Differentiating pathways of neighborhood change in 50 U.S. metropolitan areas

Abstract

Rapid transformations sweeping the United States over the past 50 years have necessitated a reassessment of longstanding theories on how the neighborhood change process has unfolded. This article builds upon recent methodological advancements aimed at understanding longitudinal dynamics by developing a workflow that blends the self-organizing map and a sequential alignment method to visualize pathways of change in a multivariate context. It identifies the predominant pathways in which neighborhoods have changed according to their racial, ethnic, socioeconomic and housing characteristics in the largest US metropolitan statistical areas from 1980 to 2010. The distribution of these pathways is subsequently examined between metropolitan statistical areas and the spatial clustering of these trajectories within cities is analyzed. Results reveal a white-flight type process, the establishment of a multiethnic neighborhood, densification of single-family neighborhoods, gentrification in relatively diverse neighborhoods, upgrading of white single family neighborhoods, and the most frequent pathway of all: no change. High-poverty minority and wealthy white neighborhoods are most spatially compact and expanding in a contiguous manner, while multiethnic neighborhoods are relatively dispersed. Six groups of metropolitan statistical areas are identified based upon the similarity of their neighborhood composition. Parallels are drawn between the formation of enduring high-poverty black neighborhoods in Northern and Midwestern cities and the emergence of clusters high-poverty Hispanic neighborhoods in Hispanic destination cities.

Structural and Exchange Components in Processes of Neighbourhood Change: A Social Mobility Approach

“…Rosenthal's study (2008), indicated that a third of all neighbourhoods remained in the same income quartile over a period of 50 years. Delmelle (2017) rather identified stability as the most frequent pathway among US metropolitan neighbourhoods, but this finding relies on a different definition of stability. Some urban features explain persistence in neighbourhood relative status.…”

“…Measures that are based on computing the ratio of neighbourhood average income to the average for all neighbourhoods in the respective metropolitan area (e.g. Fogarty 1977;Gould Ellen and O'Regan 2008;Logan and Schneider 1981;Rosenthal 2008;Rosenthal and Ross 2015), and to a lesser extent also those that are based on standardized scores (Delmelle 2015(Delmelle , 2017 do in fact capture the 'inequality' effect and therefore confound it with the 'exchange' effect. This can lead to the inconsistency of research designs with theoretical models, because the effect of changing inequality on neighbourhoods is incorporated in the total observed change which is attributed to urban-level processes.…”

The Increasing Sociospatial Fragmentation of Urban America

“…In the second part, I analyze the spatial clustering of each neighborhood type in each city. This follows the analysis of Delmelle [22] who analyzed the spatial clustering of these neighborhood types for all 50 MSAs grouped together. In this analysis, I segment out these results by MSA to determine which social groups are most spatially compact and how these patterns compare across cities and through time.…”

Spatio-temporal analysis of socioeconomic neighborhoods: The Open Source Longitudinal Neighborhood Analysis Package (OSLNAP)

“…Choosing which aspect or aspects to focus on should be driven by the research question at hand and the interpretation should proceed with caution [SR16]. A major approach of sequence analysis, the optimal matching (OM) algorithm, which was originally used for matching protein and DNA sequences [AT00], has been adopted to measure the similarity between neighborhood sequences in metropolitan areas such as Los Angeles and Chicago [Del16], [Del17]. It generally works by finding the minimum cost for transforming one sequence to another using a combination of operations including substitution, insertion, deletion and transposition.…”