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

这项研究开发了计算无家可归者的新方法，改进了现有的方法。收集有收容所和无收容所的无家可归人口的丰富数据是困难的，对研究人员和决策者来说是一个主要的复杂问题。该项目中开发的方法基于最新可用的数据收集策略，这是由于无家可归者通过智能手机、图书馆免费电脑和其他程序通过免费Wi-Fi进行在线访问的高流行率。这项研究考察了线上和线下无家可归者的人口统计数据(年龄、性别、种族/民族)以及他们的社会关系，以了解他们对无家可归的时间和时间的影响。它通过改进对难以到达的无家可归者人口的估计，并通过增加对影响无家可归持续时间的社会支助机制的了解，为研究作出贡献。研究结果将为决策者和医疗保健领导人提供有关统计无家可归者进行资源分配的最佳方法、传播信息的最佳实践以及以社会支持网络为中心的新战略。这项研究集中在美国一个拥有很大一部分无家可归者的地区，并在几个城市进行了有针对性的样本调查。这项研究结合了调查抽样和估计方面的最新发展，从在线来源获得人口统计和社会网络数据，并与线下样本进行比较，包括住房和城市发展时间点人口统计。通过四种方法使用新旧战略来衡量难以触及的人口：两种在线抽样方法(广义网络扩大法和网络抽样)和两种面对面抽样方法(受访者驱动抽样和基于空间的抽样)。这些数据将与当前的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来充分了解地理和人口结构对无家可归者的社会网络结构的影响，以及由此对无家可归的时间和持续时间的影响，从而充分利用产生的数据。空间网络模型已被证明提供了对无家可归者网络的洞察。这项研究的结果提供了关于无家可归者的空间维度和新的统计网络方法的所需信息，以提供可更新的模拟模型，以通过无家可归者社区传播信息(在线和离线)和疾病(离线)。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.