Understanding urban gentrification through machine learning.
Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and urban change...
| Publicado en: | Urban Studies (Sage Publications, Ltd.) Vol. 56; no. 5; pp. 922 - 943 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications, Ltd.
Apr2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=135463066&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 135463066 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00420980 54XY jtl: Urban Studies (Sage Publications, Ltd.) issn: 00420980 maglogo: Y pubinfo: dt: Apr2019 vid: 56 iid: 5 pid: 2171 pub: Sage Publications, Ltd. artinfo: ui: 135463066 10.1177/0042098018789054 ppf: 922 ppct: 21 formats: tig: atl: Understanding urban gentrification through machine learning. aug: au: Reades, Jonathan De Souza, Jordan Hubbard, Phil affil: King's College London, UK su: London (England) Gentrification Urban renewal Neighborhood change Census Machine learning Random forest algorithms Social conditions in England sug: subj: Gentrification Urban renewal Neighborhood change Census London (England) Machine learning Random forest algorithms Social conditions in England keyword: census gentrification London machine learning neighbourhood change principal components quantitative geography random forests 主要组成部分 人口普查 伦敦 定量地理 机器学习 社区变化 绅士化 随机森林 census gentrification London machine learning neighbourhood change principal components quantitative geography random forests 主要组成部分 人口普查 伦敦 定量地理 机器学习 社区变化 绅士化 随机森林 ab: Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and urban change, this paper shows it is possible to analyse existing patterns and processes of neighbourhood change to identify areas likely to experience change in the future. This is evidenced through an analysis of socio-economic transition in London neighbourhoods (based on 2001 and 2011 Census variables) which is used to predict those areas most likely to demonstrate 'uplift' or 'decline' by 2021. The paper concludes with a discussion of the implications of such modelling for the understanding of gentrification processes, noting that if qualitative work on gentrification and neighbourhood change is to offer more than a rigorous post-mortem then intensive, qualitative case studies must be confronted with – and complemented by – predictions stemming from other, more extensive approaches. As a demonstration of the capabilities of machine learning, this paper underlines the continuing value of quantitative approaches in understanding complex urban processes such as gentrification. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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