# Demo2Vec: Learning Region Embedding with Demographic Information

arXiv: https://arxiv.org/abs/2409.16837 · HTML: https://mlsanity.com/paper/2409.16837/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Multi-Graph Fusion Networks for Urban Region Embedding](https://mlsanity.com/paper/2201.09760/) (2022)
- [Urban Region Embedding via Multi-View Contrastive Prediction](https://mlsanity.com/paper/2312.09681/) (2023)

## Built on by (most cited first, 5 of 5)

- [MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale](https://mlsanity.com/paper/2506.01297/) (2025)
- [Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling](https://mlsanity.com/paper/2607.13558/) (2026)
- [CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment](https://mlsanity.com/paper/2608.21041/) (2026)
- [MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models](https://mlsanity.com/paper/2608.17848/) (2026)
- [PlacePulse: Learning Transferable Place Representations from Structural Geographic Data](https://mlsanity.com/paper/2610.13390/) (2026)

## Abstract (arXiv)

Demographic data, such as income, education level, and employment rate, contain valuable information of urban regions, yet few studies have integrated demographic information to generate region embedding. In this study, we show how the simple and easy-to-access demographic data can improve the quality of state-of-the-art region embedding and provide better predictive performances in urban areas across three common urban tasks, namely check-in prediction, crime rate prediction, and house price prediction. We find that existing pre-train methods based on KL divergence are potentially biased towards mobility information and propose to use Jenson-Shannon divergence as a more appropriate loss function for multi-view representation learning. Experimental results from both New York and Chicago show that mobility + income is the best pre-train data combination, providing up to 10.22% better predictive performances than existing models. Considering that mobility big data can be hardly accessible in many developing cities, we suggest geographic proximity + income to be a simple but effective data combination for region embedding pre-training.