Authors: Yuchen Li*, Eastern Michigan University
Topics: Urban Geography, Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: urban typology, neighborhood change, weighted minimum edit distance, sequential pattern analysis, Detroit
Session Type: Paper
Start / End Time: 3:20 PM / 5:00 PM
Room: Napoleon C2, Sheraton 3rd Floor
Presentation File: No File Uploaded
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 delve into 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 ANOVA, 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 40 years. This model successfully provided a thorough analysis of the neighborhood typologies and exhibited much enhanced performance in identifying long-term trajectories of urban evolution.