hood2vec: Identifying Similar Urban Areas Using Mobility Networks
This work addresses urban planning and analysis by providing a novel mobility-based approach to compare areas, though it is incremental as it builds on existing embedding techniques applied to new data.
The paper tackled the problem of identifying similar urban areas by introducing hood2vec, a method that learns node embeddings from mobility networks using Foursquare check-ins, and found low correlation with traditional venue-based similarity measures, indicating that mobility dynamics capture different aspects of urban similarity.
Which area in NYC is the most similar to Lower East Side? What about the NoHo Arts District in Los Angeles? Traditionally this task utilizes information about the type of places located within the areas and some popularity/quality metric. We take a different approach. In particular, urban dwellers' time-variant mobility is a reflection of how they interact with their city over time. Hence, in this paper, we introduce an approach, namely hood2vec, to identify the similarity between urban areas through learning a node embedding of the mobility network captured through Foursquare check-ins. We compare the pairwise similarities obtained from hood2vec with the ones obtained from comparing the types of venues in the different areas. The low correlation between the two indicates that the mobility dynamics and the venue types potentially capture different aspects of similarity between urban areas.