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作者

Eric Vaz;Fernando Bação;Bruno Damásio;Malik Haynes;Elissa Penfound

来源

HABITAT INTERNATIONAL,Vol.108,P.102319

语言

英文

关键字

GIS;Regional analysis;Spatial analysis;Wealth habitats;Regional disparities;Toronto

作者单位

Ryerson University Department of Geography and Environmental Studies, Toronto, ON, Canada;NOVA University Lisbon NOVA Information Management School, Lisbon, Portugal;Ryerson UniversityYeates School of Graduate Studies, Toronto, ON, Canada;Ryerson University Department of Geography and Environmental Studies, Toronto, ON, Canada;NOVA University Lisbon NOVA Information Management School, Lisbon, Portugal;Ryerson UniversityYeates School of Graduate Studies, Toronto, ON, Canada

摘要

Wealth in the Greater Toronto Area (GTA) continues to grow each year as Toronto's consumer market and population increase. Using a machine learning segmentation based on self-organizing maps, this paper examines the demographics, socioeconomics, and expenditure consumption patterns of the GTA's consumers. The results suggest that SOM may contribute to efficient spatial delimitation tools, enhancing the spatial patterns of clusters in the city of Toronto. The relation to urban areas displays locational neighbourhood characteristics, where the accumulation of wealth is present, pointing out a striking spatial-morphological division between census regions and geographical distribution of wealth in Toronto. In this sense, concerning regional and urban habitats, SOM position themselves as promising tools to measure wealth within highly dense urban cores with significant demographic diversity. While cities that have witnessed rapid urbanization and population growth, such as Toronto, may benefit from integrative methods that use machine learning and spatial analysis to monitor regional and urban disparities.