Copresence networks are constructed by associating individuals who are at the same place at the same time - in our case at police-involved incidents. They generalize networks such a co-offender networks, which are becoming mainstream police and intelligence tools. Because copresence can be generated from police incident data, such networks are cheap to construct since they require no specialised surveillance - they are implicit in data that is routinely collected.We demonstrate that copresence network analytics, using graph embedding, gives a sense of the criminal landscape in a city; shows the importance of non-criminals in this landscape; and suggests targets for further in-depth attention (unusual subgroups, non-criminals who are brokers). We also show that homophily in level of criminality generally holds, but there are strong exceptions that deserve police attention. Copresence networks also arise naturally in other domains such as border protection and intelligence surveillance, so the techniques described here are also applicable in those domains.
|Name||2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018|
|Conference||16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018|
|Period||09/11/18 → 11/11/18|