A comparison of the approaches for gentrification identification

Liu, Cheng; Deng, Yu; Song, Weiye; Wu, Qiyan; Gong, Jian

doi:10.1016/j.cities.2019.102482

Cited by 15 publications

(12 citation statements)

References 84 publications

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“…It is essential to understand that deep learning is not the perfect answer, and there are mainly two issues for deep learning in gentrification. First of all, the lack of training samples is probably the most significant and complicated issue [172,192]. Unlike GSV data, which receive help on sample labeling from millions of internet users [95,99], remote sensing labeling is not only labor-intensive but also requires knowledgeable users for quality labeling [99,172,182].…”

Section: Modeling Gentrification Using Deep Learning and Time-series Remote Sensingmentioning

confidence: 99%

“…Although deep learning sounds promising in remote sensing research and could yield great results, there are some limitations for deep learning in remote sensing. The limited training sample is one of the critical concerns for many researchers [172,192]. When there are no sufficient training samples or the quality of feature labels is low, the model will perform poorly in learning and generalizing features [87].…”

Section: Limitations Of Current Gentrification Mappingmentioning

confidence: 99%

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Remote Sensing of Urban Poverty and Gentrification

Lin

Zhang

et al. 2021

Remote Sensing

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In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.

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Section: Modeling Gentrification Using Deep Learning and Time-series Remote Sensingmentioning

confidence: 99%

Section: Limitations Of Current Gentrification Mappingmentioning

confidence: 99%

Remote Sensing of Urban Poverty and Gentrification

Lin

Zhang

et al. 2021

Remote Sensing

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“…Gentrification researchers have also expanded the concept, applying it to a wide range of different geographies – in search of the ‘geography of gentrification’ (Liu et al, 2019; López-Morales et al, 2016). This has led to the emergence of concepts such as super gentrification, rural gentrification, ‘studentification’, ‘residentialisation’, ‘reurbanisation’ and so forth (Maloutas, 2012).…”

Section: Literature and Framework On Gentrificationmentioning

confidence: 99%

“…As the concept of gentrification travels globally (Maloutas, 2018; Valle, 2020), this phenomenon, understood as a change in the population of land-users such that new users are of a higher socioeconomic status than the previous ones (Furman Center, 2015; Liu et al, 2019), has attracted widespread attention. The concept of gentrification is travelling at the same pace that some political economies and forms of urbanism are moving across the world (Betancur, 2014; Peck et al, 2009; Peck and Theodore, 2015; Smith, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Empirical studies on gentrification with significant samples and large-scale field survey, which are time-consuming and labour-intensive, are scarce, particularly for the Global South and East (Janoschka et al, 2014; Liu, 2019; Mendoza, 2016). As recent studies have noted that gentrification occurs differently in different cities (Betancur, 2014; Lees et al, 2016; Valle, 2020), we adopted a grounded, mixed methodology approach, aiming to identify that similar policy intervention can produce different results in different political economies.…”

Section: Introductionmentioning

confidence: 99%

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Can economic development policy trigger gentrification? Assessing and anatomising the mechanisms of state-led gentrification

et al. 2021

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This paper aims to bridge universality – as gentrification stands as a global threat to vulnerable communities – and local circumstances and geographies, by investigating structural factors, such as deindustrialisation and land rent gaps, as well as local political economies and socio-spatial structures, which are all common in the Global South. We conducted research in Belo Horizonte, Brazil, to investigate whether a neoliberal economic development policy acted as a trigger for gentrification, relying on mixed methods research to analyse evidence of economic restructuring, land rent creation, changes in resident profiles and major urban development trends in the region. Findings indicate evidence of economic restructuring and that the policy triggered higher land values. However, we did not observe evidence of gentrification in the area and attribute this to a still-relevant manufacturing sector, the extensive presence of large informal settlements, the growing numbers of suburban gated communities, the low proportion of renters, and the fact that local elites are moving southwards while the policy took place in the northern peripheries of the metropolis. Federal policies such as minimum wage increases and housing programs partially contradicted neoliberal state policies. This case study offers a lens to investigate gentrification in different latitudes and illustrates how social policies may prevent gentrification processes.

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The spatiotemporal socio-demography of the Tokyo capital region: a data-driven explorative approach

Tateishi

2023

Rev Reg Res

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In the coming decades, most of Asia’s population will reside in megacities, vast urban regions accommodating 10–30 million people. However, Asian megacities will be at the same time situated in the countries whose national population is projected to decline rapidly in the coming decades. Hence, for scholars and policymakers of Asian countries, understanding how the socio-demography of mature, post-growth, megacities will evolve within space and time is crucial to envision long-term and effective spatial governance. Prior studies have shown that varied migration patterns among socio-demographic groups lead to synchronized re-urbanization, post-suburbanization, and urban shrinkage in mature city regions. However, existing studies have limitations: they often exclude large Asian megacities, lack micro-scale analyses, and use predefined spatial typologies/divisions that obscure detailed patterns. To address these research gaps, this study investigated sub-municipal spatiotemporal patterns in Tokyo, the largest Asian megacity, using micro-scale job-household data and unsupervised machine learning clustering. The study revealed that Tokyo, like Euro-American cities, has experienced regional synchronization of (re)urbanization and (post)suburbanization within a complex landscape of shrinkage. However, the synchronized sub/urban growth is not uniform across localities within Tokyo. Complex migration flows seem to create disparities in demographic growth and decline, emphasizing the need for collaborative governance among localities within a megacity. The study contributes to a wider audience who are interested not only in the evolution of cities but also in an emerging application of machine learning to quantitative urban analyses.

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A comparison of the approaches for gentrification identification

Cited by 15 publications

References 84 publications

Remote Sensing of Urban Poverty and Gentrification

Remote Sensing of Urban Poverty and Gentrification

Can economic development policy trigger gentrification? Assessing and anatomising the mechanisms of state-led gentrification

The spatiotemporal socio-demography of the Tokyo capital region: a data-driven explorative approach

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