A geospatial agent-based model of the spatial urban dynamics of immigrant population: A study of the island of Montreal, Canada

Abstract

Residential segregation into spatial neighborhoods and boroughs is a well-known spatial dynamic process that characterise complex urban environments. Existing models of segregation, including the pioneering Schelling ones, often do not consider all the factors that can contribute to this process. Segregation as well as aggregation emerges from local interactions among individuals, and is rooted in the complexity of social, economic and environmental interactions. The main objective of this study is to develop and implement a geospatial agent-based model to simulate the decision-making process of location of new household for incoming immigrant populations. Particularly this study aims to simulate and analyse the dynamics of the new immigrant populations arriving in the bilingual cities and boroughs of the island of Montreal. The model was implemented in NetLogo software, using real geospatial datasets. The obtained simulation results indicate realistic spatial patterns of spatial composition of the ethnographic fabric on the island of Montreal. The proposed model has the potential to be used as part of the city planning purposes.

Modeling the geospatial dynamics of residential segregation in three Canadian cities: An agent‐based approach

“…The five criteria are selected based on empirical evidence that points to uniting with family and friends, schooling, affordable housing, and environmental amenities or improved quality of life as the primary reasons that immigrants will choose a location to settle (Bourne, 1999; Perez et al, 2019; Yu, 2004). The cell size of 120 m is consistent with the code used in Perez et al (2019). This is primarily due to the typical square blocks (120 × 120 m) existing in the oldest North American cities, which also include those chosen for this research study.…”

“…Schelling's conceptual work has since been extended and implemented using ABMs that can be used to test the sensitivity of the model to changes in agent tolerance (Urselmans & Phelps, 2018; Zhang, 2004) and neighborhood types (Crooks, 2010; Laurie & Jaggi, 2003), explore motivations for segregation including status (Fossett, 2006), ethnicity (Benard & Willer, 2007; Fossett, 2006), and language (Perez et al, 2019), and add a variety of agent preferences including neighborhood characteristics (Chen, Irwin, Jayaprakash, & Warren, 2005; Jayaprakash, Warren, Irwin, & Chen, 2009; Zhang, 2004) into relocation decisions. The degree of segregation is often measured using five main indices, including evenness, exposure, concentration, centralization, and clustering (Massey & Denton, 1988).…”

“…This is useful for exploring hypotheses that focus on identifying the processes that drive patterns of segregation. However, studies show that the underlying urban structure has an impact on dynamics and patterns of segregation (Banos, 2012; Perez et al, 2019; Spielman & Harrison, 2014). Therefore, geospatial data representing the built urban environment have been integrated into models characterizing residential segregation in Yaffo, Tel Aviv (Benenson, Omer, & Hatna, 2002), Buffalo, NY (Yin, 2009), the regional area of Auckland, New Zealand (Manley et al, 2015), and Montreal, Canada (Perez et al, 2019).…”

From Low-Cost Flights to the Ballot Box: How Eastern European Migration Shaped Far-Right Voting in London

“…The role of transportation infrastructure is a recognized but neglected element in the literature on migration and mobility (Pooley 2017). Studies of different urban contexts find that location decisions are linked to access to public transport, on which especially new migrants tend to rely more than established residents (Chatman and Klein 2009;Perez et al 2019;Tsang and Rohr 2011), and that some migrants value access to airports (Maslova and King 2020). In this section, we examine the link between transportation and migration with high cross-national mobility.…”

Representing Complex Evolving Spatial Networks: Geographic Network Automata

“…Shelling's work has been extended to explore the effects of parameter adjustments including individual tolerance, neighborhood size, population structures, and utility functions driving segregation [40,41]. In other research studies, models are parameterized based on real data [42][43][44]. In many cases, segregation is modelled as networks [45][46][47][48], however, network representation and analysis of the spatial processes of segregation using real geospatial data has not been explored.…”