概括
这项研究整合了卫星,Facebook和OpenStreetMap数据,以估计59个国家的财富水平和变化. 这种新的方法为传统的家庭调查提供了一种成本效益高的替代方案,用于评估贫困.
科学领域:
- 地理空间分析是什么?
- 发展经济学发展经济学
- 数据科学是数据科学.
背景情况:
- 家庭调查提供准确的贫困数据,但成本昂贵,频率不高.
- 估计财富及其在全球的变化需要创新的,可扩展的方法.
研究的目的:
- 开发和验证使用多种数据源来估计财富水平和变化的模型.
- 评估模型在广泛的国家中的通用性.
主要方法:
- 在59个国家的63,854个调查集群位置上训练了机器学习模型.
- 来自卫星,Facebook营销和OpenStreetMap的综合数据.
- 评估模型在解释集群和地区层面财富变化的表现.
主要成果:
- 该模型解释了集群层面的55%的财富水平变化,平均在地区层面的59%.
- 财富变化解释的变化在集群层面为4%,在地区层面为6%.
- 夜间灯光,OpenStreetMap和土地覆盖数据是财富水平的关键预测指标.
结论:
- 结合多个公共和私人数据源,有效估计财富水平和财富变化.
- 该模型显示出强的表现,特别是在低收入国家和高财富差异的国家.
- 这种方法为传统的贫困测量调查提供了可扩展和成本效益的补充.
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