CAREER: Measuring and Modeling the Multi-Modal Networks and Demographics of People Experiencing Homelessness

这项研究开发了新的方法来计算无家可归者，改善了现有的方法。 收集有关有庇护和无庇护无家可归人口的丰富数据很困难，这对研究人员和决策者来说是一个主要的复杂因素。该项目开发的方法基于新的数据收集策略，这是由于无家可归者通过智能手机免费Wi-Fi、图书馆免费电脑和其他程序上网的普及率很高。这项研究考察了在线和离线无家可归人口的人口统计数据（年龄，性别，种族/民族）及其社会关系，以了解他们对无家可归的时间和寿命的影响。它通过改进对难以接触到的无家可归人口的估计和增加对影响无家可归持续时间的社会支助机制的了解，为研究作出了贡献。调查结果将告知决策者和医疗保健领导者关于统计无家可归者的最佳方法，以分配资源，传播信息的最佳实践以及围绕社会支持网络的新策略。这项研究集中在美国地区，该地区包含很大一部分无家可归者，在几个城市有针对性的样本。 该研究整合了调查抽样和估计的最新发展，从在线来源获得人口统计和社交网络数据，以与离线样本进行比较，包括住房和城市发展时间点人口计数。通过四种方法采用新的和旧的战略来衡量难以接触的人口：两种在线抽样方法（广义网络扩大方法和网络抽样）和两种亲自抽样方法（受访者驱动抽样和空间抽样）。将这些与当前联邦标准时间点计数进行比较。该项目通过扩展空间网络模型（已被证明可以深入了解无家可归者的网络）来利用所产生的数据，以充分了解地理和人口统计对无家可归者的社会网络结构的影响及其对无家可归的时间和持续时间的影响。这项研究的结果提供了无家可归的空间维度和新的统计网络方法，为无家可归的社区提供了信息（在线和离线）和疾病（离线）传播的可更新的模拟模型所需的信息。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This research develops novel methods for counting the homeless that improve upon existing ways of doing so. Collecting rich data on sheltered and unsheltered homeless populations is difficult and represents a major complication for researchers and decision makers. The methods developed in this project are based on newly available data collection strategies made possible by the high prevalence of online access among homeless persons via free Wi-Fi through smartphones, free computers in libraries, and other programs. This study examines the demographics (age, gender, race/ethnicity) of the online and offline homeless populations and their social relationships to understand their impact on the timing and longevity of homelessness. It contributes to research by improving the estimation of hard-to-reach homeless populations and by increasing understanding of the social support mechanisms that affect the duration of homelessness. Findings will inform decision makers and healthcare leaders about best methods for counting people experiencing homelessness for resource allocation, best practices for disseminating information, and new strategies centered around social support networks.This research concentrates on a US region that contains a large portion of people experiencing homelessness, with targeted samples in a few cities. The study integrates recent developments in survey sampling and estimation to obtain the demographics and social network data from online sources to compare with offline samples, including the Housing and Urban Development Point-in-Time population count. Novel uses of new and old strategies for measuring hard-to-reach populations are employed through four methods: two methods for online sampling (generalized network scale-up methods and network sampling) and two methods for in-person sampling (respondent driven sampling and space-based sampling). These are compared against the current federal standard Point in Time count. The project leverages the resulting data by extending spatial network models -- which have been shown to provide insight into the networks of people experiencing homelessness -- to fully understand the effects of geography and demographics on the social network structure of people experiencing homelessness and its resultant impact on the timing and duration of homelessness. Results of this study provide needed information on the spatial dimension of homelessness and new statistical network methods for providing update-able simulation models for diffusion of information (online and offline) and disease (offline) through homeless communities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.