  Meta-data from photo-sharing websites such as Flickr can be used to obtain
rich bag-of-words descriptions of geographic locations, which have proven
valuable, among others, for modelling and predicting ecological features. One
important insight from previous work is that the descriptions obtained from
Flickr tend to be complementary to the structured information that is available
from traditional scientific resources. To better integrate these two diverse
sources of information, in this paper we consider a method for learning vector
space embeddings of geographic locations. We show experimentally that this
method improves on existing approaches, especially in cases where structured
information is available.
