Principal Components, Factor Analysis and Cluster Analysis and Application in Social Area Analysis

This chapter discusses three important multivariate statistical analysis methods: principal components analysis (PCA), factor analysis (FA) and cluster analysis (CA). PCA and FA are often used for data reduction by structuring many variables into a limited number of components and factors, respectiv...

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Bibliographic Details
Published inComputational Methods and GIS Applications in Social Science - Lab Manual pp. 146 - 156
Main Authors Liu, Lingbo, Wang, Fahui
Format Book Chapter
LanguageEnglish
Published United Kingdom CRC Press 2024
Taylor & Francis Group
Edition1
Subjects
Population Natural Growth Rate
PCA Dimension
Labor Participation Ratio
Hierarchical Cluster
Python Script
Distance Matrix
Input Ports
Kernel Density Estimation
Road Network Dataset
Top Input
Row Id
Output Data Port
CSV
DBSCAN
Health Informatics
Bottom Input
2SFCA Method
Taxi Trajectory
Multiple Nuclei Model
CSV File
PCA
Input Data Port
PCA Transformation
PCA Apply
Social Area Analysis
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Summary:This chapter discusses three important multivariate statistical analysis methods: principal components analysis (PCA), factor analysis (FA) and cluster analysis (CA). PCA and FA are often used for data reduction by structuring many variables into a limited number of components and factors, respectively. The techniques are particularly useful for eliminating variable collinearity and uncovering latent variables. While the PCA and FA group variables, the CA classifies observations into clusters according to their attributive homogeneity. In other words, given a data set as a table, the PCA and FA reduce the number of columns and the CA reduces the number of rows. A case study on social area analysis in Beijing is utilized to illustrate the application of all three methods.
ISBN:9781032302430
1032302437
DOI:10.1201/9781003304357-7