Framework to Study Migration Decisions Using Call Detail Record (CDR) Data
Framework to Study Migration Decisions Using Call Detail Record (CDR) Data
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DOI：
10.1109/tcss.2022.3177727
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发表时间：
2023-10
影响因子：
5
通讯作者：
Viren Dias;Lasantha Fernando;Yusen Lin;V. Frías-Martínez;L. Raschid
中科院分区：
文献类型：
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作者：
Viren Dias;Lasantha Fernando;Yusen Lin;V. Frías-Martínez;L. Raschid
本文介绍了使用呼叫详细记录(CDR)数据研究迁移所面临的挑战。为这项任务重新调整CDR数据的用途有许多好处，包括数据收集成本较低，并有可能进行同期分析。我们提出了一个重新定位和分析CDR数据的框架。我们确定订阅者的家庭位置，以及相应的置信度措施，并确定订阅者是确定的移民、可能的移民、可能的非移民还是明确的非移民。然后，预测模型使用从CDR数据提取的移动性和社交网络特征来预测要迁移的个人决策。我们是第一个解决在个人层面预测移民决策这一具有挑战性的任务的公司。我们还提供对可能对迁移决策产生影响的功能的洞察。使用斯里兰卡两个省的CDR数据进行的深入评估提供了移民流入和流出的细粒度地图。我们预测模型的成功和从评价中获得的见解为将CDR数据用于社会公益，重点放在移徙方面铺平了道路。
This article addresses the challenges of using call detail record (CDR) data to study migration. Repurposing CDR data for this task have many advantages, including the lower costs of data collection and the potential for contemporaneous analysis. We present a framework for the repurposing and analysis of CDR data. We identify the home location of a subscriber, with corresponding confidence measures, and determine if the subscriber is a definite migrant, likely migrant, likely nonmigrant, or definite nonmigrant. A predictive model then uses mobility and social network features, extracted from the CDR data, to predict the individual decision to migrate. We are the first to address the challenging task of predicting the migration decision at the individual level. We also provide insight into features that can have an impact on the decision to migrate. An in-depth evaluation using CDR data from two provinces in Sri Lanka provides a granular map of migrant inflow and outflow. The success of our prediction model and the insights gained from the evaluation prepare the way for the repurposing of CDR data for social good with a focus on migration.