  Moving window and hot spot detection analyses are statistical methods used to
analyze point patterns within a given area. Such methods have been used to
successfully detect clusters of point events such as car thefts or incidences
of cancer. Yet, these methods do not account for the connections between
individual events, such as social ties within a neighborhood. This paper
presents 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 area.
  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 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.
