Chain Migration Mapping
Chain migration mapping reconstructs the social mechanism by which one migrant's move triggers many others, tracing the kin, friend, and paesani ties along which earlier arrivals recruit and sponsor later ones into the same destination. John and Leatrice MacDonald's 1964 study of Italian migration to the United States gave the process its classic name, showing how chains of personal sponsorship channel newcomers into specific neighborhoods and produce the dense ethnic enclaves that dot immigrant cities. The method treats migration not as independent decisions by isolated individuals but as a self-feeding network in which each settler lowers the cost and risk of moving for those still at home. Douglas Massey's 1990 theory of cumulative causation formalized why such chains accelerate over time, as every new migrant expands the web of contacts that makes the next move easier. Mapping a chain therefore means building the directed sponsorship graph, ordering it by arrival time, and clustering it at the destination to reveal how neighborhoods crystallize. The result is both a descriptive map of who brought whom and an explanatory account of why migration streams persist and concentrate.
手法の全文を読む
無料アカウントでログインすると、このセクションを読めます。
手法マップ
関連する手法の近傍 — ノードを選択して探索できます。
出典
- MacDonald, J. S., & MacDonald, L. D. (1964). Chain Migration, Ethnic Neighborhood Formation and Social Networks. The Milbank Memorial Fund Quarterly, 42(1), 82-97. DOI: 10.2307/3348581 ↗
- Massey, D. S. (1990). Social Structure, Household Strategies, and the Cumulative Causation of Migration. Population Index, 56(1), 3-26. DOI: 10.2307/3644186 ↗
このページの引用方法
ScholarGate. (2026, June 23). Chain Migration and Ethnic Neighborhood Formation Mapping. ScholarGate. https://scholargate.app/ja/migration-studies/chain-migration-mapping
どの手法を選ぶ?
この手法を最も近い類縁の手法と並べ、両者を見比べてください — ライブラリは本を机の上に並べるだけ。選ぶのはあなたです。
- Diaspora Engagement MappingMigration Studies↔ 比較
- Migrant Network AnalysisMigration Studies↔ 比較
- Onward Migration AnalysisMigration Studies↔ 比較