Neighborhood Quality-of-Life Dynamics and the Great Recession: The Case of Charlotte, North Carolina

Delmelle, Elizabeth C.; Thill, Jean‐Claude

doi:10.1068/a46161

Cited by 25 publications

(12 citation statements)

References 34 publications

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“…Second, while, the study highlighted that the social nature of neighbourhoods can change over relatively short-to-moderate time periods, it also highlighted that many neighbourhoods do not change in character; a propensity that has been previously noted of social fragmentation (Grigoroglou et al 2019), as well as of other traits for example neighbourhood quality of life (Delmelle and Thill 2014). However, as Galster et al (2007, p.179) remarks, this observation does not imply "that neighbourhoods have so much inertia that they cannot be altered significantly from their stable state."…”

Section: Discussionmentioning

confidence: 82%

“…In the study byDelmelle and Thill (2014) neighbourhoods were defined on the basis of Neighbourhood Statistical Areas. Moreover, neighbourhoods were assigned to a quality of life class based on a composite index composed of 17-indicators.5 In the study by Weden et al (2011) neighbourhoods were measured using a set of some 32-indicators capturing features of the: built environment, migration and commuting behaviours, socioeconomic composition, and demographic and household composition.…”

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confidence: 99%

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A Longitudinal Typology of Neighbourhood-level Social Fragmentation: A Finite Mixture Model Approach

Lekkas¹,

Howard²,

Stankov³

et al. 2019

Preprint

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Neighbourhoods are social enclaves. And, from an epidemiological vantage there is substantive research examining how social traits of neighbourhoods affect health. However, this research has often focused on the effects of social deprivation. Less attention has been given to social fragmentation (SF), a construct aligned with the notions of lesser: social cohesion, social capital, collective functioning, and social isolation. Concurrently, there has been limited research that has described the spatial and temporal patterning of neighbourhood-level social traits. With a focus on SF the main aims of this paper were to model and describe the time-varying and spatial nature of SF.Conceptually, this research was informed by ‘thinking in time’ and by the ‘lifecourse-of-place’ perspective. While, from an analytical perspective, a longitudinal (3-time points over 10-years) neighbourhood database was created for the metropolitan region of Adelaide, Australia. Latent Transition Analysis was then used to model the developmental profile of SF where neighbourhoods were proxied by ‘suburbs’, and the measurement model for SF was formed of 9-conceptually related census-based indicators. A four-class, nominal-level latent status model of SF was identified: class-A=low SF; class-B=mixed-level SF/inner urban; class-C=mixed-level SF/peri-urban; and class-D=high SF. Class-A and -D neighbourhoods were the most prevalent at all time points. And, while certain neighbourhoods were inferred to have changed their SF class across time, most neighbourhoods were characterised by intransience.

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Section: Discussionmentioning

confidence: 82%

mentioning

confidence: 99%

A Longitudinal Typology of Neighbourhood-level Social Fragmentation: A Finite Mixture Model Approach

Lekkas¹,

Howard²,

Stankov³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, a common thread in current research involves efforts to spatialize time and temporalize space, either by mapping temporal processes or by pursuing more formal techniques such as spatial Markov analysis [40] for examining how temporal change shifts across geographic contexts. In fact, geographic contexts have proven to be of significant importance in helping to understand neighbourhood dynamics [41,42]. In particular, Delmelle et al [41] investigated whether a neighbourhood's geographic situation impacts its probability of improvement and decline concerning its quality of life profile.…”

Section: Socio-spatial Neighbourhood Change Researchmentioning

confidence: 99%

A Markov model of urban evolution: Neighbourhood change as a complex process

Silver

Silva

2020

Preprint

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This paper seeks to advance neighbourhood change research and complexity theories of cities by developing and exploring a Markov model of socio-spatial neighbourhood evolution in Toronto, Canada. First, we classify Toronto neighbourhoods into distinct groups using established geodemographic segmentation techniques, a relatively novel application in this setting. Extending previous studies, we pursue a hierarchical approach to classifying neighbourhoods that situates many neighbourhood types within the city’s broader structure. Our hierarchical approach is able to incorporate a richer set of types than most past research and allows us to study how neighbourhoods' positions within this hierarchy shape their trajectories of change. Second, we use Markov models to identify generative processes that produce patterns of change in the city’s distribution of neighbourhood types. Moreover, we add a spatial component to the Markov process to uncover the extent to which change in one type of neighbourhood depends on the character of nearby neighbourhoods. In contrast to the few studies that have explored Markov models in this research tradition, we validate the model's predictive power. Third, we demonstrate how to use such models in theoretical scenarios considering the impact on the city’s predicted evolutionary trajectory when existing probabilities of neighbourhood transitions or distributions of neighbourhood types would hypothetically change. Markov models of transition patterns prove to be highly accurate in predicting the final distribution of neighbourhood types. Counterfactual scenarios empirically demonstrate urban complexity: small initial changes reverberate throughout the system, and unfold differently depending on their initial geographic distribution. These scenarios show the value of complexity as a framework for interpreting data and guiding scenario-based planning exercises.

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“…One vibrant strand of work is found in analyzing the sequences using the optimal matching algorithm (Abbott, 1995;Gauthier et al, 2010). In these analyses, an initial geodemographic analysis is adopted to segment underlying urban space into specific neighborhood classes (Mikelbank, 2011;Delmelle and Thill, 2014;Delmelle, 2015Delmelle, , 2016Ling and Delmelle, 2016;Delmelle, 2017). Then, the historical experience of an urban area can be examined by summarizing the sequences of the identified neighborhood classifications it experiences.…”

Section: Dynamics Of Urban Spacesmentioning

confidence: 99%

The Dynamics of Urban Neighborhoods: A Survey of Approaches for Modeling Socio-Spatial Structure

Knaap¹,

Wolf²,

Rey³

et al. 2019

Preprint

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For close to a century, researchers from across the disciplines of Urban Studies have developed empirical models for understanding the spatial extent and social composition of urban neighborhoods--and how these dimensions change over time. Unfortunately, however, these techniques have often been developed within disciplinary silos and without broad exposure to other potentially interested constituencies. In this paper, we traverse the literatures of social science, computer science, and statistics to examine a variety of modeling techniques for understanding neighborhood dynamics. We begin our review by examining early concepts of spatial structure first outlined in the Chicago School and discuss how the notions of social ecology and quantitative neighborhood analysis permeated the urban studies for several decades to come. Our survey continues by reviewing contemporary statistical approaches for identifying urban neighborhoods, culminating with the state of the art in subfields known as `geodemographics' and `regionalization'. Following this review, we offer insight into the field's persistent conceptual issues, identify areas ripe for additional research, and highlight newly-developed computational methods that can inform more just and socially equitable public policy, community development, and accountable governance.

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Neighborhood Quality-of-Life Dynamics and the Great Recession: The Case of Charlotte, North Carolina

Cited by 25 publications

References 34 publications

A Longitudinal Typology of Neighbourhood-level Social Fragmentation: A Finite Mixture Model Approach

A Longitudinal Typology of Neighbourhood-level Social Fragmentation: A Finite Mixture Model Approach

A Markov model of urban evolution: Neighbourhood change as a complex process

The Dynamics of Urban Neighborhoods: A Survey of Approaches for Modeling Socio-Spatial Structure

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