论文标题
拓扑数据分析和联合国儿童基金会多重指标群集调查
Topological data analysis and UNICEF Multiple Indicator Cluster Surveys
论文作者
论文摘要
由联合国儿童基金会支持的多个指标群集调查（MIC）是提供有关妇女和儿童健康和教育数据的最重要的全球家庭调查计划之一。我们使用拓扑数据分析分析了塞尔维亚2014-15 MICS数据集，该数据将数据云视为拓扑空间，并提取有关其内在几何特性的信息。特别是，我们的分析使用映射器算法，该算法是减少尺寸和聚类方法，该方法可从数据云中产生图形。由此产生的映射图提供了对家庭财富之间各种关系的洞察力 - 如财富指数所表达的那样，从麦克索数据中提取的重要指标以及其他参数，例如城市/农村环境，项目所有权和所有财产的优先级。除其他用途外，这些发现可以通过提供基本便利设施的层次结构来为政策提供信息。它们还可以被用来完善财富指数或加深我们对捕获的内容的理解。
Multiple Indicator Cluster Surveys (MICS), supported by UNICEF, are one of the most important global household survey programs that provide data on health and education of women and children. We analyze the Serbia 2014-15 MICS dataset using topological data analysis which treats the data cloud as a topological space and extracts information about its intrinsic geometric properties. In particular, our analysis uses the Mapper algorithm, a dimension-reduction and clustering method which produces a graph from the data cloud. The resulting Mapper graph provides insight into various relationships between household wealth - as expressed by the wealth index, an important indicator extracted from the MICS data - and other parameters such as urban/rural setting, ownership of items, and prioritization of possessions. Among other uses, these findings can serve to inform policy by providing a hierarchy of essential amenities. They can also potentially be used to refine the wealth index or deepen our understanding of what it captures.
