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Representing Multidimensional Phenomena of Geographic Interest: Benefit of the Doubt or Principal Component Analysis?
The Professional Geographer ( IF 1.5 ) Pub Date : 2022-05-02 , DOI: 10.1080/00330124.2022.2048868
Matheus Pereira Libório 1 , Oseias da Silva Martinuci 2 , Alexei Manso Correa Machado 3 , Petr Iakovlevitch Ekel 3 , João Francisco de Abreu 1 , Sandro Laudares 1
Affiliation  

Composite indicators are one-dimensional measures of multidimensional phenomena. Through the composite indicators, it is possible to have a single map of the different subindicators of poverty, inequality, sustainability, and economic development. This research employs two well-known methods of building composite indicators to represent the social exclusion of eight cities. This research shows that the benefit of the doubt and principal component analysis have limitations to representing multidimensional phenomena of geographic interest, but adaptations in these methods reduce these limitations. The benefit of the doubt constrained (BoD-c) restricts subindicator weight variations, increasing the composite indicator’s capacity to represent the most important subindicator in the concept of the multidimensional phenomenon. The principal component analysis adjusted (PCA-a) discards poorly correlated subindicators, ensuring a variance extracted in the first component above the acceptance threshold of 0.50. Contrasting BoD-c and PCA-a, geographically weighted principal component analysis has a limited capacity to capture the most important subindicator in the concept of the multidimensional phenomenon. Among twenty-three experts from nine countries, eighteen preferred PCA-a to BoD-c, indicating that information loss is not as critical a property as full comparability across geographic areas. Local experts agree that both maps represent local social reality, but PCA-a is more faithful to that reality.



中文翻译:

代表地理利益的多维现象:怀疑或主成分分析的好处?

综合指标是多维现象的一维度量。通过综合指标,有可能获得贫困、不平等、可持续性和经济发展的不同子指标的单一地图。本研究采用了两种著名的构建综合指标的方法来表示八个城市的社会排斥情况。这项研究表明,怀疑和主成分分析的好处在表示地理感兴趣的多维现象方面存在局限性,但对这些方法的调整减少了这些局限性。怀疑约束(BoD-c)的好处限制了子指标权重的变化,增加了综合指标代表多维现象概念中最重要的子指标的能力。调整后的主成分分析 (PCA-a) 丢弃了相关性较差的子指标,确保在第一个成分中提取的方差高于接受阈值 0.50。对比 BoD-c 和 PCA-a,地理加权主成分分析在捕捉多维现象概念中最重要的子指标方面的能力有限。在来自 9 个国家的 23 位专家中,有 18 位专家更喜欢 PCA-a 而不是 BoD-c,这表明信息丢失并不像跨地理区域的完全可比性那样重要。当地专家一致认为,这两张地图都代表了当地的社会现实,但 PCA-a 更忠实于这一现实。对比 BoD-c 和 PCA-a,地理加权主成分分析在捕捉多维现象概念中最重要的子指标方面的能力有限。在来自 9 个国家的 23 位专家中,有 18 位专家更喜欢 PCA-a 而不是 BoD-c,这表明信息丢失并不像跨地理区域的完全可比性那样重要。当地专家一致认为,这两张地图都代表了当地的社会现实,但 PCA-a 更忠实于这一现实。对比 BoD-c 和 PCA-a,地理加权主成分分析在捕捉多维现象概念中最重要的子指标方面的能力有限。在来自 9 个国家的 23 位专家中,有 18 位专家更喜欢 PCA-a 而不是 BoD-c,这表明信息丢失并不像跨地理区域的完全可比性那样重要。当地专家一致认为,这两张地图都代表了当地的社会现实,但 PCA-a 更忠实于这一现实。表明信息丢失不像跨地理区域的完全可比性那样重要。当地专家一致认为,这两张地图都代表了当地的社会现实,但 PCA-a 更忠实于这一现实。表明信息丢失不像跨地理区域的完全可比性那样重要。当地专家一致认为,这两张地图都代表了当地的社会现实,但 PCA-a 更忠实于这一现实。

更新日期:2022-05-02
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