迁移网络：网络分析在宏观迁移模式中的应用
Migration Networks: Applications of Network Analysis to Macroscale Migration Patterns
摘要：
一个新兴的研究领域是研究作为代表地点的节点网络的宏观移徙模式（例如，国家、城市和农村地区）以及编码连接这些地方的移民纽带的边缘。在本章中，我们首先回顾了移民网络研究的进展，以及最近采用网络分析在不同地理尺度上研究此类网络的工作。在我们的讨论中，我们特别关注全球规模的移徙网络。然后，我们提出了利用网络分析与数字技术和在线地理定位数据的方法，以研究移民网络的结构和动态。实施这种研究移徙网络的方法面临许多挑战，包括伦理挑战、方法挑战、社会技术挑战（例如，数据可用性和再利用）和研究再现性。我们详细介绍了这些挑战，然后我们考虑将数字地理定位数据与行政和调查数据联系起来的可能方法，作为利用新技术构建日益现实的移民网络的一种方式（例如，使用多路复用网络）。我们还简要讨论了新方法（例如，多层网络分析）和相邻领域（例如，机器学习），可以帮助推进对宏观迁移模式的理解。
英文摘要：
An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places. In this chapter, we first review advances in the study of migration networks and recent work that has employed network analysis to examine such networks at different geographical scales. In our discussion, we focus in particular on global scale migration networks. We then propose ways to leverage network analysis in concert with digital technologies and online geolocated data to examine the structure and dynamics of migration networks. The implementation of such approaches for studying migration networks faces many challenges, including ethical ones, methodological ones, socio-technological ones (e.g., data availability and reuse), and research reproducibility. We detail these challenges, and we then consider possible ways of linking digital geolocated data to administrative and survey data as a way of harnessing new technologies to construct increasingly realistic migration networks (e.g., using multiplex networks). We also briefly discuss new methods (e.g., multilayer network analysis) in network analysis and adjacent fields (e.g., machine learning) that can help advance understanding of macroscale patterns of migration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
