Mapping the DNA of Urban Neighborhoods: Clustering Longitudinal Sequences of Neighborhood Socioeconomic Change

Delmelle, Elizabeth C.

doi:10.1080/00045608.2015.1096188

Cited by 80 publications

(108 citation statements)

References 49 publications

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“…The current analyses were limited by the direction and degree of changes in disadvantage within Chicago neighborhoods between 1970 and 1990 (which underwent either no change or several degrees of increasing disadvantage). Future work should explore places and periods that experienced a wider variety of changes in disadvantage over time, and other methods of operationalizing neighborhood change (Delmelle 2016).…”

Section: Discussion a N D C O N C L U S I O Nmentioning

confidence: 99%

Within-Neighborhood Dynamics: Disadvantage, Collective Efficacy, and Homicide Rates in Chicago

Becker

2018

Social Problems

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Research on neighborhood structural conditions like concentrated disadvantage and crime largely focuses on between-neighborhood differences; for example, places with more disadvantage are expected to experience higher homicide rates. However, empirical research often does not consider within-neighborhood dynamics of structural stability and change. Furthermore, several recent studies have found cross-sectional associations between structural variables and crime outcomes can vary significantly across units, violating a key assumption of global modeling strategies. The current work explores if and how historical changes in disadvantage influence neighborhood collective efficacy and homicide rates, net of the level of disadvantage at a given time point. Collective efficacy theoretically mediates the relationship between conditions and crime, and is hypothesized to be the mechanism through which structural change influences homicide rates. It is also hypothesized that spatial variation in cross-sectional associations between disadvantage and social outcomes can be explained by accounting for within-neighborhood changes in disadvantage. Using a sample of Chicago neighborhoods and ordinary least squares and geographically weighted regression models, I find that within-neighborhood changes in disadvantage significantly predict neighborhood collective efficacy, though the effects of this change on homicide rates are not completely mediated by collective efficacy. Within-neighborhood change completely accounts for spatial variation in cross-sectional associations, offering one explanation of prior research findings. Within-neighborhood structural changes appear to disrupt collective efficacy and contribute to higher homicide rates than predicted by the level of disadvantage alone.

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Section: Discussion a N D C O N C L U S I O Nmentioning

confidence: 99%

Within-Neighborhood Dynamics: Disadvantage, Collective Efficacy, and Homicide Rates in Chicago

Becker

2018

Social Problems

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“…The methodology offers an alternative to methodologies that first classify neighbourhoods into a limited number of discrete groups and subsequently examines sequences or transitions between group membership (Delmelle 2016(Delmelle , 2015Wei and Knox 2014) and provides an extension to the increasingly popular technique of visually examining SOM trajectories to understand urban processes (Lee and Rinner 2015;Delmelle et al 2013;Arribas-Bel, Kourtit, and Nijkamp 2013). In respect to the first point, when examining transitions between discrete classes, change is reduced to an ordinal distance between classes, removing the magnitude change and masking whether or not change was dictated by a singular attribute in that group, or by changes according to all attribute dimensions.…”

Section: Discussionmentioning

confidence: 99%

“…The authors suggest that the trajectory clustering method implemented was exploratory in nature and other techniques for grouping these temporal trends should be investigated. Finally, in a more recent approach to categorizing multidimensional and temporal trajectories, Delmelle (2016) proposed the use of a sequential alignment method based on the Optimal Matching algorithm to classify sequences of neighbourhoods as they traversed through a discrete set of socioeconomic classes initially developed through a k-means clustering approach.…”

Section: Introductionmentioning

confidence: 99%

Classifying multidimensional trajectories of neighbourhood change: a self-organizing map andk-means approach

Ling

Delmelle

2016

Annals of GIS

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Understanding the dynamic nature of how urban neighbourhoods evolve through time has been a critical issue both in the literature and in public policy practice for decades. Methodological limitations in understanding change across the multiple attribute dimensions that define a neighbourhood, through time and for spatially situated units, have largely reduced empirical analyses to two points in time or for a singular attribute dimension. This paper demonstrates a two-layered approach to classifying neighbourhoods according to their multidimensional, temporal trajectories. The method first projects data onto a two-dimensional output space using a self-organizing map, then constructs temporal trajectories of change across this space and, finally, classifies the resulting trajectories with a k-means algorithm. The resulting typology of neighbourhood trajectories are then mapped in the geographic space to visualize the space-time, multidimensional dynamics. A case study of neighbourhood change from 1970 to 2010 in eight US cities demonstrates the effectiveness approach for grouping neighbourhoods according to the similarity of their temporal pathways of change. Ten processes are uncovered ranging from various forms of suburbanization to revitalization and a suburban densification trend in the newest, rapidly growing cities in the study. Finally, we calculated a join-count statistic to quantify the observed spatial pattern of each of these neighbourhood types in each city.

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“…It can also be applied to analyse longitudinal individual-level family, migration and career trajectories (Brzinsky-Fay 2007, Rowe et al 2017a. This method is also used on neighbourhood trajectory mining in the United States to identify patterns of socioeconomic change over a period of time (Delmelle 2016). The key component of sequence analysis method is the optimal matching analysis which is used to measure pairwise dissimilarities between sequences and identifies "types of sequence patterns" (Studer, Ritschard 2016).…”

Section: Methodsmentioning

confidence: 99%

Exploring long-term youth unemployment in Europe using sequence analysis: a reproducible notebook approach

Patias

2020

REGION

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Youth unemployment is a factor influencing European policy. The effects of financial crisis of 2008 in youth unemployment were prominent and varied across European countries and regions. This notebook aims to identify key representative trajectories of youth unemployment by NUTS 2 regions in Europe from 2008 to 2018. Moreover, this notebook provides a self-contained research workflow that is fully reproducible and transparent, using a wide range of functionalities offered by computational notebooks. The results show that there is a divide between northern and southern regions. Northern regions follow stable low youth unemployment levels, while southern regions follow stable high youth unemployment levels. Finally, the results show that there are patterns of regional inequalities between major metropolitan areas and their adjacent regions.

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Mapping the DNA of Urban Neighborhoods: Clustering Longitudinal Sequences of Neighborhood Socioeconomic Change

Cited by 80 publications

References 49 publications

Within-Neighborhood Dynamics: Disadvantage, Collective Efficacy, and Homicide Rates in Chicago

Within-Neighborhood Dynamics: Disadvantage, Collective Efficacy, and Homicide Rates in Chicago

Classifying multidimensional trajectories of neighbourhood change: a self-organizing map andk-means approach

Exploring long-term youth unemployment in Europe using sequence analysis: a reproducible notebook approach

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