Probabilistic Causal Data Modeling of Barriers to Accessibility for Persons with Disabilities in Canada

Zakir, Mouri

Probabilistic Causal Data Modeling of Barriers to Accessibility for Persons with Disabilities in Canada

dc.contributor.advisor	Yanushkevich, Svetlana
dc.contributor.author	Zakir, Mouri
dc.contributor.committeemember	Yanushkevich, Svetlana
dc.contributor.committeemember	Pauchard, Yves
dc.contributor.committeemember	Fast, Victoria Veronica
dc.date	2025-02
dc.date.accessioned	2024-12-03T17:27:10Z
dc.date.available	2024-12-03T17:27:10Z
dc.date.issued	2024-12-02
dc.description.abstract	This thesis addresses the pressing social issue of accessibility for persons with disabilities by creating AI models using the national survey data, with a methodological two step process. The contributions of this thesis include (a) a causal reasoning framework aiming to provide an understanding of the prevalence of barriers and challenges faced by persons with disabilities when accessing federal services and facilities; and (b) network-based approach that utilizes empirical data to provide a holistic assessment of the causality among demographic features (e.g. age, gender, type of disability) and accessibility. The statistical method utilizes Structural Equation Modeling supported by Exploratory Factor Analysis. For causal probabilistic modeling, Bayesian Networks are employed as a straightforward and compact way to interpret knowledge representation. This causal reasoning approach analyzes the nature and frequency of encountering barriers based on data to understand the risk factors contributing to pressing accessibility issues. Furthermore, to evaluate the network performance and overcome data limitation, synthetic data generation techniques are applied to create and validate artificial data built on real-world knowledge. The proposed framework aims to provide reasoning to understand the prevalence of physical, social, communication or technological barriers encountered by persons with disabilities in their daily lives. This thesis contributes to identifying areas for prioritization in facilitating accessibility regulation and practices to build an inclusive society.
dc.identifier.citation	Zakir, M. (2024). Probabilistic causal data modeling of barriers to accessibility for persons with disabilities in Canada (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/120146
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Accessibility barriers
dc.subject	persons with disabilities
dc.subject	inclusive smart city
dc.subject	exploratory factor analysis
dc.subject	structural equation modeling
dc.subject	causal reasoning
dc.subject	Bayesian networks
dc.subject	statistical hypotheses testing
dc.subject	synthetic data generation
dc.subject.classification	Public and Social Welfare
dc.subject.classification	Education--Technology
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Statistics
dc.subject.classification	Social Structure and Development
dc.title	Probabilistic Causal Data Modeling of Barriers to Accessibility for Persons with Disabilities in Canada
dc.type	master thesis
thesis.degree.discipline	Engineering – Electrical & Computer
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.