Identifying poverty-driven need by augmenting census and community survey data

Abstract

Need is a function of both individual household’s ability to meet basic requirements such as food, shelter, clothing, medical care, and transportation, and latent exogenous factors such as the cost of living and available community support for such requirements. Identifying this need driven poverty helps in understanding the socioeconomic status of individuals and to identify the areas of development. This work aims at using georeferenced data from the American Community Survey (ACS) to estimate baseline need based on aggregated socioeconomic variables indicating absolute and relative poverty. In this project, I implement and compare the results of several machine learning classification algorithms such as Random Forest, Support Vector Machine, and Logistic Regression to identify poverty for different block groups in the United States