This article addresses the bidirectional relationship between residential micro-segregation, in the form of built barriers to mobility, and social capital. I engage with two bodies of the literature. On the one hand, I critique a widespread top-down model of residential segregation. This model suggests that higher-status groups drive segregation through direct (e.g., secluded neighbourhoods) and indirect (e.g., by funnelling housing demand) measures. On the other hand, I provide evidence of the bounding effects of segregation on social capital. While some scholars suggest residential homogeneity favours social capital, others argue that benefits occur within privileged neighbourhoods. The effects of segregation on social capital are less clear at lower scales and in highly unequal Global South cities. My argument is twofold. First, I uncover the dynamics of segregation below the neighbourhood scale. I use the notion of horizontal micro-segregation to identify the social and spatial conditions associated with a higher concentration of street-level segregating infrastructure. My methodological approach draws on data for all residential blocks in Lima, Peru (N = 99,685). I find that suburban-inspired urban design is positively associated with micro-segregating infrastructure, upon controlling for other factors such as socioeconomic status, density, and urbanization age of each block. Second, I provide evidence of the bounding effects of segregation on social capital. Using ten waves of the Lima Cómo Vamos survey (2010–2019), I show that micro-segregating infrastructure is associated with higher trust in neighbours and lower civic engagement. These findings indicate that exposure to segregation affects social capital within and across secluded neighbourhoods throughout the socioeconomic spectrum.

Cities have established official neighborhood boundaries for targeted social policy in recent decades. The authors propose that a sociological conception of neighborhoods sensitizes us to the potential consequences of imposing categorical divisions onto a largely continuous urban space. The authors specify this idea in three steps. First, they argue that designations affect people’s behavior toward target neighborhoods. Second, the heterogeneity within official boundaries may lead to informational distortion; disadvantaged areas are denied benefits solely because of location. Third, designations may generate negative reputations for targeted areas or extend existing stigma to new areas. To examine these processes, the authors study Toronto’s Priority Area Program (2006–2013). Difference-in-difference models show significant negative effects of the designation on rent, home value, and building permits. The authors provide evidence of informational distortion through income distribution analysis. An analysis of policy documents, newspaper reports, and secondary literature illustrates the stigmatizing aspects that local community members and observers interpreted about the designation.

This article applies a method we term “predictive clustering” to cluster neighborhoods. Much of the literature in this direction is based on groupings built using intrinsic characteristics of each observation. Our approach departs from this framework by delineating clusters based on how the neighborhood’s features respond to a particular outcome of interest (e.g., income change). To do so, we leverage a classification and regression via integer optimization (CRIO) method that groups neighborhoods according to their predictive characteristics and consistently outperforms traditional clustering methods along several metrics. The CRIO methodology contributes a novel methodological and conceptual capability to the literature on neighborhood dynamics that can provide useful insights for policymaking.

This paper develops novel methods for using Yelp reviews as a window into the collective representations of a city and its neighbourhoods. Basing analysis on social media data such as Yelp is a challenging task because review data is highly sparse and direct analysis may fail to uncover hidden trends. To this end, we propose a deep autoencoder approach for embedding the language of neighbourhood-based business reviews into a reduced dimensional space that facilitates similarity comparison of neighbourhoods and their change over time. Our model improves performance in distinguishing real and fake neighbourhood descriptions derived from real reviews, increasing performance in the task from an average accuracy of 0.46 to 0.77. This improvement in performance indicates that this novel application of embedded language analysis permits us to uncover comparative trends in neighbourhood change through the lens of their venues' reviews, providing a computational methodology for reading a city through its neighbourhoods. The resulting toolkit makes it possible to examine a city's current sociological trends in terms of its neighbourhoods' collective identities.

Populism is often viewed as a national-level phenomenon that pits a declining periphery against a cosmopolitan, economically successful metropolis. Our analysis of Rob Ford’s 2010 campaign and mayoralty in Toronto reveals the potential for the emergence of populist politics within the metropolis. To comprehend his appeal, principally within the city’s ethnically diverse postwar peripheral areas, we apply Brubaker’s conceptualization of populism as a discursive repertoire. Drawing on qualitative information and analysis of survey research, we first describe how Ford constructed electorally salient protagonists and antagonists. Second, we discuss how his emergence was enabled by institutional, economic, and demographic change. Finally, we explain Ford’s appeal to a diverse electorate in terms of the sincerity and coherence of his performance as the collective representation of suburban grievance. We conclude by arguing that populism may emerge in metropolitan settings with strong, spatially manifest internal social, economic, and cultural divisions.