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.

翻译：在本文中，我们提出使用基于网络的抽样策略来估算特定行政服务单元（即“持续关怀区”）内无庇护所 homelessness 人群的数量。我们展示了网络抽样方法在解决此问题中的有效性。本文重点研究受访者驱动抽样（RDS），该方法已被证明在缺乏抽样框（例如住房地址）的情况下，能够为目标难以触及人群提供无偏或低偏的总数与比例估计。为了使RDS估计适用于估算无庇护所居住者总数，我们引入了一种新方法，该方法利用了住房与城市发展部（HUD）强制要求的管理信息系统（HMIS）中的行政数据。HMIS提供了在紧急庇护所过夜的 homelessness 人群的高质量计数与人口统计数据。随后，我们使用在田纳西州纳什维尔收集的网络数据结合仿真方法，展示了该方法的有效性，并引入了一种开展功效分析的方法以确定此情境下的最优样本量。最后，我们基于金县区域 homelessness 管理局于2022年开展的RDS无庇护所PIT计数（数据公开于HUD网站），对2022年RDS估算的无庇护所 homelessness 人群数量与ARIMA预测的视觉PIT计数进行了对比分析。文章结尾讨论了该方法在估算无庇护所 homelessness 人群中的应用及未来研究方向。