政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151573

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政大機構典藏 > 社會科學學院 > 社會學系 > 會議論文 > Item 140.119/151573

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/151573

Title:	Computational Archives of Population Dynamics and Migration Networks as a Gateway to Get Deep Insights into Hard-To-Reach Populations: Research on Taiwan Indigenous Peoples
Authors:	林季平 Lin, Ji-Ping
Contributors:	社會系
Keywords:	hard-to-reach population;migration network;population dynamics;ethnic lineage;TICD;TIPD
Date:	2021-12
Issue Date:	2024-06-03 14:54:11 (UTC+8)
Abstract:	This paper highlights research on constructing big computational archives of hard-to-reach populations (HRPs), using Taiwan Indigenous Peoples (TIPs) as an example. The research uses archives of (1) anonymous individual-level migration flows computed from population dynamics data and (2) Taiwan indigenous community data (TICD) to illustrate characteristics of HRPs which were unknown before. The research suggests that computational HRP networks (e.g., migration networks) help overcome barriers to accessing HRPs and promote mutual understanding. The archives of Taiwan Indigenous Peoples Open Research Data (TIPD) are a research data source, with archives of address geocoding, population dynamics, and indigenous communities being most relevant to TIPs network systems. The migration flows are computed at the individual level and have unveiled various dimensions of HRP networks that were invisible before. The newly computed TICD archives enable us to trace migration flows of TIPs within and between indigenous communities and urban localities at the individual level in the context of ethnic lineages. The research findings suggest that strengthening intra- and inter-ethnic network connections serves as an effective measure to get deep insights into HRPs.
Relation:	2021 IEEE International Conference on Big Data, IEEE
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/BigData52589.2021.9671838
DOI:	10.1109/BigData52589.2021.9671838
Appears in Collections:	[社會學系] 會議論文

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