Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Bachelor of Science in Data Science

Degree Level

Undergraduate

Department

Data Science

Advisor/Mentor

Schubert, Karl D.

Committee Member

Adams, Tianjiao

Second Committee Member

Sullivan, Kelly

Abstract

Fire station placement has a critical role in emergency response efficiency and community safety. Traditional optimization models focus on mainly the minimization of response times and the maximization of coverage. However, this approach may overlook potential socioeconomic disparities that can influence emergency demand. This study seeks to expand upon the existing project of zoning a fire station in Sugar Land, TX, by integrating spatial road network analysis and publicly available census data—including population density, median household income, and age-based vulnerability—into a Maximal Coverage Location Problem (MCLP) framework. Using a road network-based travel time with realistic constraints, the goal is to identify an optimal location for an additional station. This weighted optimization’s goal is to ensure that the coverage is equitably distributed, prioritizing vulnerable populations while maintaining efficiency. The findings will provide a data-driven methodology for emergency planners to enhance public safety via equitable resource allocation.

Keywords

Socioeconomic; Optimization; Sugar Land; MCLP

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