  Current approaches to community detection in social networks often ignore the
spatial location of the nodes. In this paper, we look to extract spatially-near
communities in a social network. We introduce a new metric to measure the
quality of a community partition in a geolocated social networks called
"spatially-near modularity" a value that increases based on aspects of the
network structure but decreases based on the distance between nodes in the
communities. We then look to find an optimal partition with respect to this
measure - which should be an "ideal" community with respect to both social ties
and geographic location. Though an NP-hard problem, we introduce two heuristic
algorithms that attempt to maximize this measure and outperform non-geographic
community finding by an order of magnitude. Applications to counter-terrorism
are also discussed.
