Urban Data Memory: Using Generative AI to Structure and Visualize Zoning Data for Urban Planning Evaluation

Urban planners face significant challenges in systematically and quantitatively evaluating past planning practices, stemming, among other reasons, from the scarcity of accessible structured data. The period from a plan’s initiation to implementation can span generations; recorded data from the planning processes are often deemed obsolete for addressing present concerns by the time of post-occupancy evaluation. This research examines whether generative AI can help bridge this gap and under what conditions - highlighting both challenges and opportunities - by introducing a system that responsively transforms qualitative zoning data into structured, queryable formats to support the quantitative analysis of planning practices. A database of ~150 approved semi-structured urban plans under Tel Aviv municipality’s local jurisdiction supports this project's case study. The system relies on proprietary LLMs (ChatGPT, Claude), streamlining a natural language query input through 3 agentic tasks: (1) RAG (Retrieval Augmented Generation) based querying, generating free-text answers from all plans, (2) structuring the answers to a valid JSON, and (3) visualizing structured data. Key findings indicate an 85.45% precision of the system, as evaluated through an end-to-end assessment of 11 representative queries, each validated against 40 manually labeled plans. The tool provides actionable insights, enabling queries such as trends in sheltered bicycle parking approvals or the status of affordable housing planning over the past decade. This research underlines the significance of flexibly structuring non- and semi-structured data for urban science. It addresses the growing gap between static legacy data collection and real-time policymaking, democratizing access to planning information and fostering informed decision-making practices. Integrating cutting-edge AI-driven tools contributes to the current discourse on AI applications for city management and planning by providing a replicable model for more cities and planning datasets to build upon and improve.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Kupershmidt, Adi

Advisor dc:contributor.advisor

• Williams, Sarah E.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164587

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164587