Decoding Neighborhood Environments with Large Language Models

TL;DR

This paper investigates using large language models to identify neighborhood environmental features from textual data, achieving high accuracy without extensive training, thus offering a scalable alternative to traditional assessment methods.

Contribution

It demonstrates the feasibility of employing LLMs like ChatGPT and Gemini for decoding neighborhood environments at scale, reducing reliance on resource-intensive traditional methods.

Findings

YOLOv11-based model achieves 99.13% accuracy in detecting environmental indicators.

LLMs combined with majority voting reach over 88% accuracy in identifying neighborhood features.

Using LLMs reduces the need for extensive training data and manual labeling.

Abstract

Neighborhood environments include physical and environmental conditions such as housing quality, roads, and sidewalks, which significantly influence human health and well-being. 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.