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.

随着多伦多的消费市场和人口的增长，大多伦多地区（GTA）的财富每年继续增长。本文使用基于自组织地图的机器学习细分，研究了GTA消费者的人口统计学，社会经济学和支出消费模式。结果表明，SOM可能有助于建立有效的空间定界工具，从而增强多伦多市集群的空间格局。与城市区域的关系显示出区域邻里的特征，那里存在财富的积累，指出了人口普查地区与财富的地理分布之间惊人的空间形态划分。从这个意义上讲，关于区域和城市栖息地，SOM将自己定位为有前途的工具，可以用来在人口密度很高的高密度城市核心地区衡量财富。尽管见证了快速城市化和人口增长的城市（例如多伦多）可能会受益于使用机器学习和空间分析来监控区域和城市差异的集成方法。