Date of Award
Fall 11-21-2025
Document Type
Masters Capstone Project
Degree Name
M.S. in Geographic Information Sciences
Organizational Unit
College of Natural Science and Mathematics, Geography and the Environment
First Advisor
Steven Hick
Second Advisor
Keith Ratner
Copyright Statement / License for Reuse
All Rights Reserved.
Keywords
Geographic information science, Linear regression, Poisson regression, Public transportation, Subway, Accessibility, Americans with Disabilities Act (ADA)
Abstract
This research analyzes the spatiotemporal relationship between explanatory variables and the frequency of downed elevator/escalator alerts at train stations on the Massachusetts Bay Transportation Authority’s (MBTA) Orange and Red lines. The data is from 2023. The null hypothesis is that there is no spatiotemporal pattern to the frequency/distribution of alerts. Generalized Linear Poisson Regression is used to model relationships across four time periods – the entire 2023 calendar year, Spring (January-April), Summer (May-August), and Fall (September-December). Results indicate that the null hypothesis can be rejected for all variables in Summer and Fall models but cannot be rejected for all variables in entire year and Spring models. Residual and Hot Spot analysis suggest that while technically significant, real-world effects of variable relationships (such as age and/or ridership levels impacting alert frequency) are miniscule.
Copyright Date
11-1-2025
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Maura Forde
Provenance
Received from Author
File Format
application/pdf
Language
English (eng)
Extent
69 pgs
File Size
10.4 MB
Recommended Citation
Forde, Maura, "Mobility Disrupted: Geospatial Analysis of Accessibility on the MBTA’s Orange and Red Lines Using Generalized Linear Poisson Regression" (2025). Geography and the Environment: Graduate Student Capstones. 95.
https://digitalcommons.du.edu/geog_ms_capstone/95