Are new patterns of low‐income distribution emerging in Canadian metropolitan areas?

Abstract

Recent studies on urban poverty in Canadian cities suggest a growing spatial concentration of poor populations within metropolitan regions. This article assesses trends in the intra‐urban distribution of the poor population from 1986 to 2006 in eight of Canada's largest cities. We consider five well‐known dimensions of segregation, as identified by Massey and Denton (1988), in order to examine changes in the spatial distribution of poor populations within metropolitan areas: evenness, exposure, concentration, clustering, and centralization. These indices were calculated for low‐income populations at the census tract level using data from five Canadian censuses. Although each metropolitan area has distinctive characteristics, we were able to identify some general trends. The results suggest that, in 2006 compared to 1986, low‐income populations lived in more spatially concentrated areas, which were, at the same time, socioeconomically more homogeneous and more dispersed throughout the metropolitan area. In addition, we observed that over the last twenty years areas of poverty have been located, for the most part, in neighbourhoods adjacent to downtown cores. Nevertheless, we found that poverty has mostly increased in suburban areas located outside inner‐city neighbourhoods. Growing socioeconomic homogeneity and dispersion of low income areas in metropolitan areas reveal new spatial patterns of urban poverty distribution. These findings should be cause for concern as social isolation in the most disadvantaged neighbourhoods could affect the life chances and opportunities for the residents of those areas.

Suburbanization of transport poverty

“…This could be due to a number of factors; increases in post-secondary students, rising housing costs relative to income, or deprivation within social housing and older apartment buildings. Overall, these spatial patterns of changing SES corroborate results of neighborhood change analyses in other research papers (Ades et al, 2012;Breau et al, 2018;Hulchanski, 2010;Pavlic & Qian, 2014). For car ownership, we observe pockets of zero-car households growing within central and inner-suburban areas.…”

Sizing up transport poverty: A national scale accounting of low-income households suffering from inaccessibility in Canada, and what to do about it

“…In recent decades, Canada has witnessed a rise in socioeconomic inequalities, and concentrations of poverty, both at a regional level (Breau, 2015) and within cities (Hulchanski et al, 2010;Walks & Twigge-Molecey, 2013;Breau et al, 2018). Along with this growth in poverty, evidence has indicated that poverty distributions have become more suburbanized; increased costs of housing in city centres have pushed lower-income residents to more affordable, but less accessible areas (Ades et al, 2012(Ades et al, , 2016 Spinney et al (2009) showed there is significant association between transport mobility benefits and quality of life for elderly Canadians. Allen and Farber (2018) analyzed how low accessibility limits the on-campus participation of University students.…”

“…Toronto and Vancouver have the greatest percent of their DAs at high risk of transport poverty, and the smaller cities of Winnipeg and Quebec have the lowest (see Table 5). Toronto and Vancouver are also the two cities which have been reported on the most in terms of experiencing rising housing costs and sub-urbanization of poverty (Ades et al, 2012(Ades et al, , 2016. Lastly, we conduct a k-means cluster analysis of zones at risk of transport poverty in order to generate a typology that can be used for policy recommendations.…”

The cost of equity: Assessing transit accessibility and social disparity using total travel cost

“…To ascertain the social vulnerability of neighborhoods in the Montreal region, we used census tract-level data from Statistics Canada's 2011 NHS andCensus (Statistics Canada, 2011). With the assumption that socially disadvantaged groups are spatially concentrated (Ades, Apparicio, & Séguin, 2012), we derived an indicator from four equally-weighted variables to identify socially vulnerable neighborhoods at the CT-level. In addition to median household income, we also used percentage of recent immigrants (since 2006), the percentage of the workforce that is unemployed, and percentage of residents with education at the level of only a high school diploma (25-64 years old).…”