Adolescent Health in an Urban Environment

项目摘要 主机代管网络-捕获个人和位置之间的连接的双模网络 地理空间-在从传染病研究到卫生科学的各个领域具有广泛的相关性 疾病传播，以了解社会过程对健康结果和行为的影响。 然而，尽管它们具有广泛的相关性，但用于理解主机代管网络的统计方法是有限的。 这项以方法论为导向的建议侧重于制定一个统计框架，以研究 基于双线性混合效应模型的协同定位网络 配置文件。通过潜在的交互随机效应，我们的模型捕捉到了个体之间的依赖关系 基于他们对空间的共享使用，以及基于经常使用这些空间的个人的地点之间的共享。我们的 灵活的建模框架使用混合成员结构来放松活动配置文件的假设 是静态的，并利用数据增强策略来允许模型的版本直接 或间接说明演员-地点关系之间的依赖关系。我们的新统计模型将是 用于分析收集的活动模式数据，作为#年青少年健康与发展的一部分 上下文(Ahdc)研究，俄亥俄州富兰克林县正在进行的数据收集工作。通过基于GPS的 智能手机跟踪和时空预算软件，Ahdc的研究提供了关于一地两检的丰富细节 研究区域内的青少年网络。此外，智能手机管理的丰富的调查数据 生态即时评估(捕获位置、社交网络合作伙伴的实时测量 研究参与者的存在、活动、危险行为和情绪)和生物测量数据可用。 认识到我们提出的统计模型可能无法捕捉到共存的结构 完全基于观察到的活动模式的青少年的网络结构，我们还建议 通过信息性先验分布将来自社交媒体的相关信息嵌入到我们的分析中 在模型参数上。为此，我们提出了新的数据挖掘算法来获取潜在的活动模式 推特帖子和网络结构中的主题和重合配置文件。特别是，我们扩展了命名实体 空间设置的识别方法，以自动检索与活动模式相关的信息 开发新的方法，根据活动模式信息与特定信息的相关性来确定其优先顺序 子群体(这里是青少年)使用可扩展的情绪分析。使用我们新的统计和数据 挖掘方法论，我们将进行详细的统计分析，探索空间关系 和社会空间暴露，来自推断的共同定位网络和生理应激 青少年。

Project Summary Co-location networks – two-mode networks that capture connections between individuals and locations in geographic space – have broad relevance in the health sciences in areas ranging from the study of infectious disease transmission to understanding the influence of social processes on health outcomes and behaviors. Despite their broad relevance, however, statistical methods for understanding co-location networks are limited. This methodologically oriented proposal focuses on the development of a statistical framework for the study of co-location networks using a bilinear mixed-effects model with interacting latent activity pattern motifs and profiles. Through latent interacting random effects, our model captures the dependence between individuals based on their shared use of space and between locations based on the individuals who frequent them. Our flexible modeling framework uses a mixed-membership structure to relax the assumption that activity profiles are static and takes advantage of a data augmentation strategy to allow versions of the model with either direct or indirect specification of the dependence between actor-location ties. Our novel statistical models will be used in analyses of activity pattern data collected as part of the Adolescent Health and Development in Context (AHDC) Study, an ongoing data collection effort in Franklin County, Ohio. Through GPS-based smartphone tracking and space-time budget software, the AHDC Study provides rich detail on the co-location networks of adolescents in the study area. In addition, a wealth of survey data, smartphone-administered Ecological Momentary Assessments (capturing real-time measures of location, social network partner presence, activities, risk behaviors, and mood), and biomeasure data on the study participants are available. Recognizing that our proposed statistical model may not be able to capture the structure the co-location network structure of AHDC adolescents based entirely on their observed activity patterns, we also propose to embed relevant information derived from social media into our analyses through informative prior distributions on model parameters. To do so, we propose novel data mining algorithms to retrieve potential activity pattern motifs and coincident profiles from Twitter posts and network structure. In particular, we extend named entity identification methods to the spatial setting to automatically retrieve information relevant to activity patterns and develop novel methods for prioritizing activity pattern information based on its relevance to particular subpopulations (here, adolescents) using scalable sentiment analysis. Using our new statistical and data mining methodology, we will perform detailed statistical analyses to explore the relationship between spatial and socio-spatial exposures derived from an inferred co-location network and physiological stress in adolescents.