Mapping and modelling the main determinants of Migration Flows at the NUTS2 Level in European Union using Spatial Data Analysis Techniques

Elena-Maria Prada, Smaranda Cimpoeru
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Abstract

Abstract Spatial data analysis approaches have grown in popularity in recent years as there has been an increased interest in studying migration at the regional level. We employed in our analysis the Nomenclature of Territorial Units for Statistics (NUTS) level, which offers a more thorough view of migratory patterns inside a nation as well as among EU countries.Using spatial data analytic tools, this research aims to investigate the key determinants of international migration at the NUTS2 level. Using data from the Eurostat database, we undertake an extensive analysis of migration movements in a sample of European Union countries. We apply spatial econometric models to examine the relationship between crude rate migration and other relevant variables as independent variables. Although the available data at the NUTS2 level of regional ranking are limited, we believe that we included the most comprehensive set of relevant variables in our analysis.Our study contributes to the existing literature on migration determinants by highlighting the importance of spatial analysis techniques in understanding migration factors at the NUTS2 level.
利用空间数据分析技术绘制欧盟 NUTS2 级移民流主要决定因素图并建立模型
摘要 近年来,随着人们对区域层面移民研究的兴趣日益浓厚,空间数据分析方法也越来越受欢迎。我们在分析中采用了《统计用领土单位命名法》(NUTS)层面,它能更全面地反映一个国家内部以及欧盟国家之间的移民模式。本研究利用空间数据分析工具,旨在研究 NUTS2 层面国际移民的关键决定因素。我们利用欧盟统计局数据库中的数据,对欧盟国家样本中的移民流动情况进行了广泛分析。我们采用空间计量经济模型来研究粗移民率与作为自变量的其他相关变量之间的关系。虽然 NUTS2 级别的地区排名可用数据有限,但我们相信我们的分析包含了最全面的相关变量。我们的研究强调了空间分析技术在理解 NUTS2 级别移民因素方面的重要性,从而为现有的移民决定因素文献做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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