REAT (version 3.0.3)

litzenberger2: Litzenberger-Sternberg Cluster Index

Description

Calculating the Cluster Index by Litzenberger and Sternberg for a given number of \(I\) industries and \(J\) regions

Usage

litzenberger2(e_ij, industry.id, region.id, a_j, p_j, b_ij, 
CI.output = "mat", na.rm = TRUE)

Arguments

e_ij

a vector with the employment of industry \(i\) in region \(j\)

industry.id

a vector containing the IDs of the industries \(i\)

region.id

a vector containing the IDs of the regions \(j\)

a_j

a vector containing the areas of the regions \(j\)

p_j

a vector containing the populations of the regions \(j\)

b_ij

a vector containing the numbers of firms of industry \(i\) in region \(j\)

CI.output

Type of output: matrix (default: CI.output = "mat") or data frame (CI.output = "df")

na.rm

logical argument that indicates whether NA values should be excluded before computing results

Value

A matrix or data frame containing \(IxJ\) values of \(CI\)

Details

The Litzenberger-Sternberg Cluster Index is not standardized and depends on the number of regarded industries and regions.

References

Farhauer, O./Kroell, A. (2014): “Standorttheorien: Regional- und Stadtoekonomik in Theorie und Praxis”. Wiesbaden : Springer.

Hoffmann J./Hirsch, S./Simons, J. (2017): “Identification of spatial agglomerations in the German food processing industry”. In: Papers in Regional Science, 96, 1, p. 139-162.

Litzenberger, T./Sternberg, R. (2006): “Der Clusterindex - eine Methodik zur Identifizierung regionaler Cluster am Beispiel deutscher Industriebranchen”. In: Geographische Zeitschrift, 94, 2, p. 209-224.

See Also

litzenberger, gini.conc, gini.spec, locq, locq2, ellison.a, ellison.a2, ellison.c, ellison.c2

Examples

# NOT RUN {
data (G.regions.industries)

lss <- litzenberger2(G.regions.industries$emp_all, 
G.regions.industries$ind_code, G.regions.industries$region_code,
G.regions.industries$area_sqkm, G.regions.industries$pop,
G.regions.industries$firms, CI.output = "df")
# output as data frame

lss_sort <- lss[order(lss$CI, decreasing = TRUE),]
# Sort decreasing by size of CI

lss_sort[1:5,]
# }