Identifying groups of children's social mobility opportunity for public health applications using k-means clustering.

ABSTRACT:

Background

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.

Methods

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 pa

SUBMITTER: Zelasky S 

PROVIDER: S-EPMC10560027 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature