  For short distance traveling in crowded urban areas, bike share services are
becoming popular owing to the flexibility and convenience. To expand the
service coverage, one of the key tasks is to seek new service ports, which
requires to well understand the underlying features of the existing service
ports. In this paper, we propose a new model, named for Efficient and Semantic
Location Embedding (ESLE), which carries both geospatial and semantic
information of the geo-locations. To generate ESLE, we first train a
multi-label model with a deep Convolutional Neural Network (CNN) by feeding the
static map-tile images and then extract location embedding vectors from the
model. Compared to most recent relevant literature, ESLE is not only much
cheaper in computation, but also easier to interpret via a systematic semantic
analysis. Finally, we apply ESLE to seek new service ports for NTT DOCOMO's
bike share services operated in Japan. The initial results demonstrate the
effectiveness of ESLE, and provide a few insights that might be difficult to
discover by using the conventional approaches.
