机器学习和电话数据可以改善人道主义援助的定位
Emily Aiken1, Suzanne Bellue2, Dean Karlan3
1School of Information, University of California, Berkeley, CA, USA.
Nature
|March 17, 2022
概括
移动电话数据可以改善低收入国家的援助目标. 在调查数据上训练的机器学习算法识别贫困模式,减少在危机期间提供人道主义援助的排除错误.
科学领域:
- 数据科学
- 发展经济学
- 人道主义物流
背景情况:
- COVID-19 疫情加剧了低收入和中等收入国家的粮食不安全和贫困.
- 全球已有超过15亿人的社会援助项目.
- 帮助那些最需要帮助的人是一个持续的挑战.
研究的目的:
- 研究移动电话网络数据在加强人道主义援助的有效性.
- 使用移动数据开发和评估用于贫困识别的机器学习算法.
主要方法:
- 使用传统的调查数据来训练机器学习算法.
- 在手机数据中应用训练有素的算法,
- 评估了使用多哥COVID-19紧急现金转账计划的方法.
主要成果:
- 与地理定位相比,机器学习方法减少了4-21%的排除错误.
- 与假设的全面社会登记簿相比,排除错误增加了9-35%.
结论:
- 移动电话数据为人道主义援助的传统方法提供了有价值的补充.
- 这种方法在数据不完整或过时的危机情况下尤为重要.
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