CAREER: A Research and Educational Framework for Incorporating Spatial Heterogeneity into Social Network Analysis

社交网络是个人或群体之间关系的代表，从孤立村庄的家庭纽带到数十亿在线社交媒体连接。分析这些结构可以告诉我们，谁联系得很好，谁在网络中很重要，以及更高级的结构特性，比如信息将如何在网络中流动。然而，这些分析通常在没有参考网络在地理空间中的位置的情况下进行。通过映射社交网络中人员或组织(节点)的位置以及他们之间的关系(边)，我们可以测量新的属性，如会议障碍、附近的重要设施以及跨越空间的知识和信息流动路径。利用地理信息科学(GIS)和社会网络分析(SNA)的模型、技术和方法，该项目将创建一个公共空间社会网络(SSN)数据库，其中包括流行病学、社会学、人类学、历史学、政治学、组织科学和公共卫生等各种学科的SSN。除了数据，该项目还将开发新的模型和统计测试，以帮助更好地了解这些网络。为了帮助促进和交流对SSN的研究，调查员将通过软件、教程、课程指导和SSN研究人员在线社区的支持，领导教育和研究基础设施的发展。这一学院早期职业发展(职业)奖将支持调查建筑环境和物理环境的特征如何在社交网络的形成和发展中发挥作用，以及在地理空间规定内随之而来的关系。研究人员将通过测量节点和图形属性，并将这些属性与环境特征(例如热点和网络中心性)联系起来，将SNA和地理信息系统的原则融合在一起。她将把真实的SSN与基于移动、距离衰减、城市形成和城市等级以及优先依恋、结构性漏洞和小世界模型等社会网络特征的那些网络的模拟进行比较。这项研究将展示在规模、范围、抽样和简化方面的选择如何影响统计结果，并设计在地理空间中分析和建模社交网络的最佳实践。该项目将通过纳入地点、土地使用、地形、名胜点、行政区划、人口密度和交通特征等特征，为改进物理科学中使用的模型做出贡献。该项目还强调SSN在地图上的可视化，这有助于探索性的空间数据分析和与广大受众的接触。预期的结果是测试社交网络如何与周围环境互动的创新方法，以及对个人或团体如何以及为什么在其周围环境和建筑环境条款的背景下形成关系的新理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Social networks are representations of the relationships between individuals or groups, ranging from household ties in an isolated village to billions of online social media connections. Analyzing these structures can tell us who is well-connected, who is important in the network, and more advanced structural properties like how information will flow through the network. Yet, these analyses are typically conducted without references to where the network is located in geographic space. Mapping the locations of the people or organizations (nodes) in a social network and the relationships between them (edges) allows us to measure new properties such as obstacles to meeting, important nearby facilities, and paths of knowledge and information flow across space. Using models, technologies and methods from Geographic Information Science (GIS) and Social Network Analysis (SNA), the project will create a public Spatial Social Network (SSN) data repository that includes SSNs from a wide variety of subjects including epidemiology, sociology, anthropology, history, political science, organization science, and public health. In addition to the data, the project will develop new models and statistical tests to help better understand these networks. To help promote and communicate the study of SSNs, the investigator will lead the development of educational and research infrastructure through software, tutorials, curriculum guidance and the support of an online community of SSN researchers. This Faculty Early-Career Development (CAREER) award will support the investigation of how the features of the built environment and physical environment play a role in how social networks form and develop, and the relationships that ensue within the provisions of geographic space. The investigator will fuse both SNA and GIS principles by measuring node and graph properties, and linking these properties to environmental features for simultaneous assessment (e.g. hot spots and network centrality). She will compare real SSNs to simulations of those networks based on principles of movement, distance decay, city formation and the urban hierarchy, as well as social network characteristics such as preferential attachment, structural holes and small-world models. This research will show how choices in scale, scope, sampling, and simplification affect statistical results, and design best practices for analyzing and modeling social networks in geographic space. The project will contribute to improving models used in the physical sciences by incorporating features of places, land use, topography, points of interest, administrative districts, population density, and transportation features. This project also emphasizes the visualization of SSNs on maps, which helps both exploratory spatial data analysis and engagement with a broad audience. The expected outcomes are innovative methods for testing how social networks interact with their surroundings, and a new understanding of how and why individuals or groups form relationships with one another in the context of their surroundings and provisions of the built environment.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.