City2City: Translating Place Representations across Cities

TL;DR

This paper introduces a method to translate place representations across different cities using mobility data, enabling transfer of urban insights similar to language translation, validated with landuse data.

Contribution

It presents a novel approach by applying language translation techniques to transfer place embeddings between cities, addressing the lack of inter-city perspective in urban mobility studies.

Findings

Successfully translated place representations between cities

Validated translations with landuse data

Demonstrated cross-city transferability of urban insights

Abstract

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 \textit{cities} and \textit{languages} 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.