economy of Cities in Shaanxi Province, China, as an Example. Sustainability 2021, 13, 5121. 1. Introduction https://doi.org/10.3390/su13095121 In recent years, political issues, such as trade protectionism, racism, and populism, have brought great uncertainty to the political and economic development of countries Academic Editors: Federico Amato, worldwide [1]. In particular, the trade dispute between the United States and China Sabrina Lai, Alessandro Marucci, has brought major challenges to the development of the world economy. In the face of Beniamino Murgante and today’s complex international environment, China, as the largest developing country, must Lorena Fiorini urgently adjust its thinking on economic development, expand domestic demand, adjust its industrial structure, and establish a new strategy for regional economic development with Received: 22 March 2021 adaptive capacity. In the report of China’s 19th National Congress, President Xi Jinping Accepted: 29 April 2021 Published: 3 May 2021 pointed out that China has entered a new era, and he clearly proposed building China into a strong, democratic, civilized, harmonious, and beautiful socialist modern power by Publisher’s Note: MDPI stays neutral the middle of this century [2]. However, in the context of current complex and changing with regard to jurisdictional claims in macroeconomic development, how to effectively identify the main industrial drivers of published maps and institutional affil- regional economic development, how to accurately measure the internal correlation of the iations. indicators affecting regional industrial development, and how to truly grasp the interaction between different industrial systems and their impacts on regional economic development are all important questions for the selection of pillar industries, the enhancement of urban competitiveness, and the development of regional economic synergy. Therefore, correlation analysis of the economic development impact factors of multiregional industries has Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. become an important research topic. This article is an open access article In recent years, results from regional economy and industrial economy research in distributed under the terms and China have been abundant and broad in scope, and the research methods have become conditions of the Creative Commons more advanced. Scholars have conducted multilevel and multiperspective research based Attribution (CC BY) license (https:// on different research content and using various research methods, resulting in many creativecommons.org/licenses/by/ research results. In terms of research content, most of the literature has focused on spe- 4.0/). cific industries, such as agriculture [3], manufacturing [4,5], real estate [6], logistics [7], Sustainability 2021, 13, 5121. https://doi.org/10.3390/su13095121 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 5121 2 of 13 finance [8], productive services [9], cultural [10], information [11], and other related in- dustries, and it has focused on regional industrial linkages and their ripple effects [12]. Policy recommendations to promote the rationalization of industrial structure have been proposed [13] as a powerful tool for determining the economic contributions of industries and the leading regional industries. Studies considering multiple industries and exploring regional economic linkages and their effects on the regional economy from multidimen- sional perspectives, such as outward orientation [14,15], have gradually gained attention. Research on the spatiotemporal correlation effects of regional economic development has become popular, studies have shown spatial correlations, hierarchical correlations, and administrative correlations in regional economic growth, and there are objective effects on the network of regional growth relationships [16]; there are network structures and spatiotemporal coupling characteristics in the spatial correlations of cross-country [17], interprovincial [18] and municipal economic growth in China [19,20], and there are strong urban economic spatial autocorrelations [21] and obvious spatial spillover effects [22–24]. Exploring the problem of the spatiotemporal effects of economic growth from a compre- hensive multi-industry perspective is a promising direction for subsequent research. In terms of research methods, the Leontief inverse coefficient is the main tool for study- ing associations, and the input–output method pioneered by Leontief is the most widely used method for industry association calculations. Scholars have used the inductance and influence coefficients [25] to measure the forward and backward linkages between a single industry and the entire national economy, including the industry itself, by constructing an economic distance model of industry linkages [26]. The classical regression analysis meth- ods, principal component analysis methods, Markov chain methods, spatial econometric methods, and econometric models in economics have been fully used. In recent years, the research methods of regional economic growth spatial club convergence analysis [27–29] have received wide attention. In terms of research regions, the results involve both large-scale studies, such as international, national, and regional studies, and small- and medium-scale studies, such as provincial, sectoral, and enterprise studies, showing geospatial dispersion. Although industrial relevance studies reflect variability across different regions and scales, indus- trial relevance studies have become criteria for comprehensive consideration of regional economic development capabilities. However, regional economic development has multiregional and multi-industry char- acteristics, and economic development indicators have the characteristics of high dimen- sionality and a large amount of data. The existing research methods are particularly inadequate for mining the correlations between multiregional and multi-indicator systems. 2. Analysis of the Linkage of Multiregional Economy Since the reform and opening up, all industries in China have become well developed and the conditions for the synergistic development of multiple industries exist. In the cur- rent era of prevalent neoliberalism [1], the comprehensive and coordinated development of various industrial economies and the promotion of comprehensive economic development of each region of China have become the top priorities. Determining how to coordinate the development of a multiregional multi-industry economy is very challenging: the economic indicators of multiregional multi-industries have the characteristics of high dimension- ality and a large amount of data. As shown in Figure1, among the three regions, there are correlations of economic indicators, which can influence each other. For example, in region 1, good agriculture and ecological environment can promote the development of tourism in the region, but at the same time, it will be influenced by the population size and culture level of region 2. The education level of the population in regions 1 and 2 is correlated with the development of the information industry and financial sector in region 3. Sustainability 2021, 13, x FOR PEER REVIEW 3 of 13 Sustainability 2021, 13, 5121 3 of 13 is correlated with the development of the information industry and financial sector in re- gion 3. Figure 1. NetworkFigure 1. Network of associations of associations of impact of impact factors factors (indicators) (indicators) affecting affecting economic economic development development in regions in 1regions and 2. The 1 and 2. The node denotesnode the denotes impact the impactfactor factor affecting affecting the the economic economic development of regions.of regions. (1) Within (1) Within the same the industry same (largeindustry circle), (large if circle), if two nodestwo have nodes connected have connected edges, edges, it itindicates indicates thatthat there there is a is connection a connection between between these two these factors two (nodes) factors and that(nodes) they mayand that they may influenceinfluence
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