  OpenStreetMap (OSM) constitutes an unprecedented, free, geographic
information source contributed by millions of individuals, resulting in a
database of great volume and heterogeneity. In this study, we characterize the
heterogeneity of the entire OSM database and historical archive in the context
of big data. We consider all users, geographic elements, and user contributions
from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear
methods such as power-law statistics and head/tail breaks to uncover and
illustrate the underlying scaling properties. All three aspects (users,
elements, and contributions) demonstrate striking power laws or heavy-tailed
distributions. The heavy-tailed distributions imply that there are far more
small elements than large ones, far more inactive users than active ones, and
far more lightly edited elements than heavily edited ones. Furthermore, about
500 users in the core group of the OSM are highly networked in terms of
collaboration.
  Keywords: OpenStreetMap, big data, power laws, head/tail breaks, ht-index
