Region Embedding with Intra and Inter-View Contrastive Learning

TL;DR

This paper introduces ReMVC, a contrastive learning model for multi-view region representation in urban data, significantly improving land usage clustering and region popularity prediction accuracy.

Contribution

The paper presents a novel multi-view contrastive learning framework, ReMVC, that effectively captures intra- and inter-view region features for urban data analysis.

Findings

ReMVC outperforms seven baseline methods in land usage clustering by over 30%.

The model improves region popularity prediction accuracy.

Contrastive learning enhances multi-view region representation quality.

Abstract

Unsupervised region representation learning aims to extract dense and effective features from unlabeled urban data. While some efforts have been made for solving this problem based on multiple views, existing methods are still insufficient in extracting representations in a view and/or incorporating representations from different views. Motivated by the success of contrastive learning for representation learning, we propose to leverage it for multi-view region representation learning and design a model called ReMVC (Region Embedding with Multi-View Contrastive Learning) by following two guidelines: i) comparing a region with others within each view for effective representation extraction and ii) comparing a region with itself across different views for cross-view information sharing. We design the intra-view contrastive learning module which helps to learn distinguished region embeddings and the inter-view contrastive learning module which serves as a soft co-regularizer to constrain the embedding parameters and transfer knowledge across multi-views. We exploit the learned region embeddings in two downstream tasks named land usage clustering and region popularity prediction. Extensive experiments demonstrate that our model achieves impressive improvements compared with seven state-of-the-art baseline methods, and the margins are over 30% in the land usage clustering task.