Full Citation
Title: Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach
Citation Type: Journal Article
Publication Year: 2022
ISBN:
ISSN: 0016-7363
DOI: 10.1111/gean.12298
NSFID:
PMCID:
PMID:
Abstract: Recent neighborhood studies have focused on longitudinal aspects of neighborhood change and data-mining methodologies that identify neighborhood trajectory patterns using time-series multivariate census data. Existing neighborhood trajectory models capture neighborhood change by stacking cross-sectional neighborhood clustering results across years and analyzing the discrete stepwise switching patterns between the clusters. Taking a different approach, we employ the functional data analysis (FDA) method to analyze longitudinal patterns of neighborhood change from mathematically represented multivariate time-dependent curves to identify neighborhood trajectory clusters. This FDA-based neighborhood trajectory model incorporates a multivariate functional principal component analysis and k-means clustering. We have applied our model to neighborhoods in the Charlotte and Detroit metropolitan areas to identify ongoing racial and socioeconomic segregation patterns and the time dynamics of neighborhood change.
Url: https://doi.org/10.1111/gean.12298
User Submitted?: No
Authors: Jung, Paul H.; Song, Jun
Periodical (Full): Geographical Analysis
Issue: 4
Volume: 54
Pages: 789-819
Data Collections: IPUMS USA, IPUMS NHGIS
Topics: Land Use/Urban Organization, Methodology and Data Collection
Countries: