A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit.
This article develops an integrated methodology to investigate dominant trajectories of neighborhood change that are often confronted in urban studies. Currently, researchers are using k-means cluster analysis to establish diverse neighborhood typologies and principal component analysis (PCA) to ide...
| Publicado en: | Annals of the American Association of Geographers Vol. 108; no. 5; pp. 1313 - 1338 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Sep2018
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| 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=130970403&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 130970403 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: Sep2018 vid: 108 iid: 5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 130970403 10.1080/24694452.2018.1433016 ppf: 1313 ppct: 25 formats: tig: atl: A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit. aug: au: Li, Yuchen Xie, Yichun affil: Institute for Geospatial Research & Education, Eastern Michigan University su: Neighborhoods Psychological typologies Socioeconomic factors Urban growth Sequential pattern mining sug: subj: Neighborhoods Psychological typologies Socioeconomic factors Urban growth Land Subdivision Sequential pattern mining keyword: 加权最小编辑距离。 城市类型学 序列模式分析 底特律 邻里变迁 análisis secuencial de patrones cambio vecinal Detroit distancia de edición mínima ponderada neighborhood change sequential pattern analysis tipología urbana urban typology weighted minimum edit distance análisis secuencial de patrones cambio vecinal distancia de edición mínima ponderada tipología urbana 加权最小编辑距离。 城市类型学 序列模式分析 底特律 邻里变迁 análisis secuencial de patrones cambio vecinal Detroit distancia de edición mínima ponderada neighborhood change sequential pattern analysis tipología urbana urban typology weighted minimum edit distance análisis secuencial de patrones cambio vecinal distancia de edición mínima ponderada tipología urbana ab: This article develops an integrated methodology to investigate dominant trajectories of neighborhood change that are often confronted in urban studies. Currently, researchers are using k-means cluster analysis to establish diverse neighborhood typologies and principal component analysis (PCA) to identify socioeconomic interactions explaining the neighborhood typologies. Little attention has been given to longitudinal trajectories and dynamics of neighborhood evolution over a long period. Our new model adapts a newly developed dynamic sequential analysis (the weighted minimum edit distance algorithm) in big data analytics to sort and identify dominant trajectories of neighborhood change. Our model also innovatively synthesizes three statistical procedures—k-means, PCA, and analysis of variance—to derive the weight matrix, which naturally integrates the core characteristics of urban neighborhood changes into the sequential reordering. Using the census data in Metro Detroit over five census years (1970, 1980, 1990, 2000, and 2010), this model was tested to identify a unique city's demographic and socioeconomic transition pattern in the past forty years. This model successfully provided a thorough analysis of the neighborhood typologies and exhibited a much-enhanced performance in identifying long-term trajectories of urban evolution. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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