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

职业：测量和建模无家可归者的多模式网络和人口统计数据

• 批准号: 2142964
• 负责人: Zack Almquist
• 金额: $ 50万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142964&HistoricalAwards=false
• 关键词: CAREER; Measuring; Modeling; Multi; Modal

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.

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。收集关于受庇护和未受庇护的无家可归人口的丰富数据是困难的，这对研究人员和决策者来说是一个主要的复杂问题。本项目开发的方法基于最新的数据收集策略，这些策略是通过智能手机免费Wi-Fi、图书馆免费电脑和其他项目在无家可归者中广泛使用网络而实现的。本研究考察了在线和离线无家可归人口的人口统计（年龄、性别、种族/民族）及其社会关系，以了解它们对无家可归时间和寿命的影响。它通过改进对难以接触到的无家可归人口的估计和增进对影响无家可归持续时间的社会支持机制的了解，促进了研究。调查结果将使决策者和医疗保健领导人了解统计无家可归者以进行资源分配的最佳方法、传播信息的最佳做法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的一个地区，那里有很大一部分无家可归的人，在几个城市有目标样本。该研究整合了调查抽样和估计方面的最新进展，从在线来源获取人口统计和社会网络数据，以便与离线样本进行比较，包括住房和城市发展时间点人口统计。通过四种方法，采用新旧策略的新用途来测量难以到达的人群：两种在线抽样方法（广义网络放大法和网络抽样）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）。这些数据与当前的联邦标准时间点进行比较。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）来利用所得数据，充分了解地理和人口统计学对无家可归者的社会网络结构的影响，及其对无家可归时间和持续时间的最终影响。本研究的结果提供了关于无家可归者空间维度的必要信息，并提供了新的统计网络方法，为无家可归者社区的信息（在线和离线）和疾病（离线）扩散提供了可更新的模拟模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。