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For short distance traveling in crowded urban areas, bike share services arebecoming popular owing to the flexibility and convenience. To expand theservice coverage, one of the key tasks is to seek new service ports, whichrequires to well understand the underlying features of the existing serviceports. In this paper, we propose a new model, named for Efficient and SemanticLocation Embedding (ESLE), which carries both geospatial and semanticinformation of the geo-locations. To generate ESLE, we first train amulti-label model with a deep Convolutional Neural Network (CNN) by feeding thestatic map-tile images and then extract location embedding vectors from themodel. Compared to most recent relevant literature, ESLE is not only muchcheaper in computation, but also easier to interpret via a systematic semanticanalysis. Finally, we apply ESLE to seek new service ports for NTT DOCOMO'sbike share services operated in Japan. The initial results demonstrate theeffectiveness of ESLE, and provide a few insights that might be difficult todiscover by using the conventional approaches.

Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services