Table 1 Results of principal component analysis.

From: Development and application of a WebGIS-based prediction system for multi-criteria decision analysis of porcine pasteurellosis

 

RC1

RC2

h2

u2

Railway

0.62

0.22

0.43

0.571

People

− 0.08

0.97

0.94

0.058

Temperature

0.85

− 0.17

0.75

0.251

Precipitation

0.80

− 0.08

0.64

0.362

Pig

0.88

− 0.05

0.77

0.228

Influence degree

0.94

− 0.08

0.89

0.108

Highway

0.91

− 0.13

0.84

0.156

Eigenvalue

4.22

1.05

  

Contribution Rate

0.60

0.15

  

Cumulative Rate

0.60

0.75

  

Proportion Explained

0.80

0.20

  
  1. RC: Load of principal component; h2: The explanation degree of principal component variance for each variable; u2: The proportion of variance that cannot be explained by principal components.
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