Region2vec

Abstract

With the advancement of urbanization, urban land use detection has become a research hotspot. Numerous methods have been proposed to identify urban land use, in which points of interest (POI) data is widely used, and sometimes other data source like GPS trajectories is incorporated. However, previous works have hardly fully utilized the global spatial information contained in the POI data, or ignored correlations between features when integrating multiple data source, so resulting in information loss. In this study, we propose an integrated framework titled Region2vec to detect urban land use type by combining POI and mobile phone data. First, POI-based region embeddings are generated by applying Glove model and LDA model to mine the global spatial information and land use topic distributions respectively. The mobile phone data is utilized to generate human activity pattern-based embeddings. Then a similarity matrix is constructed according to POI-based and activity pattern-based embeddings. Finally, the similarity measures are regarded as clustering features to extract the urban land use results. Experiments are implemented and compared with other urban land use algorithms based on data in Sanya, China. The results demonstrate the effectiveness of the proposed framework. This research can provide effective information support for urban planning.