A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Li, Yuchen; Xie, Yichun
• Is Part Of: Annals of the American Association of Geographers. Volume 108:Issue 5(2018)
• Page Start: 1313
• Page End: 1337
• Publication Date: 2018-09-03
• Subjects: Detroit -- neighborhood change -- sequential pattern analysis -- urban typology -- weighted minimum edit distance; 底特律 -- 邻里变迁 -- 序列模式分析 -- 城市类型学 -- 加权最小编辑距离。; Detroit -- cambio vecinal -- análisis secuencial de patrones -- tipología urbana -- distancia de edición mínima ponderada; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: 10.1080/24694452.2018.1433016
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : 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.