Date of Award

4-6-2021

Publication Type

Master Thesis

Degree Name

M.A.Sc.

Department

Civil and Environmental Engineering

First Advisor

Hanna Maoh

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

Since the 1960s, Integrated Urban Models (IUMs) have consistently been applied to simulate the future of cities. Technological advancement in recent years has opened the doors for sophisticated IUMs to be developed, ones requiring extreme computing power. The SMARTPLANS IUM is one example. While the development and application of SMARTPLANS exists in the literature, exploring potential improvements in the model’s predictive ability is lacking. This dissertation aims to fill the gap in the literature by focusing on two sub-modules of SMARTPLANS to test and ultimately advance their performance. The research conducted in this thesis explores the population mobility and land price submodules within the Land Use Module of SMARTPLANS. The models were estimated using relevant parameters, compared over time, and validated with Canadian census data. The results show that the population aged 24-35 is the primary influencing factor to impact population mobility in all study areas. Additionally, the number of detached dwellings and household income were found to positively impact house prices in all models. Further, the number of row houses and the distance from the central business district (CBD) negatively influenced prices. The estimated models for the two sub-modules suggest stable transferability over time in regions experiencing steady pace growth. Furthermore, the analysis confirms a strong spatial influence present in the data associated with both submodules. As such, the utilization of spatially oriented techniques, namely the Simultaneous Auto-Regressive (SAR) model, resulted in superior predictions when compared to the predictions obtained from Ordinary Least Squares (OLS) regression models. The implementation of SAR models within SMARTPLANS will therefore improve its predictive ability.

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