Date of Award
5-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Planning, Design, and the Built Environment
Committee Chair/Advisor
John Gaber
Committee Member
Robert Benedict
Committee Member
Eric Morris
Committee Member
Barry Nocks
Abstract
History matters. History provides a vital foundation for shaping present decisions and future planning. By understanding a city’s past, cities can make more thoughtful, informed choices to guide development and policy with greater intention and impact.
Greenville, South Carolina, is a unique city planning case study and learning laboratory. Greenville’s Downtown revitalization since the 1980s demonstrates how historical insights can be integrated into contemporary policymaking to create vibrant, human-centered, and citizen-focused urban environments. Greenville’s evolution from a deserted, forgotten Main Street to one of the world’s most livable cities is an example of community development worthy of emulation from other cities.
This study redefines planning practice by integrating history with advanced technologies, particularly Artificial Intelligence (AI) and Natural Language Processing (NLP). Using a three-journal article dissertation format, this study explores the applied history approach in city planning, emphasizing the importance of local historical knowledge and AI tools to enhance accessibility and decision-making; It uses AI applications to analyze historical and contemporary planning documents, demonstrating NLP's potential to uncover trends, streamline complex analyses, and create tools that make the past accessible. Finally, it explains the visual evolution of urban planning thought through professional planning journal cover analysis, revealing shifts in focus from abstract U.S.-centric perspectives to globally grounded representations.
Using Greenville as a planning case study, the research highlights how integrating historical insights with AI-driven frameworks enables more connected city planning and offers actionable recommendations to advance urban planning practice.
Recommended Citation
Stall, Russell H., "Integrating Applied History and Artificial Intelligence (AI) Into City Planning Practice" (2025). All Dissertations. 3884.
https://open.clemson.edu/all_dissertations/3884
Author ORCID Identifier
0009-0006-1172-1331