Date of Award

7-31-2026

Document Type

Open Access Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Business Administration

First Advisor

Jeffrey M. Keisler

Second Advisor

Michael P. Johnson

Third Advisor

Davood Golmohammadi

Abstract

Addressing climate-induced flooding and housing insecurity requires decisions that are technically sound, locally meaningful and feasible to implement. In public transit systems, flooding can damage infrastructure, increase travel time and reduce access to essential destinations, especially for older adults. Shared housing offers an alternative housing model where staff must balance affordability, compatibility, service exchange and participant choice when pairing home providers with housing seekers. In this dissertation, I examine how analytics and decision modeling can support community resilience across these two domains. I use spatial analysis, machine learning, optimization and matching theory to develop practical decision-support workflows. Chapter 2 proposes a hybrid convolutional neural network and optimization framework for flood adaptation planning in public transit systems, estimating flood susceptibility and converting stop-level risk into budget-feasible intervention portfolios. Chapter 3 assesses flood-related transit disruptions and older adults’ access to senior-serving facilities in Boston using flood hazard data, GTFS-based transit analysis, vulnerability measures and multi-attribute decision analysis. Chapter 4 develops a matching decision-aid framework for Home Match, a Bay Area shared housing program. Using historical match and survey data, it evaluates affordability rules, match outcomes, service exchange and shortlist-based recommendation policies that support staff judgment without replacing the program’s human-centered process. Together, these chapters show how analytic methods can be translated into decision workflows for community-based problems. The dissertation contributes by connecting GIS, analytics, optimization and matching models to practical decisions that affect disadvantaged communities.

Comments

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