norm_ubiquity: Compute a measure of complexity by normalizing ubiquity of...

In EconGeo: Computing Key Indicators of the Spatial Distribution of Economic Activities

Compute a measure of complexity by normalizing ubiquity of industries

Description

This function computes a measure of complexity by normalizing ubiquity of industries. We divide the share of the total count (employment, number of firms, number of patents, ...) in an industry by its share of 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

norm_ubiquity(mat)

Arguments

mat	An incidence matrix with regions in rows and industries in columns

Value

A numeric vector representing the measure of complexity obtained by normalizing the ubiquity of industries. Each value in the vector corresponds to the normalized complexity score of an industry.

Author(s)

Pierre-Alexandre Balland [e-mail]

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

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

## run the function
norm_ubiquity(mat)

EconGeo documentation built on July 9, 2023, 6:58 p.m.