# City2City: Translating Place Representations across Cities

arXiv: https://arxiv.org/abs/1911.12143 · HTML: https://mlsanity.com/paper/1911.12143/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patterns](https://mlsanity.com/paper/2407.19872/) (2024)
- [SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective](https://mlsanity.com/paper/2604.07383/) (2026)

## Abstract (arXiv)

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies have generated place representations (or embeddings) from mobility patterns in a similar manner to word embeddings to better understand the functionality of different places within a city. However, studies have been limited to generating such representations of cities in an individual manner and has lacked an inter-city perspective, which has made it difficult to transfer the insights gained from the place representations across different cities. In this study, we attempt to bridge this research gap by treating textitcities and textitlanguages analogously. We apply methods developed for unsupervised machine language translation tasks to translate place representations across different cities. Real world mobility data collected from mobile phone users in 2 cities in Japan are used to test our place representation translation methods. Translated place representations are validated using landuse data, and results show that our methods were able to accurately translate place representations from one city to another.