Date of Award
6-3-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2026
Department
Computer Science and Engineering
First Advisor
Sharon Hsiao
Abstract
EcoVisor is a prototype system for extracting, structuring, and visualizing sustainability related metrics from natural language articles. The project integrates a FastAPI backend with a Next.js frontend to transform raw text or article URLs into structured sustainability data, detect trends over time, and generate visual outputs for users. Key capabilities include automated scraping of web articles for sustainability semantics, sustainability related metric data extraction, post-processing of extracted values into cleaned segments and sentences using HTML parsing, grouping of metrics by category and year, generation of Chart.js visualizations, and production of concise “comic brief” summaries for accessible storytelling. EcoVisor aims to bridge the gap between complex sustainability reports and intuitive visual analysis, allowing its users to more easily understand real-world content more quickly and easily. The goal is for more people all around the world to understand trends regarding many topics such as emissions, trajectories, and other environmental indicators.
Recommended Citation
Van Dyke, Matthew; Chen, Emily; Le, Hayden; and Xu, Dorothy, "Visualizing Sustainability Data with AI" (2025). Computer Science and Engineering Senior Theses. 375.
https://scholarcommons.scu.edu/cseng_senior/375
