  In this article, we propose using network-based sampling strategies to
estimate the number of unsheltered people experiencing homelessness within a
given administrative service unit, known as a Continuum of Care. We demonstrate
the effectiveness of network sampling methods to solve this problem. Here, we
focus on Respondent Driven Sampling (RDS), which has been shown to provide
unbiased or low-biased estimates of totals and proportions for hard-to-reach
populations in contexts where a sampling frame (e.g., housing addresses) is not
available. To make the RDS estimator work for estimating the total number of
people living unsheltered, we introduce a new method that leverages
administrative data from the HUD-mandated Homeless Management Information
System (HMIS). The HMIS provides high-quality counts and demographics for
people experiencing homelessness who sleep in emergency shelters. We then
demonstrate this method using network data collected in Nashville, TN, combined
with simulation methods to illustrate the efficacy of this approach and
introduce a method for performing a power analysis to find the optimal sample
size in this setting. We conclude with the RDS unsheltered PIT count conducted
by King County Regional Homelessness Authority in 2022 (data publicly available
on the HUD website) and perform a comparative analysis between the 2022 RDS
estimate of unsheltered people experiencing homelessness and an ARIMA forecast
of the visual unsheltered PIT count. Finally, we discuss how this method works
for estimating the unsheltered population of people experiencing homelessness
and future areas of research.

Social and Information Networks;Methodology