Cities are highly dynamic entities, with urban elements such as businesses, cultural and social Points-of-Interests (POIs), housing, transportation and the like, continuously changing. In order to maintain accurate spatial information in these settings, crowd-sourcing models of data collection, such as in OpenStreetMap (OSM), have come under investigation. Like many crowd-sourcing platforms (e.g., Wikipedia), these geowikis exhibit tailing-off activity, bringing into question their long-term viability. In this paper, we begin an investigation into the sustainability of urban crowd-sourcing, by studying the network structure and geographical mapping of implicit communities of contributors in OSM. We observe that spatially clustered crowd-sourcing communities produce higher coverage than those with looser geographic affinity. We discuss the positive implications that this has on the future of urban crowd-sourcing. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.