Decoding Neighborhood Environments with Large Language Models

--Neighborhood environments include physical and environmental conditions such as housing quality, roads, and sidewalks, which significantly influence human health and wellbeing. Traditional methods for assessing these environments, including field surveys and geographic information systems (GIS), are resource-intensive and challenging to evaluate neighborhood environments at scale. Although machine learning offers potential for automated analysis, the laborious process of labeling training data and the lack of accessible models hinder scalability. This study explores the feasibility of large language models (LLMs) such as ChatGPT and Gemini as tools for decoding neighborhood environments (e.g., sidewalk and powerline) at scale. We train a robust YOLOv11-based model, which achieves an average accuracy of 99.13% in detecting six environmental indicators, including streetlight, sidewalk, powerline, apartment, single-lane road, and multilane road. We then evaluate four LLMs, including ChatGPT, Gemini, Claude, and Grok, to assess their feasibility, robustness, and limitations in identifying these indicators, with a focus on the impact of prompting strategies and fine-tuning. We apply majority voting with the top three LLMs to achieve over 88% accuracy, which demonstrates LLMs could be a useful tool to decode the neighborhood environment without any training effort. Neighborhood environments refer to the community where people live and participate in daily life, including its physical and environmental conditions, which play a critical role in shaping human health, behavior, and quality of life [1]- [3]. Those environmental indicators include housing quality, streetlights, parks, sidewalks, green space, power lines, etc. Research studies have shown the impact of neighborhood environments on health outcomes (e.g., obesity, diabetes, and mortality rates) [4], [5] and well-being factors (e.g., physical activity and access to nutritious foods) [4], [6].