inv.norm.ubiquity: Compute a measure of complexity from the inverse of the...

Description Usage Arguments Author(s) References See Also Examples

Description

This function computes a measure of complexity from the inverse of the normalized ubiquity of industries. We divide the logarithm of the total count (employment, number of firms, number of patents, ...) in an industry by its ubiquity. Ubiquity is given by the number of regions in which an industry can be found (location quotient > 1) from regions - industries (incidence) matrices

Usage

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Arguments

mat

An incidence matrix with regions in rows and industries in columns

Author(s)

Pierre-Alexandre Balland p.balland@uu.nl

References

Balland, P.A. and Rigby, D. (2017) The Geography of Complex Knowledge, Economic Geography 93 (1): 1-23.

See Also

diversity, location.quotient, ubiquity, TCI, MORt

Examples

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## generate a region - industry matrix with full count
set.seed(31)
mat <- matrix(sample(0:10,20,replace=T), ncol = 4)
rownames(mat) <- c ("R1", "R2", "R3", "R4", "R5")
colnames(mat) <- c ("I1", "I2", "I3", "I4")

## run the function
inv.norm.ubiquity (mat)

PABalland/EconGeo documentation built on Nov. 13, 2020, 2:50 a.m.