Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Master of Science

Program

Geography

Supervisor

Wang, Jinfei

2nd Supervisor

Mok, Diana

Co-Supervisor

Abstract

Major Canadian cities have experienced rapid sprawl in the last 30 years. This dissertation presents two studies that empirically examine the causes of urban sprawl, merging census socioeconomics data and satellite imageries of 11 major Census Metropolitan Areas (CMAs). The monocentric city model and the Tiebout model are the main traditional theories explaining urban boundary changes and mobility residential. The first study focuses on the cross-sectional comparison among the 11 CMAs in 2016. In the second study, we zoom into the Toronto CMA and examine the longitudinal changes in its urban coverage at the fringe. We detect land cover/use changes of the Toronto CMA in 1986-2016. In both studies, we insert the role of price risk in understanding the timing of urban development. In doing so, both studies aim to contribute to the literature by broadening the traditional theories to include the role of risk in influencing urban development.

Summary for Lay Audience

Major Canadian cities have experienced rapid sprawl in the last 30 years. This dissertation presents two studies that empirically examine the causes of urban sprawl, merging census socioeconomics data and satellite imageries of 11 major Census Metropolitan Areas (CMAs) in 1986, 2006 and 2016. The monocentric city model and the Tiebout model, are the main traditional theories explaining urban boundary changes and mobility residential. The first empirical study focuses on the cross-sectional comparison among the 11 CMAs and attempts to study the role of price risk in influencing the extent of urban coverage expansion outside of the cities covered by the CMA boundaries. In the second study, we zoom into the largest CMA in Canada, Toronto, and examine the longitudinal changes in its urban coverage at the fringe. We detect the land cover/use changes of the Toronto CMA for 1986-2006 and 2006-2016. The 1986, 2006 and 2016 satellite imageries are matched with residents’ socioeconomic data from the corresponding census, forming a panel data set, based on Dissemination Areas, for the Toronto CMA. Similar to the first study, we insert the role of price risk in understanding the timing of urban development. In doing so, both studies aim to contribute to the literature by broadening the traditional theories to include the role of risk in influencing urban development.

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