Sensitivity of sequence methods in the study of neighborhood change in the United States. (May 2020)

• Record Type: Journal Article
• Authors: Kang, Wei; Rey, Sergio; Wolf, Levi; Knaap, Elijah; Han, Su
• Is Part Of: Computers, environment and urban systems. Volume 81(2020)
• Publication Date: 2020-05
• Subjects: Sequence analysis -- Neighborhood change -- Optimal matching -- Clustering -- Sensitivity -- Geodempraphics; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101480
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: There is a recent surge in research focused on urban transformations in the United States via empirical analysis of neighborhood sequences. The alignment-based sequence analysis methods have seen many applications in urban neighborhood change research. However, it is unclear to what extent these methods are robust in terms of producing consistent and converging neighborhood sequence typologies. This article sheds light on this issue by applying four sequence analysis methods to the same data set – 50 largest Metropolitan Statistical Areas (MSAs) of the United States from 1970 to 2010, and finds that these methods do not provide converging neighborhood sequence typologies, and their behavior varies across MSAs, thus prohibiting meaningful comparisons of similar studies. MSAs with higher average household income in 1970 tend to be less sensitive to the choice of the SA methods. In other words, when investigating neighborhood change in these MSAs, different SA methods tend to produce a more converging neighborhood sequence typology. Comparatively, for MSAs hosting neighborhoods which have experienced frequent changes during the period 1970–2010, they are less likely to produce similar typologies with different SA methods. In addition, there is a big difference in the neighborhood sequence typology between applying the classic SA methods with varying costs and using the SA variant focusing on the second-order sequence property. After comparing the behavior of these methods, we highlight one method ("OMecenter") which leverages the socioeconomic similarities of neighborhoods and suggest researchers consider it as the building block towards designing a meaningful sequence analysis method for neighborhood change research. Highlights: We address the robustness of sequence analysis (SA) methods in neighborhood change research. We apply five SA methods to 50 largest Metropolitan Statistical Areas (MSAs) of the United States from 1970 to 2010 and assess whether they lead to consistent and converging neighborhood sequence typologies. We demonstrate the neighborhood sequence typology is sensitive to the choice of SA method and the sensitivity demonstrates spatial heterogeneity.