Predicting Urban Growth of the Greater Toronto Area - Coupling a Markov Cellular Automata with Document Meta-Analysis

Abstract

Toronto’s Census Metropolitan Area (CMA) has faced on-going challenges concerning its demographic shifts in the urban and rural fringe tending to become a megacity over the coming decades, due to rapid population increase and urban amalgamation. For this research we examine past urban land use transitions in Toronto’s CMA based on collected remote sensing data between 1973 and 2010. A Markov Cellular Automata approach is used deriving the CMA urban future based on the existing and planned strategies for Ontario. This is done by a combination of multi-criteria evaluation processes originating transition probabilities that allow a better understanding of the regions urban future by 2030. While the transition probabilities are incorporated from the traditional Markov Chain process, the variables for suitability are measured through a text mining approach, by incorporating several planning documents. The result offers a more integrative vision of policymaker’s preference of future planning instruments, allowing for the creation of a better integration of propensity of future growth indicators. The northern part of Toronto is expected to register continuous growth in the coming decades, while agricultural land will continue to decrease. Urban areas after 2020 tend to become more clustered suggesting an importance of planning of green spaces within the Toronto.

Keywords: urban growth, cellular automata, text mining, Greater Toronto Area, Toronto, multi-criteria evaluation