# Urban Region Embedding via Multi-View Contrastive Prediction

arXiv: https://arxiv.org/abs/2312.09681 · HTML: https://mlsanity.com/paper/2312.09681/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding](https://mlsanity.com/paper/2001.11101/) (2020)
- [Multi-Graph Fusion Networks for Urban Region Embedding](https://mlsanity.com/paper/2201.09760/) (2022)
- [Region Embedding with Intra and Inter-View Contrastive Learning](https://mlsanity.com/paper/2211.08975/) (2022)
- [Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction](https://mlsanity.com/paper/2302.13094/) (2023)
- [PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction](https://mlsanity.com/paper/2003.07033/) (2020)

## Built on by (most cited first, 13 of 13)

- [FlexiReg: Flexible Urban Region Representation Learning](https://mlsanity.com/paper/2503.09128/) (2025)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [Urban Region Pre-training and Prompting: A Graph-based Approach](https://mlsanity.com/paper/2408.05920/) (2024)
- [Demo2Vec: Learning Region Embedding with Demographic Information](https://mlsanity.com/paper/2409.16837/) (2024)
- [MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale](https://mlsanity.com/paper/2506.01297/) (2025)
- [Boundary Prompting: Elastic Urban Region Representation via Graph-based Spatial Tokenization](https://mlsanity.com/paper/2503.07991/) (2025)
- [UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Models](https://mlsanity.com/paper/2505.11654/) (2025)
- [UrbanVerse: Learning Urban Region Representation Across Cities and Tasks](https://mlsanity.com/paper/2602.15750/) (2026)
- [Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling](https://mlsanity.com/paper/2607.13558/) (2026)
- [A Modality-Tailored Graph Modeling Framework for Urban Region Representation via Contrastive Learning](https://mlsanity.com/paper/2509.23772/) (2025)
- [ToPT: Task-Oriented Prompt Tuning for Urban Region Representation Learning](https://mlsanity.com/paper/2602.01610/) (2026)
- [MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models](https://mlsanity.com/paper/2608.17848/) (2026)
- [Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding](https://mlsanity.com/paper/2609.08268/) (2026)

## Abstract (arXiv)

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.