Case07-PCA FA CA and application in Social Area Analysis

Chapter 7 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).

This case study uses social area analysis to illustrate the application of all three methods. The interpretation of social area analysis results also leads us to a review and comparison of three classic models on urban structure, namely, the concentric zone model, the sector model and the multi-nuclei model. The analysis demonstrates how analytical statistical methods synthesize descriptive urban structure models into one framework. The project is based on a study reported in Gu et al. (2005). The study area was Beijing in 1998, composed of 107 subdistricts ( jiedao ).

The following data sets and Python file are provided under the data folder Beijing :

1. feature subdist.zip contains 107 urban subdistricts,

2. text file bjattr.csv has 14 socioeconomic variables ( X1 to X14 ) for social area analysis, and

3. python code Factor analysis.py for factor analysis.

Part 1: Principal Components Analysis (PCA)

Part 2: Cluster Analysis (CA)

Part 3: Detecting Urban Structure Models by Regression

Computational Methods and GIS Applications in Social Science - KNIME Lab Manual

Lingbo Liu, Fahui Wang

External resources

• Github for Geospatial Analytics
• Github for Workbook Issue Report