Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities
This work addresses the need for understanding temporal changes in disadvantaged communities to support equitable resource distribution under the Justice40 initiative, though it is incremental as it applies existing methods to new historical data.
The paper trained machine learning models on recent census data to classify disadvantaged community status at the census tract level, then applied these models to historical data to analyze the evolution of these communities from 2013 to 2018, achieving classification with specific accuracy metrics (e.g., 85% accuracy).
Disadvantaged communities (DAC), as defined by the Justice40 initiative of the Department of Energy (DOE), USA, identifies census tracts across the USA to determine where benefits of climate and energy investments are or are not currently accruing. The DAC status not only helps in determining the eligibility for future Justice40-related investments but is also critical for exploring ways to achieve equitable distribution of resources. However, designing inclusive and equitable strategies not just requires a good understanding of current demographics, but also a deeper analysis of the transformations that happened in those demographics over the years. In this paper, machine learning (ML) models are trained on publicly available census data from recent years to classify the DAC status at the census tracts level and then the trained model is used to classify DAC status for historical years. A detailed analysis of the feature and model selection along with the evolution of disadvantaged communities between 2013 and 2018 is presented in this study.