Is your neighbor your friend? Scan methods for spatial social network hotspot detection
Is your neighbor your friend? Scan methods for spatial social network hotspot detection
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DOI：
10.1111/tgis.13050
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发表时间：
2023-04
影响因子：
2.4
通讯作者：
Xiaofan Liang;Joshua Baker;Daniel DellaPosta;Clio Andris
中科院分区：
文献类型：
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作者：
Xiaofan Liang;Joshua Baker;Daniel DellaPosta;Clio Andris
GIS 分析使用移动窗口方法和热点检测来识别给定区域内的点模式。此类方法可以检测点事件的集群，例如犯罪或疾病发生率。然而，这些方法没有考虑实体之间的连接，因此，事件集中度相对稀疏但网络连接性较高的区域可能不会被检测到。我们开发了两种扫描方法（即移动窗口或焦点过程），EdgeScan 和 NDScan，用于检测局部空间社会联系。这些方法分别捕获给定焦点区域中每个节点的边缘和网络密度。我们将方法应用于 1960 年代纽约市黑手党成员的社交网络和 2019 年佐治亚州亚特兰大的家庭到餐厅访问的空间网络。这些方法成功地捕捉到了黑手党成员关系密切、餐厅游客高度本地化的重点区域；这些结果与使用 Getis–Ord Gi* 统计数据进行传统空间热点分析得出的结果不同。最后，我们描述了如何将这些方法应用于加权、有向和二分网络，并提出未来的改进建议。
GIS analyses use moving window methods and hotspot detection to identify point patterns within a given area. Such methods can detect clusters of point events such as crime or disease incidences. Yet, these methods do not account for connections between entities, and thus, areas with relatively sparse event concentrations but high network connectivity may go undetected. We develop two scan methods (i.e., moving window or focal processes), EdgeScan and NDScan, for detecting local spatial‐social connections. These methods capture edges and network density, respectively, for each node in a given focal area. We apply methods to a social network of Mafia members in New York City in the 1960s and to a 2019 spatial network of home‐to‐restaurant visits in Atlanta, Georgia. These methods successfully capture focal areas where Mafia members are highly connected and where restaurant visitors are highly local; these results differ from those derived using traditional spatial hotspot analysis using the Getis–Ord Gi* statistic. Finally, we describe how these methods can be adapted to weighted, directed, and bipartite networks and suggest future improvements.