Consider the situation where we have some pre-trained classification models for bike rental stations (or any other spatially located data). Given a new rental station (deployment context), we imagine that there might be some rental stations that are more similar to this station in terms of the daily usage patterns, whether or not these stations are close by or not. We propose to use a Gaussian Process (GP) to model the relationship between geographic location and the type of the station, as determined by heuristics based on the daily usage patterns. For a deployment station, we then find the closest stations in terms of the Gaussian Process (GP) function output, and then use the models trained on these stations on the deployment station. We compare against several baselines, and show that this method is able to outperform those baselines.