Investigating the Effects of Panethnicity in Geospatial Models of Segregation

Abstract

Social systems are inherently complex and can be represented using agent-based modelling (ABM) methods. Based on the innovative work of Thomas Schelling, ABMs are used to represent, analyze, and forecast emergent spatial-temporal dynamics of residential segregation. Segregation is modelled by representing the complex dynamics between individual agents with various socio-demographic profiles who self-organize into spatial clusters of alike individuals. Agents are typically classified into broad panethnic categories such as “Asian” or “Hispanic”, however these categories group together individuals from a very large number of countries that are ethnically and economically distinct and thus have diverse settlement patterns. Therefore, the objective of this study is to implement an ABM that simulates the spatio-temporal dynamics of segregation that emerge from interactions between incoming immigrants who are classified at two different levels of aggregation. At the aggregate level, ethnic groups are defined based on typical broad panethnic categories. At the disaggregate level, the “Asian” category is further disaggregated. The ABM is implemented to simulate processes leading to segregation in the City of Toronto and Metro Vancouver using actual geospatial and census data. The simulated spatial patterns of segregation are compared with actual census data that records the real settlement patterns of immigrants of various ethnicities. In addition, the degree of segregation is quantified and compared with the degree of segregation measured from the actual census data. Results show that both the spatial patterns of segregation and the measure of segregation are significantly influenced by the level of aggregation of the various ethnicities. The presented research has the potential to contribute to policy-planning and decision-making by assisting city planners and policymakers in mitigating persistent residential segregation.