Where is also about time: A location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures

While geocoding returns coordinates for a full or partial address, the converse process of reverse geocoding maps coordinates to a set of candidate place identifiers such as addresses or toponyms. For example, numerous Web APIs map geographic point coordinates, e.g., from a user's smartphone, to an ordered set of nearby Places Of Interest (POI). Typically, these services return the k nearest POI within a certain radius and measure distance to order the results. Reverse geocoding is a crucial task for many applications and research questions as it translates between spatial and platial views on geographic location. What makes this process difficult is the uncertainty of the queried location and of the point features used to represent places. Even if both could be determined with a high level of accuracy, it would still be unclear how to map a smartphone's GPS fix to one of many possible places in a multi-story building or a shopping mall. In this work, we break up the dependency on space alone by introducing time as a second variable for reverse geocoding. We mine the geosocial behavior of users of online location-based social networks to extract temporal semantic signatures. In analogy to the notion of scale distortion in cartography, we present a model that uses these signatures to distort the location of POI relative to the query location and time, thereby reordering the set of potentially matching places. We demonstrate the strengths of our method by evaluating it against a purely spatial baseline by determining the Mean Reciprocal Rank and the normalized Discounted Cumulative Gain. Our method performs substantially better than said baseline.