POI types characterization based on geographic feature embeddings

Abstract

Representing Points of Interest (POI) types, such as restaurants and shopping malls, is crucial to develop computational mechanisms that may assist in tasks such as urban planning and POI recommendation. The POI co-occurrences in different spatial regions have been used to represent POI types in high-dimensional vectors. However, such representations do not consider the geographic features (e.g. streets, buildings, rivers, parks) in the vicinity of POIs which may contribute to characterize such types. In this context, we propose the Geographic Context to Vector (GeoContext2Vec), an approach that relies on geographic features in the POIs' vicinity to generate POI types representation based on embeddings. We carried out an experiment to evaluate the GeoContext2Vec by using a POI type representation from the state-of-the-art that it does not consider geographic features. The promising results show that the geographic information provided by the GeoContext2Vec outperforms the state-of-the-art and demonstrates the relevance of surrouding geographic features on representing POI type more precisely.