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Virginia Tech

Identifying Healthcare Access and Enhancing Geospatial Analysis with Generative AI

Abstract

dc:description.abstract

This thesis brings together geospatial modeling and generative artificial intelligence to address healthcare accessibility and automation of spatial analysis. The first study examines disparities in dental care access across six regions of Virginia by comparing driving and public transit modes for both all dental clinics and those accepting Medicaid. Using a modified two-step floating catchment area (2SFCA) method, the research quantifies access based on travel time, supply-demand ratios, and vehicle ownership. Results show that public transit accessibility is significantly lower and more unequal than driving access, particularly for Medicaid recipients, with variation across regions. Spatial error models further reveal demographic factors, such as poverty, race, and vehicle access, influence accessibility patterns. The second study fine-tunes OpenAI's GPT-4o-mini model to convert natural language queries into executable Python code for geospatial analysis. Trained on over 600 geospatial prompt-completion pairs using Virginia health data, the model achieves an 89.7% accuracy rate, improving significantly over the baseline. It integrates spatial reasoning, fuzzy geographic matching, and modular function calls to reduce execution errors and enhance usability. Together, these studies demonstrate how AI and geospatial science can jointly address inequities in healthcare access while making spatial tools more accessible to policymakers, researchers, and the general public.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Geography
Department dc:contributor.department
Geography
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2025

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Sherman, Zachary Harold
Chairs dc:contributor.committeechair
  • Kim, Junghwan
  • Zhang, Mengxi
Committee member dc:contributor.committeemember
  • Crawford, Thomas Wall

Subjects

dc:subject × 9

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:44344
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/137279

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Sherman, Zachary Harold. Identifying Healthcare Access and Enhancing Geospatial Analysis with Generative AI. masters thesis, Virginia Tech, 2025. https://hdl.handle.net/10919/137279