Abstract

Spatial layout and structure deeply influence how people interact with the urban environment. The physical complexity of the growing urban built environment holds significant information of transdisciplinary interest to better understand the implications for many societal problems. However, many researchers continue to rely on discontinuous data or simplistic geographic measures to simplify their analyses. These studies fail to quantify and fully capture structural impacts on urban function. To address this gap, this dissertation examines the current state of urban accessibility studies that use graph-theoretic methods to study the function of the urban environment providing more accurate measurement outcomes. Evidence-based research shows the importance of accurate road network models and rigorous graph-theoretic analysis. To support the power of this approach, methods leveraging graph theory are used to better understand observed behaviors through trajectory analysis on an urban street network. A method for defining continuous metropolitan regions is presented along with the resulting graphs representing the 100 largest metropolitan statistical areas (MSAs) in the United States. A comparative analysis illustrates the drastic structural differences beyond traditional city boundaries caused by urban sprawl. Following, several methodological approaches are explored to measure areas of low food access to better understand practical applications of how urban structure can create regions where residents lack access to affordable and healthy food. Ultimately, the USDA-ERS Food Access Research Atlas, a widely accepted food access classification system, has many methodological shortcomings. This dissertation dissects the USDA-ERS food access measurement methodology, demonstrating the impact of using more precise, graph-theoretic measurement methods, consistent scales of measurement, and continuous urban data.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.

Graduation Date

2023

Semester

Spring

Advisor

Kider Jr., Joe

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

School of Modeling, Simulation, and Training

Degree Program

Modeling and Simulation

Identifier

CFE0009854; DP0028152

URL

https://purls.library.ucf.edu/go/DP0028152

Language

English

Release Date

November 2026

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)

Download

Restricted to the UCF community until November 2026; it will then be open access.

Share

COinS