The article discusses the application of Principal Component Analysis (PCA) to derive a score for ranking geographic areas based on socio-economic advantage and disadvantage using publicly accessible data in Australia. The process involves data standardization, PCA application, visualization of explained variance, and validation through comparison with a published Index of Economic Resource (IER). The demonstration successfully replicates the methodology used by the Australian Bureau of Statistics.
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Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area
Using Principal Component Analysis with Real-life data
Motivation
There is publicly accessible data describing socio-economic characteristics of geographic locations, such as income, occupation, education, employment, and housing. The goal is to derive a score that ranks geographic areas from most to least advantaged.
The Problem
The challenge is to understand which data points explain the most variations in order to derive a score based on a numerical combination of these data points.
The Data
Data points from the Australian Bureau of Statistics (ABS) include various socio-economic characteristics at the Statistical Area 1 (SA1) level, providing a granular digital boundary for analysis.
The Steps
The Python code demonstrates the process of deriving a socio-economic score using Principal Component Analysis (PCA) and validating the results against the Index of Economic Resource (IER) published by ABS.
The Validation
The derived scores are validated against the published IER scores to ensure accuracy and alignment with the ABS methodology.
Concluding Thought
Dimensionality reduction techniques such as PCA can effectively calibrate socio-economic scores and provide valuable insights for decision-making.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area
- Towards Data Science – Medium
- Twitter – @itinaicom
