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

Xiaofan Liang, Joshua Baker, Daniel DellaPosta, Clio Andris

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)607-625
Number of pages19
JournalTransactions in GIS
Issue number3
StatePublished - May 2023

All Science Journal Classification (ASJC) codes

  • General Earth and Planetary Sciences


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