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

职业：将空间异质性纳入社交网络分析的研究和教育框架

• 批准号: 2045271
• 负责人: Clio Andris
• 金额: $ 49.15万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045271&HistoricalAwards=false
• 关键词: CAREER; Research; Educational; Framework; Incorporating

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.

社交网络是个人或群体之间关系的表现形式，从一个孤立村庄的家庭关系到数十亿的在线社交媒体联系。分析这些结构可以告诉我们谁是连接良好的，谁在网络中很重要，以及更高级的结构属性，如信息将如何在网络中流动。然而，这些分析通常在不参考网络在地理空间中的位置的情况下进行。绘制人或组织在社交网络中的位置（节点）以及它们之间的关系（边）使我们能够测量新的属性，例如会议障碍，附近的重要设施以及知识和信息在空间中流动的路径。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）数据存储库，其中包括流行病学，社会学，人类学，历史，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SSN的研究，研究人员将通过软件，教程，课程指导和SSN研究人员在线社区的支持来领导教育和研究基础设施的发展。该学院早期职业发展（CAREER）奖将支持对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的调查，以及地理空间规定中随之而来的关系。调查员将通过测量节点和图形特性，融合SNA和GIS原则，并将这些特性与环境特征联系起来，以便同时进行评估（例如热点和网络中心性）。她将比较真实的SSN和基于运动、距离衰减、城市形成和城市等级制度原则的网络模拟，以及社会网络特征，如偏好连接、结构洞和小世界模型。本研究将展示规模、范围、抽样和简化方面的选择如何影响统计结果，并设计地理空间中社交网络分析和建模的最佳实践。该项目将通过纳入地点、土地使用、地形、兴趣点、行政区、人口密度和交通特征的特点，有助于改进自然科学中使用的模型。该项目还强调了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互动。预期成果是测试社交网络如何与周围环境互动的创新方法，以及对个人或群体如何以及为什么在周围环境和建筑环境的规定中彼此形成关系的新理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Is your neighbor your friend? Scan methods for spatial social network hotspot detection

• DOI: 10.1111/tgis.13050
• 发表时间: 2023-04
• 期刊: Transactions in GIS
• 影响因子: 2.4
• 作者: Xiaofan Liang;Joshua Baker;Daniel DellaPosta;Clio Andris