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Bridging geographic information science and population health intervention research: Quantifying equity, representation, and spatial patterns of change in bicycle ridership using crowdsourced data

Resource type
Thesis type
(Thesis) Ph.D.
Date created
2023-06-19
Authors/Contributors
Abstract
Increasing bicycling for transportation is a priority on healthy city policy agendas given its potential to improve health outcomes and advance sustainability goals. Safety concerns and inadequate infrastructure limit bicycling uptake in Canadian cities, and there are inequities in who bikes. Realizing the societal benefits of bicycling requires substantial investments in high-comfort infrastructure, and interventions need to be implemented in ways that support equitable access. Bicycle count data are needed to evaluate interventions but are sparse due to limitations of city count programs. Crowdsourced data are a promising supplement to conventional bicycle count data but biases in who the data represent must be understood to avoid reinforcing inequities or creating new ones. Equitable implementation of bicycle infrastructure can also be supported by Population Health Intervention Research, broadly defined as research that focuses on policies, programs, and infrastructure that could improve population health by addressing underlying socioeconomic and environmental conditions of risk and inequities. In this thesis I apply Geographic Information Science – an approach that encompasses research on and with geographic information and Geographic Information Systems – to intervention research on bicycling in Canadian cities (Victoria, Kelowna, and Vancouver, BC, and Halifax, NS). I evaluate representation in crowdsourced bicycle ridership data and contribute knowledge on infrastructure interventions and change in spatial patterns of bicycle ridership over 2019 – 2020. The chapters in this thesis address knowledge gaps on: (i) spatial biases in crowdsourced bicycle ridership data, (ii) demographic representation in crowdsourced bicycle ridership data, (iii) COVID-19 street reallocation interventions, and (iv) spatial patterns of change in bicycle ridership during the first wave of COVID-19. In this dissertation I contribute guidance on representation and use of crowdsourced bicycling data to evaluate spatial patterns of change in bicycling, spatial methods for quantifying distributional equity in the implementation of bicycling infrastructure interventions, and knowledge on the impacts of COVID-19 on bicycling in Canadian cities. Future research should extend to understand how planners and advocates are using crowdsourced ridership data in planning and evaluating infrastructure interventions, expand knowledge on upstream factors contributing to inequities in bicycling, and the longer-term impacts of COVID-19 on bicycling should be monitored.
Document
Extent
122 pages.
Identifier
etd22599
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Winters, Meghan
Language
English
Member of collection
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etd22599.pdf 5.05 MB

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