Towards Human-AI Collaborative Urban Science Research Enabled by Pre-trained Large Language Models

TL;DR

This paper explores how pre-trained large language models like ChatGPT can support urban science research by aiding understanding, analysis, and monitoring of urban phenomena, while addressing technical and social challenges.

Contribution

It provides a comprehensive analysis of potential applications, challenges, and future prospects of PLMs in urban science research, highlighting new opportunities for human-AI collaboration.

Findings

PLMs can help understand complex urban concepts.

PLMs facilitate urban spatial form identification.

PLMs assist in disaster monitoring and public sentiment analysis.

Abstract

Pre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potential applications of PLMs to urban institution, urban space, urban information, and citizen behaviors research through seven examples using ChatGPT. We also examined the challenges of PLMs in urban science research from both technical and social perspectives. The prospects of the application of PLMs in urban science research were then proposed. We found that PLMs can effectively aid in understanding complex concepts in urban science, facilitate urban spatial form identification, assist in disaster monitoring, and sense public sentiment. At the same time, however, the applications of PLMs in urban science research face evident threats, such as technical limitations, security, privacy, and social bias. The development of fundamental models based on domain knowledge and human-AI collaboration may help improve PLMs to support urban science research in future.