We investigate data-driven strategies for planning the locations of bike-sharing system stations, uniquely considering both competition from nearby stations and the complementary influence of stations within a bike trip's target area. Our investigation is based on a dataset of over eight million entries from three jurisdictions affiliated with a leading German bike-sharing provider. To evaluate our approach, we employ a spatial out-of-sample technique on this dataset.