Which Area Has Clustered Local Social Connections? Spatial Social Network (SSN) Hot Spot Detection through Scan Methods

Keywords: spatial network, social network, GIS, crime
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 12:40 PM (Eastern Time (US & Canada)) - 2/25/2022 02:00 PM (Eastern Time (US & Canada))
Room: Virtual 18

Abstract

In GIS, we use moving window and hot spot detection to analyze point patterns within a given area. Such methods can detect clusters of point events such as concentration of crime incidences or disease incidences. Yet, these methods do not account for the connections between individual events, such as personal connections, but instead, only find clusters of points.

Here, we share two GIS methods, EdgeScan and NDScan, for capturing areas with high and low levels of local social connections. Both methods are moving window processes that count the number of edges and network density, respectively, in a given focal area (window area). The focal window attaches resultant EdgeScan and NDScan statistics to nodes at the center of the focal window. We implement these methods on a case study of 1960s connections between members of the Mafia in New York City. We use various definitions of a focal neighborhood including Euclidean, Manhattan, and K Nearest Neighbor (KNN) definitions. We find that KNN tends to overstate the values of local networks and that there is more variation in outcome values for nodes on the periphery of the study area. We also find that, location-wise, EdgeScan and NDScan hot spots differ from traditional spatial hot spots in the study area. These methods can be extended to future studies that detect local triads and motifs, which can capture the local network structure in more detail. We published the EdgeScan and NDScan methods in an R package called SSNtools available to the public (https://github.com/friendlycities-gatech/SSNtools).