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. Further, we specifically advocate for the network sampling method known as 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) not available. To make the RDS estimator work for estimating the total number of unsheltered people, 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. Finally, we end with discussing how this could be used in practice.

翻译：本文提出采用基于网络的抽样策略，以估算特定行政服务单元（即持续护理体系）内无家可归的露宿者人数。我们特别倡导使用名为"受访者驱动抽样"（RDS）的网络抽样方法。该方法已被证明在缺乏抽样框架（如居住地址）的情况下，能为难以触及的人群提供无偏或低偏的总量及比例估计。为使RDS估计量适用于估算露宿者总数，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"无家可归者管理信息系统"（HMIS）中的行政数据。HMIS提供了在紧急避难所过夜的无家可归者的高质量人口统计与人口特征数据。随后，我们通过收集于田纳西州纳什维尔的网络数据，结合模拟方法，验证了该方法的有效性。最后，本文探讨了该方法在实际中的应用前景。