Zelasky Sarah, Martin Chantel L, Weaver Christopher, Baxter Lisa K, Rappazzo Kristen M
Oak Ridge Associated Universities at the U.S. Environmental Protection Agency, Chapel Hill, NC, USA.
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, USA.
Heliyon. 2023 Sep 18;9(9):e20250. doi: 10.1016/j.heliyon.2023.e20250. eCollection 2023 Sep.
The Opportunity Atlas project is a pioneering effort to trace social mobility and adulthood socioeconomic outcomes back to childhood residence. Half of the variation in adulthood socioeconomic outcomes was explainable by neighborhood-level socioeconomic characteristics during childhood. Clustering census tracts by Opportunity Atlas characteristics would allow for further exploration of variance in social mobility. Our objectives here are to identify and describe spatial clustering trends within Opportunity Atlas outcomes.
We utilized a k-means clustering machine learning approach with four outcome variables (individual income, incarceration rate, employment, and percent of residents living in a neighborhood with low levels of poverty) each given at five parental income levels (1st, 25th, 50th, 75th, and 100th percentiles of the national distribution) to create clusters of census tracts across the contiguous United States (US) and within each Environmental Protection Agency region.
At the national level, the algorithm identified seven distinct clusters; the highest opportunity clusters occurred in the Northern Midwest and Northeast, and the lowest opportunity clusters occurred in rural areas of the Southwest and Southeast. For regional analyses, we identified between five to nine clusters within each region. PCA loadings fluctuate across parental income levels; income and low poverty neighborhood residence explain a substantial amount of variance across all variables, but there are differences in contributions across parental income levels for many components.
Using data from the Opportunity Atlas, we have taken four social mobility opportunity outcome variables each stratified at five parental income levels and created nationwide and EPA region-specific clusters that group together census tracts with similar opportunity profiles. The development of clusters that can serve as a combined index of social mobility opportunity is an important contribution of this work, and this in turn can be employed in future investigations of factors associated with children's social mobility.
“机会地图集”项目是一项开创性的工作，旨在将社会流动性和成年后的社会经济成果追溯到童年时期的居住地。成年后社会经济成果的一半差异可以由童年时期邻里层面的社会经济特征来解释。根据“机会地图集”特征对人口普查区进行聚类，将有助于进一步探索社会流动性的差异。我们在此的目标是识别和描述“机会地图集”成果中的空间聚类趋势。
我们采用k均值聚类机器学习方法，使用四个结果变量（个人收入、监禁率、就业率以及居住在贫困水平较低社区的居民百分比），每个变量在五个父母收入水平（全国分布的第1百分位、第25百分位、第50百分位、第75百分位和第100百分位）上进行分析，以在美国本土及每个环境保护局区域内创建人口普查区聚类。
在国家层面，该算法识别出七个不同的聚类；机会最高的聚类出现在中西部北部和东北部，机会最低的聚类出现在西南部和东南部的农村地区。对于区域分析，我们在每个区域内识别出五到九个聚类。主成分分析（PCA）载荷在不同父母收入水平之间波动；收入和居住在低贫困社区解释了所有变量中的大量差异，但许多成分在不同父母收入水平上的贡献存在差异。
利用“机会地图集”的数据，我们选取了四个社会流动性机会结果变量，每个变量在五个父母收入水平上进行分层，并创建了全国性和特定于环境保护局区域的聚类，将具有相似机会概况的人口普查区归为一组。开发能够作为社会流动性机会综合指标的聚类是这项工作的一项重要贡献，这反过来可用于未来对与儿童社会流动性相关因素的调查。
