Small area estimation of the homeless in Los Angeles: An application of cost-sensitive stochastic gradient boosting

TL;DR

This paper applies cost-sensitive stochastic gradient boosting to estimate homeless populations in large metropolitan areas, addressing the challenge of imputation in unvisited regions with asymmetric error costs.

Contribution

It introduces an augmentation of $L_1$ stochastic gradient boosting that incorporates asymmetric costs for over- and underestimation, tailored for spatial sampling in homelessness estimation.

Findings

Effective imputation of homeless counts in unvisited areas.

Asymmetric loss functions influence estimation accuracy.

Practical policy implications for resource allocation.

Abstract

In many metropolitan areas efforts are made to count the homeless to ensure proper provision of social services. Some areas are very large, which makes spatial sampling a viable alternative to an enumeration of the entire terrain. Counts are observed in sampled regions but must be imputed in unvisited areas. Along with the imputation process, the costs of underestimating and overestimating may be different. For example, if precise estimation in areas with large homeless c ounts is critical, then underestimation should be penalized more than overestimation in the loss function. We analyze data from the 2004--2005 Los Angeles County homeless study using an augmentation of $L_1$ stochastic gradient boosting that can weight overestimates and underestimates asymmetrically. We discuss our choice to utilize stochastic gradient boosting over other function estimation procedures. In-sample fitted and out-of-sample imputed values, as well as relationships between the response and predictors, are analyzed for various cost functions. Practical usage and policy implications of these results are discussed briefly.