Botanic garden visitors’ preference analysis - A case study of Chicago botanic garden

User-generated content (UGC) offers an extensive and readily accessible data source for understanding visitor preferences and experiences in natural landscapes. However, there is a lack of large-scale spatial analysis of UGC images depicting landscapes. This study introduces a novel framework that systematically manages and analyzes landscape UGC data to investigate visitor preferences at the Chicago Botanic Garden. The methodology involves web scraping Google Maps reviews and photos, employing generative AI models for computer vision-based image analysis, and applying natural language processing techniques for text analysis. Landscape spatial structure types, elements, components, and plant species are systematically categorized and quantified from the images, while sentiment analysis extracts visitors' feedback and experiences from the text reviews. The results provide insights into the most favored landscape spaces, elements, and specific components that enhance visitor experiences. The efficacy of integrating UGC data analysis with GenAI models is demonstrated, bridging existing research gaps concerning spatial assessment and quantification of visitors' landscape preferences. This study contributes methodological advances in utilizing UGC for landscape research and offers practical implications to empower landscape managers and designers in creating visitor-centric spaces aligned with user preferences. The systematic management of landscape UGC data presents a viable complementary approach to conventional visitor survey methods for landscape design and management.

Name thesis:degree_name

M.L.A.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Landscape Architecture

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Huang, Hexiang

Contributors dc:contributor

• Deal, Brian
• Sullivan, William C.
• Lemon, Kelley
• Fang, Fang

• Landscape Spatial Analysis
• Visitor Preferences
• User-generated Content

Statement dc:rights

• Copyright 2024 Hexiang Huang

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124592

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124592