一种用于多维空间分析多维社会排斥的新方法
Matheus Pereira Libório1, Hamidreza Rabiei-Dastjerdi2,3, Sandro Laudares1
1Pontifical Catholic University of Minas Gerais, Belo Horizonte, 30535-012 Brazil.
GeoJournal
|June 26, 2023
概括
本研究介绍了强大的多空间PCA,这是分析社会现象的新方法. 它减少了数据丢失,并改善了社会排斥指标的多地域比较.
科学领域:
- 社会科学 社会科学 社会科学
- 地理信息科学 地理信息科学
- 统计分析 统计分析
背景情况:
- 社会现象是复杂的,并且在地理上相互依赖.
- 复合指标,通常使用主要组件分析 (PCA),代表这些现象,但遭受数据敏感性和局限性在多空间时间比较.
- 像PCA这样的现有方法可能导致信息丢失,并阻碍跨不同地理尺度进行比较分析.
研究的目的:
- 引入强大的多空间PCA,一种新的方法,旨在克服传统PCA在分析多维社会现象方面的局限性.
- 提高社会排斥综合指标的准确性和信息性.
- 为了促进社会现象在多个地理空间和时间点的更强大的比较.
主要方法:
- 基于概念相关性的加权子指标重要性.
- 非补偿性聚合以确保权重显著性.
- 维度聚合以平衡重量结构.
- 一个新的消除异常值的尺度转换函数用于多空间比较.
主要成果:
- 与传统方法相比,强大的多空间PCA方法显著减少了1.52倍的信息损失.
- 在八个城市的城市地区,社会排斥的综合指标的准确性有所提高.
- 新的尺度转换有效地处理异常值,使可靠的多空间比较成为可能.
结论:
- 强大的多空间PCA提供了多维社会现象的更具信息性和准确的表示.
- 该方法的易用性使其适合研究人员和政策制定者.
- 它支持在多个地理范围内制定有针对性的政策,增强基于证据的决策.
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