Table 1 The capacity dimension, information dimension, and the correlation dimension of the spatial diffusion pattern of COVID-19 in Chinese mainland (examples).

From: Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland

Date

Capacity dimension D0

R2

Information dimension D1

R2

Correlation dimension D2

R2

Jan 20

0.5369***

0.9068

0.1033**

0.8151

0.0423**

0.7211

(0.0861)

(0.0246)

(0.0132)

Jan 25

1.4399***

0.9918

0.9960***

0.9915

0.5729***

0.9900

(0.0656)

(0.0460)

(0.0288)

Jan 30

1.5321***

0.9940

0.9964***

0.9833

0.6052***

0.9732

(0.0595)

(0.0649)

(0.0502)

Feb 5

1.5589***

0.9950

0.8561***

0.9753

0.4671***

0.9584

(0.0551)

(0.0682)

(0.0486)

Feb 10

1.5598***

0.9951

0.7792***

0.9726

0.397***

0.9572

(0.0547)

(0.0654)

(0.0420)

Feb 15

1.5607***

0.9952

0.6112***

0.9679

0.2714***

0.9560

(0.0544)

(0.0556)

(0.0291)

Feb 20

1.5607***

0.9952

0.5852***

0.9680

0.2538***

0.9571

(0.0544)

(0.0532)

(0.0269)

Feb 25

1.5607***

0.9952

0.5747***

0.9676

0.2472***

0.9569

(0.0544)

(0.0526)

(0.0262)

Feb 29

1.5607***

0.9952

0.5658***

0.9678

0.2414***

0.9574

(0.0544)

(0.0516)

(0.0255)

  1. Note: The number of data points in each plot is 6, so the degree of freedom is 4. The parameter standard errors are quoted in parenthesis. *** significant at 1%; ** significant at 5%. Based on the significance level at 5%, fractal dimension plus or minus twice the standard error yields the margin of error, i.e., the lower and upper limits of fractal dimension.
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