Authors: Wei Kang*, University of California - Riverside
Topics: Spatial Analysis & Modeling, Urban Geography, Quantitative Methods
Keywords: Urban data science, Neighborhood change, Social sequence analysis
Session Type: Paper
Presentation File: No File Uploaded
Recently, Optimal Matching (OM) is increasingly used for analyzing urban neighborhood change. Originally developed for matching DNA sequences in biology, OM works by finding the minimum cost of transforming one sequence to completely match the other using operations like substitution, insertion, and deletion. The minimum cost is considered as the distance or dissimilarity between these two sequences. Having recognized the inherent difference in DNA and life course sequences, social scientists have proposed various variants of OM to better suit the life course study and to reveal different characteristics of life courses, like timing, sequencing, and duration.
The current application of OM to evaluate the distance/dissimilarity between neighborhood sequences has benefited a lot from its application to life courses. However, we argue that a neighborhood sequence is fundamentally different from a life course sequence. While the latter is formed by natural categorical life events, the former is a construct in that the neighborhood types are usually the outcome of clustering multiple socioeconomic characteristics.
We propose a variant of OM which considers neighborhood dynamics as an unfolding process and explicitly treats neighborhood sequences as a construct by utilizing the vital information of neighborhood differences in setting the costs of OM operations. In addition, we extend the definition of “neighborhood stability” based on the empirical transition rates between neighborhood types. We demonstrate the advantage of the proposed OM variant in uncovering the sequencing and stability structure in the neighborhood sequences via a case study of the Los Angeles metropolitan area 1970-2010.
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