Leveraging Large Language Models for Automated Assessment and Mapping in Participatory Urban Planning
TL;DR
The findings demonstrate the feasibility of leveraging AI and LLMs to create a responsive and interactive platform that not only streamlines data collection but also enriches the participatory planning process.
Abstract
Abstract. This research introduces an innovative platform designed to enhance citizen engagement in urban planning and management by integrating emerging technologies such as Artificial Intelligence (AI), Large Language Models (LLMs), and chatbots. Traditional Public Participation Geographic Information Systems (PPGIS) often face challenges in effectively capturing and analyzing citizen input. This platform addresses these limitations by enabling users to articulate urban issues or ideas in natural language, which are then processed through AI-driven Natural Language Processing (NLP) techniques to identify key elements such as location, issue type, and intensity. Furthermore, the platform facilitates interactive dialogues, allowing citizens to inquire about perspectives from other community members, thereby fostering a dynamic exchange of views. In the absence of an initial user base, a dataset comprising 2,000 tweets related to Montreal's public transportation was curated. An LLM was fine-tuned using this data, equipping the model to respond to queries concerning Montreal's public transportation system. The findings demonstrate the feasibility of leveraging AI and LLMs to create a responsive and interactive platform that not only streamlines data collection but also enriches the participatory planning process. This approach has the potential to transform urban governance by making it more inclusive and data driven.