Integrating social network data into GISystems
Integrating social network data into GISystems
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DOI：
10.1080/13658816.2016.1153103
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发表时间：
2016-01-01
影响因子：
5.7
通讯作者：
Andris, Clio
中科院分区：
文献类型：
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作者：
Andris, Clio
如今，在线社交媒体提供了关于社交网络（SN）和基于位置的社交网络（LBSN）的新的且丰富的数据源，即，个人之间联系的地理定位证据虽然SN已被用于显示社交连接的大小如何随距离而减小，但很少有示例说明如何将SN作为GI系统中的层。如果可以在地理信息系统（GIS）环境中分析SN以及人际关系，我们就可以更好地模拟人类如何在复杂的地理景观中社交、共享信息和形成社会群体。我们的目标是为分析SN（来自在线社交媒体、电信、调查等）提供指导。通过结合社会网络分析（SNA）和地理信息系统的成熟领域，在地理空间内。首先，我们描述了为什么在地理空间中建模社会化对于理解人类行为至关重要。然后，我们概述了最佳实践和技术，通过引入“社会流”和“人类空间”等术语，以及数据和空间聚合类型的分类，在GISystems中嵌入SN节点和边缘。最后，我们探讨了案例研究的GISystems内的SNA插图从不同的地区位于玻利维亚，中国，科特迪瓦，新加坡，英国和美国，使用的概念，如地理定位的二元体，自我改变的关系，节点功能的作用，模块化，和网络传递性。
Today, online social media outlets provide new and plentiful sources of data on social networks (SNs) and location-based social networks (LBSNs), i.e., geolocated evidence of connections between individuals. While SNs have been used to show how the magnitude of social connectivity decreases with distance, there are few examples of how to include SNs as layers in a GISystem. If SNs, and thus, interpersonal relationships, could be analyzed in a geographic information system (GIS) setting, we could better model how humans socialize, share information, and form social groups within the complex geographic landscape.Our goal is to facilitate a guide for analyzing SNs (as derived from online social media, telecommunications, surveys, etc.) within geographic space by combining the mature fields of social network analysis (SNA) and GISystems. First, we describe why modeling socialization in geographic space is essential for understanding human behavior. We then outline best practices and techniques for embedding SN nodes and edges in GISystems by introducing terms like 'social flow' and 'anthrospace', and categorizations for data and spatial aggregation types. Finally, we explore case study vignettes of SNA within GISystems from diverse regions located in Bolivia, China, Cote d'Ivoire, Singapore, the United Kingdom, and the United States, using concepts such as geolocated dyads, ego-alter relationships, node feature roles, modularity, and network transitivity.