论文标题
信息理论网络方法的社会经济相关性
Information theoretic network approach to socioeconomic correlations
论文作者
论文摘要
由于其对从确定收入不平等的热点到政治重新划分的所有事物都具有广泛的影响，因此在整个科学中都有丰富的文献来量化社会经济数据中的空间模式。特别是，与本地人口之间的社会和经济福祉相关的指标的变异性具有很大的兴趣，因为它与不平等和隔离的空间表现有关。但是，人口密度的异质性，统计分析对空间聚集的敏感性以及预绘制政治界限对政策干预的重要性可能会降低现有方法分析空间社会经济数据的疗效和相关性。此外，这些度量通常缺乏比较相同量表的定性和定量数据结果的框架，或者是对多区域相关性的概括机制。为了减轻与传统空间措施相关的这些问题，在这里我们查看社会经济变量与拓扑镜头而不是空间镜头的局部偏差，并使用基于广义的Jensen Shannon Divergence的新型信息理论网络方法来区分相邻区域的分布数量。我们在一系列实验中运用我们的方法，以研究美国大陆的邻近人口普查区域的网络，从而量化了整个网络之间的两点分布相关性的衰落，研究了县级的社会经济差异，这是由于小区域的聚集而引起的，并将algorithm构建为一个城市的Algorithm，将其构建为属于同源性的Clusers clusters。这些结果提供了一个新的框架，用于分析区域人群之间属性的变化，并阐明社会经济属性中的新普遍模式。
Due to its wide reaching implications for everything from identifying hotspots of income inequality to political redistricting, there is a rich body of literature across the sciences quantifying spatial patterns in socioeconomic data. In particular, the variability of indicators relevant to social and economic well-being between localized populations is of great interest, as it pertains to the spatial manifestations of inequality and segregation. However, heterogeneity in population density, sensitivity of statistical analyses to spatial aggregation, and the importance of pre-drawn political boundaries for policy intervention may decrease the efficacy and relevance of existing methods for analyzing spatial socioeconomic data. Additionally, these measures commonly lack either a framework for comparing results for qualitative and quantitative data on the same scale, or a mechanism for generalization to multi-region correlations. To mitigate these issues associated with traditional spatial measures, here we view local deviations in socioeconomic variables from a topological lens rather than a spatial one, and use a novel information theoretic network approach based on the Generalized Jensen Shannon Divergence to distinguish distributional quantities across adjacent regions. We apply our methodology in a series of experiments to study the network of neighboring census tracts in the continental US, quantifying the decay in two-point distributional correlations across the network, examining the county-level socioeconomic disparities induced from the aggregation of tracts, and constructing an algorithm for the division of a city into homogeneous clusters. These results provide a new framework for analyzing the variation of attributes across regional populations, and shed light on new, universal patterns in socioeconomic attributes.
