Urban Region Embedding via Multi-View Contrastive Prediction
Zechen Li Weiming Huang Kai Zhao Min Yang Yongshun Gong Meng Chen
Recently, learning urban region representations utilizing multi-modal data
(information views) has become increasingly popular, for deep understanding of
the distributions of various socioeconomic features in cities. However,
previous methods usually blend multi-view information in a posteriors stage,
falling short in learning coherent and consistent representations across
different views. In this paper, we form a new pipeline to learn consistent
representations across varying views, and propose the multi-view Contrastive
Prediction model for urban Region embedding (ReCP), which leverages the
multiple information views from point-of-interest (POI) and human mobility
data. Specifically, ReCP comprises two major modules, namely an intra-view
learning module utilizing contrastive learning and feature reconstruction to
capture the unique information from each single view, and inter-view learning
module that perceives the consistency between the two views using a contrastive
prediction learning scheme. We conduct thorough experiments on two downstream
tasks to assess the proposed model, i.e., land use clustering and region
popularity prediction. The experimental results demonstrate that our model
outperforms state-of-the-art baseline methods significantly in urban region
representation learning.